TEMPOCO, Jakarta - The Meteorology, Climatology and Geophysics Agency (BMKG) plans to set up automatic weather stations to provide weather information for the 18th Asian Games event. "To support the Asian Games, BMKG will provide weather information in 16 sports venues in Jakarta and Palembang," BMKG Chief Dwikorita Karnawati said in Jakarta on Sunday.
AWS STASIUN KLIMATOLOGI PALEMBANG Automatic Weather Station AWS merupakan stasiun cuaca otomatis yang di desain untuk mengukur dan mencatat parameter-parameter meteorologi secara otomatis. AWS terdiri dari beberapa komponen yaitu sensor, data logger, sistem komunikasi, sistem catu daya, display, dan peralatan pendukung lainnya. Sensor yang digunakan pada aws yaitu Termometer berfungsi untuk mengukur suhu dan kelembaban udaraBarometer berfungsi untuk mengukur tekanan udaraAnemometer berfungsi untuk mengukur arah dan kecepatan anginPyranometer berfungsi untuk mengukur radiasi matahariRain Gauge berfungsi untuk mengukur curah hujan Sistem catu daya yang digunakan oleh AWS menggunakan solar panel yang akan menyerap energi matahari diubah menjadi energi listrik dan diteruskan ke baterai melalui regulator. Pada dasarnya prinsip kerja AWS yaitu sensor-sensor AWS akan mengukur parameter cuaca kemudian data yang didapat di proses melalui data logger selanjutnya data yang dihasilkan tersebut dikirim melalui modem dengan metode FTP / HTTP ke server BMKG Pusat dan secara simultan mengirimkan data ke Stasiun Klimatologi Palembang melalui jaringan kabel. Pengiriman data realtime ke server BMKG dilakukan setiap 10 menit. Data yang dikirimkan dapat di monitoring melalui Data yang tersimpan di data logger dapat dipanggil melalui data collect yang terhubung dengan komputer.
instrument(Automatic Weather Station/AWS) and air temperature measurements using manual instrument. The data that is used in this study are three-hourly data collected from February to June 2016 in 12 (twelve) synoptic stations of the Indonesian Agency for Meteorology Climatology and Geophysics (BMKG), which are Bengkulu, Dabo Singkep, Gunung
Abstract Weather observation has a very important role in human life, especially in the shipping world. BMKG Meteorological, Climatology, and Geophysics Agency, as a government agency, is carrying out these weather observations. However, weather observations conducted at BMKG Maritime Perak Stationary still manual with the tendency of the human error. Therefore, it is necessary to develop an automatic, real-time and accurate weather observation device to assist BMKG Station. This device consists of SHT11 humidity sensor and MS5611 pressure sensor which is connected to the NodeMCU microcontroller as an internet module using the Arduino IDE platform. Measurement parameters are wet ball temperature, dry ball temperature, dew point, humidity, absolute pressure QFE, and atmospheric pressure QNH. The six parameters are compared to conventional devices at BMKG Station. From the data analysis, it was found that the error values of these parameters were dry ball temperature, wet ball temperature, dew point, humidity, QFE, and QNH are %, %, %, %, %, % respectively. The researchers have developed automatic, digital and real-time weather observation devices and low-cost products.
BMKGwas invited to The 1st International Panel for Director General of Meteorological Services of Islamic Countries. Provision of Critical Weather Information Services to Prevent and Curb Drought-Induced Forest Fire. BMKG Wins the Best Booth Award in Disaster Risk Reduction (DRR) Week 2017.
Automatic Weather Stations in an Edged Internet of Things IoT system publicationAutomatic Weather Stations AWS are extensively used for gathering meteorological and climatic data. The World Meteorological Organization WMO provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Things, there is an ever-increasing necessity for the...Context 1... on the literature [31] and our perspective, an Edged IoT system architecture has three 3 main layers, as illustrated in Figure 2, which are as follows Connected End devices at the edge of a network with embedded processing power, primitive intelligence, network connectivity, and sensing capabilities. ...... The technological advancement in weather predictions has shown the need for accurate measurement of atmospheric parameters which is of utmost importance to meteorologists. Of recent, there is a need for observing atmospheric weather parameters that will make scientist have access to real-time data they need to forecast or predict atmospheric weather conditions [2]. The need for accurate meteorological forecasts is on the rise because some sectors are in need of accurate weather prediction like the agriculture sector, maritime sector, and aviation sector. ...An atmospheric data acquisition device is designed to ease and improve on the current method of acquiring Temperature, Pressure, and Relative Humidity measurement at different altitudes. The proposed work aims to solve the problem of inadequate atmospheric data by monitoring atmospheric weather conditions using sensors while the microcontroller processes the data collected and relays it to the user. This research was carried out at the University of Uyo, between September 2018 and January, 2023. Considering that weather forecasting is of the utmost importance in our current society, the system has been built using a BME280 module for the atmospheric parameters acquisition, an ESP8266 as the microcontroller for Data processing, and a wireless module for processing and transfer of the data from the BME module, a NEO6M GPS module for longitude and latitude, a Li-ion cell to power the components and a TP4056 circuit to recharge the Li-ion cell. A web application was incorporated to help the user interact and access the data to enable ease of understanding and real-time logging of the data collected. This work is targeted toward the weather forecasting sector, agricultural sector, and individuals which may wish to gather information about the atmosphere for knowledge consumption. The results show that this device has a good performance for capturing atmospheric parameters for real-time monitoring purposes.... The use of current technologies is also included. Finally, the study presents a case study developed in AWS AgroComp project and its results [9]. The work of Kulkarni et al. is about the nature of a weather display method using low-cost components that even any electronics enthusiast could design. ...Industrialization and rapid urbanization in almost every country adversely affect many of our environmental values, such as our core ecosystem, regional climate differences and global diversity. The difficulties we encounter as a result of the rapid change we experience cause us to encounter many problems in our daily lives. The background of these problems is rapid digitalization and the lack of sufficient infrastructure to process and analyze very large volumes of data. Inaccurate, incomplete or irrelevant data produced in the IoT detection layer causes weather forecast reports to drift away from the concepts of accuracy and reliability, and as a result, activities based on weather forecasting are disrupted. A sophisticated and difficult talent, weather forecasting needs the observation and processing of enormous volumes of data. In addition, rapid urbanization, abrupt climate changes and mass digitization make it more difficult for the forecasts to be accurate and reliable. Increasing data density and rapid urbanization and digitalization make it difficult for the forecasts to be accurate and reliable. This situation prevents people from taking precautions against bad weather conditions in cities and rural areas and turns into a vital problem. In this study, an intelligent anomaly detection approach is presented to minimize the weather forecasting problems that arise as a result of rapid urbanization and mass digitalization. The proposed solutions cover data processing at the edge of the IoT and include filtering out the missing, unnecessary or anomaly data that prevent the predictions from being more accurate and reliable from the data obtained through the sensors. Anomaly detection metrics of five different machine learning ML algorithms, including support vector classifier SVC, Adaboost, logistic regression LR, naive Bayes NB and random forest RF, were also compared in the study. These algorithms were used to create a data stream using the time, temperature, pressure, humidity and other sensor-generated information.... The results are then compared to land use data from the CORINE land cover inventory. The methodology provided promising results, which can be further improved by applying machine learning methods such as artificial neural networks, random forests and expert systems [12,18,19], and the results can be used for the application of forest policy as well as decision making [20][21][22][23][24]. ... Konstantinos IoannouThe detection of possible areas for the application of agroforestry is essential and involves the usage of various technics. The recognition of forest types using satellite or aerial imagery is the first step toward this goal. This is a tedious task involving the application of remote sensing techniques and a variety of computer software. The overall performance of this approach is very good and the resulting land use maps can be considered of high accuracy. However, there is also the need for performing high-speed characterization using techniques that can determine forest types automatically and produce quick and acceptable results without the need for specific software. This paper presents a comprehensive methodology that uses Normalized Difference Vegetation Index NDVI data derived from the Moderate Resolution Imaging Spectroradiometer instrument MODIS aboard the TERRA satellite. The software developed automatically downloads data using Google Earth Engine and processes them using Google Colab, which are both free-access platforms. The results from the analysis were exported to ArcGIS for evaluation and comparison against the CORINE land cover inventory using the latest update 2018.... The last prediction results are compared with other models and found that this gives little more accuracy than the others. In the paper [12], the Authors reviewed the technology used for the implementation of automated weather stations is being made. In addition, the Authors also introduced the advanced computing such as IoT, Edge Computing, In-Depth Learning, Low Power WAN LPWAN, etc. using upcoming AWS-based viewing systems. ...... For the measurements of ambient temperature and humidity, a DHT22 sensor AM2302 Waveshare, Waveshare Electronics, Shenzhen, China is used, which is interfaced with the Raspberry Pi. DHT22 is a commonly used sensor in the prototyping phase of IoT Internet of Things system developments [29], which is capable of performing periodic measurements every around two seconds, which is adequate for the task. ...The emerging use of low-temperature plasma in medicine, especially in wound treatment, calls for a better way of documenting the treatment parameters. This paper describes the development of a mobile sensory device referred to as MSD that can be used during the treatment to ease the documentation of important parameters in a streamlined process. These parameters include the patient’s general information, plasma source device used in the treatment, plasma treatment time, ambient humidity and temperature. MSD was developed as a standalone Raspberry Pi-based version and attachable module version for laptops and tablets. Both versions feature a user-friendly GUI, temperature–humidity sensor, microphone, treatment report generation and export. For the logging of plasma treatment time, a sound-based plasma detection system was developed, initially for three medically certified plasma source devices kINPen MED, plasma care, and PlasmaDerm Flex. Experimental validation of the developed detection system shows accurate and reliable detection is achievable at 5 cm measurement distance in quiet and noisy environments for all devices. All in all, the developed tool is a first step to a more automated, integrated, and streamlined approach of plasma treatment documentation that can help prevent user variability.... In the same way, in the article [11] the author comments that, in Greece, there is a growing need for automated observation systems that provide scientists with the realtime data necessary to design and implement environmental policies. Therefore, this article reviews the technologies most currently used to implement weather stations, where they use the Internet of Things, Edge Computing, Deep Learning, LPWAN and more. ...Jeffry Ricaldi Cerdan Laberiano Andrade ArenasCurrently, pollution and global warming is a very controversial problem, due to the various consequences and effects it generates on health and the environment. There are studies that highlight that the main pollutants are due to human action that generates the extermination of certain ecosystems, as well as the increase in various acute and chronic diseases. The ecosystems most neglected by the authorities and the citizens are the wetlands, which can be seen reflected in the wetlands of Ventanilla, whose surface has been reduced from 1,500 to hectares due to overpopulation and contamination by the citizens themselves in recent years. These accessions endanger the extermination of the habitat of 126 birds and 27 species of native plants that inhabit a certain place, which is of great concern because these ecosystems are rapidly degrading. That is why, in the face of this problem, the design of a weather station applying the internet of things is proposed, which aims to inform the caretakers of the current state of the wetlands through a web server, where it will serve to carry out preventive actions. regarding the care of a certain ecosystem that are essential for the stabilization of CO2 emissions. This system is made up of the ESP32 platform, which will activate the emergency lights and a siren when the DHT 22, BMP180, ML8511 and MQ135 sensors detect abnormal values in temperature, humidity, atmospheric pressure, altitude, UV radiation and toxic gases.... In some cases, the low sensor cost criterion is formulated implicitly, as a low-cost assumption of the entire system Madokoro et al. [7], Shahadat et al. [19], Singh et al. [21], or as a remark that the lower cost of weather sensors directly translates into a larger number of weather stations Adityawarman and Matondang [2]. In many cases, the application of the low-cost criterion can be expected, based on the types of sensors used Mestre et al. [25], Chiba et al. [10], Nomura et al. [11], Hill et al. [13], Almalki et al. [9], Kim et al. [26], Kuo et al. [27], and Ioannou et al. [28]. ...... Low-Cost Sensors [25] stationary high resolution, stability over different weather conditions [7] mobile analysis based on literature review [19] stationary not specified [2] stationary not specified [21] stationary analysis based on literature review [22] stationary low cost [8] mobile weight, size, range, resolution, cost [9] both 1 reliability in high temperatures, energy efficiency [23] stationary low cost [20] portable not specified [26] stationary analysis based on literature review [27] stationary analysis based on literature review [28] stationary analysis based on literature review [24] stationary low cost this paper both 1 response time of a sensor in the cyber-physical subsystem, two defined factors of information accuracy 1 mobile, stationary. ...... However, modern low-cost sensors, calibrated by the manufacturers, are believed to be able to ensure getting measured data at good or, at least, sufficient accuracy. This means that in current weather stations, low-cost sensors are willingly used [2,[7][8][9][18][19][20][21][22][23][24][25][26][27][28], and was the premise for formulating the two minor criteria of sensor selection. ...Agnieszka ChodorekRobert Ryszard Chodorek Paweł SitekSmart-city management systems use information about the environment, including the current values of weather factors. The specificity of the urban sites requires a high density of weather measurement points, which forces the use of low-cost sensors. A typical problem of devices using low-cost sensors is the lack of legalization of the sensors and the resulting inaccuracy and uncertainty of measurement, which one can attempt to solve by additional sensor calibration. In this paper, we propose a different approach to this problem, the two-stage selection of sensors, carried out on the basis of both the literature pre-selection and experiments actual selection. We formulated the criteria of the sensor selection for the needs of the sources of weather information the major one, which is the fast response time of a sensor in a cyber-physical subsystem and two minor ones, which are based on the intrinsic information quality dimensions related to measurement information. These criteria were tested by using a set of twelve weather sensors from different manufacturers. Results show that the two-stage sensor selection allows us to choose the least energy consuming due to the major criterion and the most accurate due to the minor criteria set of weather sensors, and is able to replace some methods of sensor selection reported in the literature. The proposed method is, however, more versatile and can be used to select any sensors with a response time comparable to electric ones, and for the application of low-cost sensors that are not related to weather stations.... Stasiun pengamatan cuaca berfungsi pemantauan dan pengamatan cuaca dan perubahan kejadian alam berdasarkan pembacaan sensor terhadap kondisi suhu, temperatur, udara dan kelembapan suatu daerah pada kurun waktu tertentu [7]. Badan Meteorologi Klimatologi dan Geofisika BMKG sebagai institusi yang melakukan tugas pemantauan cuaca telah memiliki jaringan pengamatan cuaca secara otomatis atau Automatic Weather Station AWS yang tersebar di seluruh wilayah Indonesia [8]. ...... Stasiun pengamatan cuaca otomatis lebih dikenal dengan istilah AWS demikian juga dengan istilah di dokumen Guide to Meteorological Instruments and Methods of Observation Nomor 8 dari World Meteorological Organisation WMO [7], [9], [10], namun untuk membedakan kategori stasiun pengamatan tersebut BMKG membagi 3 tiga tipe stasiun. Stasiun pengamatan cuaca terdiri dari Automatic Rain Gauge ARG, Agroclimat Automatic Weather Station AAWS dan Automatic Weather Station AWS, dimana ketiganya hanya dibedakan dalam parameter cuaca yang diamati dan jumlah sensor yang dipasang. ...Stasiun Pengamatan Cuaca pada Badan Meteorologi Klimatologi dan Geofisika BMKG telah merapatkan jaringan stasiun pengamatan cuaca guna menghasilkan akurasi data yang lebih baik. BMKG memiliki kurang lebih 1000 dan jumlah ini masih jauh dari ideal untuk kerapatan jaringan pengamatan cuaca se-Indonesia. Stasiun pengamatan cuaca yang terbagi dalam 3 tiga type yaitu Automatic Rain Gauge ARG, Automatic Weather Station AWS dan Agroclimate Automatic Weather Station AAWS. Pemuktahiran sistem pengiriman data dari stasiun pengamat cuaca terhadap protokol pengiriman File Transfer Protocol FTP melalui modem General Packet Radio Service GPRS setiap 10 menit, dengan upgrade teknologi Internet of Things IoT perlu peninjauan terhadap kinerja operasional sistem komunikasi data. Karakteristik data yang kecil sangat cocok pada teknologi Internet of Things dengan menggunakan protokol Message Queuing Telemetry Transport MQTT guna monitoring data-data cuaca secara real-time. Berdasarkan hasil kajian dan penelitian dengan pengujian yang dilakukan terhadap metode komunikasi protokol FTP dengan protokol IoT MQTT pada stasiun AWS menggunakan analisa dengan metode PIECES Performance, Information, Economic, Control, Efisiency dan Service menunjukkan protokol MQTT yang berbasis IoT sebagai konsep komunikasi data yang tepat dimasa depan mengantikan protokol FTP... Assim, tecnologias digitais são alternativas ou complementos para análises tradicionais. Em IoT e Deep Learning há monitoramento de condições climáticas em estações meteorológicas automáticas, em tempo real, de baixo custo, com avançada transmissão de dados [15]. Uso de IoT associada a redes neurais para analisar fatores e emissões de gases poluentes dióxido e monóxido de carbono e dióxido de enxofre, a fim de reduzir efeito estufa [16]. ...... This study uses MAE, RMSE, and RMSLE to compare the performance of different models. Most studies use the above three indicators a lot for data comparison [38][39][40]. They are widely used to objectively assess the accuracy of a regression equation by analyzing differences between observations and estimates. ...Kyung-Su ChuCheong-Hyeon OhJung-Ryel ChoiByung-Sik KimIn recent years, Korea has seen abnormal changes in precipitation and temperature driven by climate change. These changes highlight the increased risks of climate disasters and rainfall damage. Even with weather forecasts providing quantitative rainfall estimates, it is still difficult to estimate the damage caused by rainfall. Damaged by rainfalls differently for inch watershed, but there is a limit to the analysis coherent to the characteristic factors of the inch watershed. It is time-consuming to analyze rainfall and runoff using hydrological models every time it rains. Therefore, in fact, many analyses rely on simple rainfall data, and in coastal basins, hydrological analysis and physical model analysis are often difficult. To address the issue in this study, watershed characteristic factors such as drainage area A, mean drainage elevation H, mean drainage slope S, drainage density D, runoff curve number CN, watershed parameter Lp, and form factor Rs etc. and hydrologic factors were collected and calculated as independent variables, and the threshold rainfall calculated by the Ministry of Land, Infrastructure and Transport MOLIT was calculated as a dependent variable and used in the machine learning technique. As for machine learning techniques, this study uses the support vector machine method SVM, the random forest method, and eXtreme Gradient Boosting XGBoost. As a result, XGBoost showed good results in performance evaluation with RMSE 20, MAE 14, and RMSLE and the threshold rainfall of the ungauged watersheds was calculated using the XGBoost technique and verified through past rainfall events and damage cases. As a result of the verification, it was confirmed that there were cases of damage in the basin where the threshold rainfall was low. If the application results of this study are used, it is judged that it is possible to accurately predict flooding-induced rainfall by calculating the threshold rainfall in the ungauged watersheds where rainfall-outflow analysis is difficult, and through this result, it is possible to prepare for areas vulnerable to flooding.
AUTOMATICWEATHER STATION (AWS) BERBASIS MIKROKONTROLER TESIS Diajukan sebagai salah satu syarat untuk memperoleh gelar Magister Sains KANTON LUMBAN TORUAN (BMKG) as an observer of the weather. Key words: Sensor, microcontroller, Automatic Weather Station, portable, data 1 Automatic weather, Kanton Lumban Toruan, FMIPA UI, 2009.
NASA/ADS Abstract To improve the quality and quantity of meteorological data over Indonesia, Meteorology Climatology and Geophysics Agency of Indonesia BMKG is continuously developing automatic weather observations. BMKG has 63 units Automatic Weather Station AWS and 165 units Automatic Weather Observation System AWOS both inside and outside the BMKG Station environment. To make the control of sensor conditions easier, especially for temperature, pressure, relative humidity, and rainfall sensors, an additional system is needed to monitor and warn when problems occur with these sensors. The correlation among weather parameters data is the key to monitoring the sensor condition, these data are going to be trained and tested with the Artificial neural network ANN method. Then, the sensor condition normal or error indicated can be well detected based on AWS’s data. The quality improvement of automatic weather station data is expected to increase the utilization of the data. Publication Journal of Physics Conference Series Pub Date February 2021 DOI Bibcode 2021JPhCS1816a2056W
jasasewa alat meteorologi/ klimatologi - portable marine automatic weather station (pmaws)
To improve the quality and quantity of meteorological data over Indonesia, Meteorology Climatology and Geophysics Agency of Indonesia BMKG is continuously developing automatic weather observations. BMKG has 63 units Automatic Weather Station AWS and 165 units Automatic Weather Observation System AWOS both inside and outside the BMKG Station environment. To make the control of sensor conditions easier, especially for temperature, pressure, relative humidity, and rainfall sensors, an additional system is needed to monitor and warn when problems occur with these sensors. The correlation among weather parameters data is the key to monitoring the sensor condition, these data are going to be trained and tested with the Artificial neural network ANN method. Then, the sensor condition normal or error indicated can be well detected based on AWS’s data. The quality improvement of automatic weather station data is expected to increase the utilization of the may be subject to copyright. Discover the world's research25+ million members160+ million publication billion citationsJoin for free Journal of Physics Conference SeriesPAPER • OPEN ACCESSTemperature, pressure, relative humidity and rainfall sensors early errordetection system for automatic weather station AWS with artificialneural network ANN backpropagationTo cite this article P Wellyantama and S Soekirno 2021 J. Phys. Conf. Ser. 1816 012056View the article online for updates and content was downloaded from IP address on 09/03/2021 at 0630 Content from this work may be used under the terms of the Creative Commons Attribution licence. Any further distributionof this work must maintain attribution to the authors and the title of the work, journal citation and under licence by IOP Publishing LtdThe 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi pressure, relative humidity and rainfall sensors early error detection system for automatic weather station AWS with artificial neural network ANN backpropagation P Wellyantama1 and S Soekirno1 1Physics Department, University of Indonesia, Depok, West Java, Indonesia E-mail pradawellyantama Abstract. To improve the quality and quantity of meteorological data over Indonesia, Meteorology Climatology and Geophysics Agency of Indonesia BMKG is continuously developing automatic weather observations. BMKG has 63 units Automatic Weather Station AWS and 165 units Automatic Weather Observation System AWOS both inside and outside the BMKG Station environment. To make the control of sensor conditions easier, especially for temperature, pressure, relative humidity, and rainfall sensors, an additional system is needed to monitor and warn when problems occur with these sensors. The correlation among weather parameters data is the key to monitoring the sensor condition, these data are going to be trained and tested with the Artificial neural network ANN method. Then, the sensor condition normal or error indicated can be well detected based on AWS’s data. The quality improvement of automatic weather station data is expected to increase the utilization of the data. 1. Introduction Indonesia is a very large archipelago country with an area of about km2, Indonesia has 17,508 islands and a long coastline of about 81,000 km [1]. In Indonesia, weather information has an important role both, to plan and to operate daily life in various sectors. From the construction development, economy, social, transportation, tourism, health, etc. In the construction development sector for buildings, airports and ports require information about wind direction, wind speed, and tides, in the economic sector, the analysis of inflation in a region requires information on wave height, the tourism sector requires weather forecast data, temperature, humidity, wave height, and the land, sea, and air transportation sector requires weather information data, air pressure, wave height, and significant weather maps. The Meteorology Climatology and Geophysics Agency BMKG has 183 Meteorological Stations that observe and provide weather information spread across Indonesia. Weather observations are carried out manually or by using human power to observe weather parameters using conventional weather instruments and there are also automatic observations using digital weather instruments. Of the 183 meteorological stations, 62 use fully automatic observation, and the rest use a conventional instrument. BMKG has 63 units meteorological AWS automatic weather station and 165 units AWOS automatic weather observation system spread throughout Indonesia, both inside and outside of the Meteorological Station zone. Some digital instruments usually unnoticed if there is a problem with the values generated by the sensor, if they are not compared to other instruments or if there is no event that validates the value. This makes the The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi process plays an important role to maintain data quality. BMKG always calibrates the equipment every 6 months, but between the 6 months it does not rule out the possibility of potential problems in measuring values, especially for electronic or digital equipment. The eligibility conditions for meteorological instruments adhere to the regulations of the World Meteorological Organization WMO CIMO of 2014, where the measurement tolerances are 1 temperature maximum of 2 humidity maximum 3%, 3 air pressure maximum of hPa, 3 maximum wind speed of m/s, 4 wind direction maximum 5o, 5 rainfall maximum 5%, 6 sun radiation maximum 5%. To make the control of sensor conditions easier, especially temperature, pressure, humidity, and rainfall sensors, we need a system that can monitor and detect when problems occur with these sensors. The correlation among weather parameters is the key to controlling the sensor conditions to be trained and tested using the ANN backpropagation method. This ANN system design works by learning the correlation and pattern of each sensor data during the training phase. In the testing phase, the condition of the test data will be predicted. If any sensor outputs a value that is unusual or different from the pattern studied by ANN, the system will give a warning indicating sensor failure. With better quality weather observation data, it will improve the quality of providing weather information, so that the use of weather information becomes more accurate and useful. In a study [2] entitled Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data, and research [3] entitled Temperature error correction based on BP neural network in meteorological wireless sensor network, they tried to approach a calibration using software and models, but only limited to the temperature sensor. In this study, we try to do the same approach, but for more sensors. The next approach to sensor error detection is studied based on the correlation pattern among sensors, this was done in a study [4] entitled Soft Sensors for Instrument Fault Accommodation in Semiactive Motorcycle Suspension Systems. The detection of a condition in classification has been carried out in a research conducted by [5] entitled Intelligent Multi-Sensor Control Device for Recognition of Gas-Air Mixture Samples with the Use of Artificial Neural Networks, which classifies and detection odors with electronic noses using ANN. From the researches above, the ANN model has good results, so this paper will try to apply the ANN-BP method for an early detection approach for error indication of more than one sensor on AWS in a result that is classified as error or normal. 2. Method Figure1. Schematic of the AWS sensor condition early detection system. The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi design of the early detection system begins with the design of the ANN backpropagation model, the model is built with pattern recognition in training data on observations of weather parameters, temperature, humidity, pressure, and rain at Tanjung Priok Maritime Station for 4 years, from 2017 until 2020. The data training is carried out using Rstudio software. The composition of data for training is 80% data. The training is carried out so that the network can recognize the patterns generated from the input and output pairs. The data input consists of weather parameters, temperature, humidity, pressure, and rain, and the output is a label of the sensor's condition, normal or an error indication. After the model produces the best accuracy in training and testing data, then the ANN model is used to estimate and to detect the condition of the AWS sensors, especially pressure, temperature, humidity, and rain sensors. The details of the research steps are Preprocessing data Before the data was processed using ANN, the data were compiled and conditioned, with a composition of ± 50% actual data and ± 50% in the form of synthetic data. The synthetic data mean the actual data that has been added and subtracted in value according to WMO CIMO regulation 2014 to obtain data in the form of damaged sensor label values. Figure 2. AWS Tanjung Priok. ANN Design ANN design is done by determining the amount of input data used in training, the number of hidden layers used and the number of outputs desired. The data used as input are temperature, humidity, pressure, and rain observation data at the Tanjung Priok Maritime Meteorological Station from 2017 to 2020, with details of the network architecture as follows Figure 3. ANN architecture of temperature and humidity. The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi 4. ANN architecture Air pressure. Figure 5. ANN rainfall architecture. Figure 6. Research algorithm. The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi Pattern Recognition training. In the training process, the maritime meteorological station’s conventional weather observation data for 4 years are arranged into 2 output conditions, namely 1 Output conditions "sensor in normal conditions", where all input values are the original values of weather observations for the past 4 years. 2 The output condition is "problematic sensor", where all input values are added and also subtracted from the value that exceeds the tolerance limits of the CIMO World Meteorological Organization WMO No. 8 of 2014, where the measurement tolerance is as follows a temperature maximum b Humidity maximum 3%, c Air pressure maximum of hPa , d Rainfall maximum 5%. The input and output data during the training are in the form of 1 Input temperature, humidity, and pressure data, the output temperature sensor label indication is damaged or normal, 2 Input temperature, humidity, and pressure data, the output humidity sensor label indicates damaged or normal . 3 Temperature, humidity, and pressure data input, the output pressure indication is damaged or normal. 4 Temperature, humidity, and rain data input, output rain label indication of damage or normal in all rain categories except 1-3mm rain which has additional pressure data input. Testing and estimation Data testing is carried out aimed to determine whether the network can recognize patterns of training data from the input data provided. If the resulting error value has reached the target, the resulting output can be used as estimation data. The model validation value is obtained from the accuracy coefficient with the following value interpretation Table 1. The relation between accuracy coefficient and interpretation [6] - 20 % - % - % - % - 100 % Very low Low Moderate High Very high The estimation is done after the pattern recognition process is carried out by the network when the training is complete and the model has been tested with good accuracy values. Input data consist of AWS Tanjung Priok’s temperature, humidity, pressure, and rain data and the output is a classification of sensor conditions a Normal, or b The temperature sensor is indicated as damaged, or c The humidity sensor is indicated as damaged, or d The pressure sensor is indicated as damaged, or e The rain sensor is indicated to be damaged. 3. Result and Discussion Test result Temperature Sensor. After the data training was carried out, then testing was carried out with the remaining 20% of the data, with the target data being the previously known sensor conditions. In the testing temperature sensor conditions, obtained a very high accuracy value is 99%, false negative prediction is “normal”, which it should “error indication” value is and false positive prediction is “error indication”, which it should “normal” value is with the graph of the independent variable contribution as follows The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi 7. Contribution of the independent variable, temperature sensor label output. The figure above shows the intensity of the contribution of the independent variable in training and testing for the output temperature sensor condition label, where the highest contribution is the value of the temperature sensor itself. Humidity Sensors. After the data training was carried out, testing was carried out with the remaining 20% of the data, with the target data being in the form of previously known conditions. In testing the temperature sensor conditions, a very high accuracy value was obtained false negative prediction is “normal”, which it should “error indication” value is and false positive prediction is “error indication”, which it should “normal” of with a graph of the independent variable contribution as follows Figure 8. Contribution of the independent variable, humidity sensor label output. The figure above shows the intensity of the contribution of the independent variable in training and testing for the output humidity sensor condition label, where the highest contribution is the value of the humidity sensor itself. Pressure Sensor. After the data training was carried out, testing was carried out with the remaining 20% of the data, with the target data being in the form of previously known conditions. In testing the temperature sensor conditions, obtained a very high accuracy value of 100%, false negative prediction is “normal”, which it should “error indication” value is 0% and false positive prediction is “error The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi which it should “normal” value is 0%, with a contribution graph independent variable as follows Figure 9. Contribution of the independent variable, pressure sensor label output. The figure above shows the intensity of the contribution of the independent variable in training and testing for the output Pressure sensor condition label, where the highest contribution is the value of the Pressure sensor itself. Rain Sensor. After the data training was carried out, testing was carried out with the remaining 20% of the data, with the target data being in the form of previously known conditions. In testing the temperature sensor conditions, obtained a very high accuracy value on average of 82%, an average false negative prediction is “normal”, which it should “error indication” value is and false positive prediction is “error indication”, which it should “normal” value is with details a Rainfall 1-3 mm, the test accuracy is 77%, false-negative and false-positive b Rainfall 3-20 mm testing accuracy is 82%, false-negative 0%, and false-positive c Rainfall 20-50 mm has 82% accuracy testing, 0% false-negative and false-positive. d Rainfall above 50 mm has 91% accuracy testing, false-negative 0%, and false-positive With the graph of the independent variable contribution as follows Figure 10. Contribution of the independent variable, 1-3mm, and 3-20mm rain sensor label output. The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi 11. Contribution of the independent variable, rain sensor label output 20-50mm and> 50mm. The Figure above shows the intensity of the contribution of the independent variable in training and testing for the output rain sensor condition label, where the highest contribution is the value of the rain sensor itself. Estimation Results After the training and data testing process, based on the high accuracy results above, the sensor condition estimation process is carried out. The data to be estimated is the latest AWS Tanjung Priok data on October 16 - 18, 2020 with the following results Table 2. The estimation results of the AWS Tanjung Priok sensor condition label. Estimated of sensor condition labels Error Indication for Pressure sensor Error Indication for Pressure sensor The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi on the model obtained from training and tested with previous data, and used to estimate the AWS Tanjung Priok sensor data for 16-18 October 2020, it was found that almost all were in normal condition, 2 conditions indicated that the pressure sensor had an error, on October 17 at and UTC, which can be seen at those 2 times the pressure value suddenly decreased significantly, but other weather parameters were still in conditions not much different from the previous time. 4. Conclusion The sensor condition, especially temperature, humidity, pressure, and rain on AWS Tanjung Priok can be estimated using the ANN backpropagation method, where the accuracy results between the model output and the target during training and testing show very high values. Based on this model, the estimation results of the AWS Tanjung Priok sensor conditions on 16-18 October 2020 are almost all in normal conditions, 2 conditions indicated that the pressure sensor had an error, on October 17 at and UTC, this can be seen at the 2 times the pressure value decreased significantly, but other weather parameters are still not much different from the previous time. Based on the results of this estimation, it is hoped that it can serve as a warning to the nearest Maritime Meteorological Station so that checks can be carried out as soon as possible and if damage occurs, replacement or repair of sensor hardware can be carried out so that the quality of AWS data can always be maintained. Acknowledgment This research was supported by the grant of PITTA Publikasi Internasional Terindeks Untuk Tugas Akhir Mahasiswa of Universitas Indonesia under the contract number NKB-1005/ We would like to acknowledge the Indonesian Agency for Meteorology Climatology and Geophysics for supporting data and facilities. References [1] Dahuri R 2004 Pengelolaan Sumber Daya Wilayah Pesisir dan Lautan Secara Terpadu, Edisi Revisi Jakarta Pradnya Paramita [2] Yamamoto K, Togami T, Yamaguchi N, Ninomiya S 2017 Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data. Sensors 176 1290 [3] Wang B 2017 Temperature error correction based on BP neural network in meteorological wireless sensor network. Int. J. Sensor Networks 234 [4] Capriglione D, et al 2020 Soft Sensors for Instrument Fault Accommodation in Semiactive Motorcycle Suspension Systems IEEE transactions on instrumentation and measurement 69 5 [5] Kulagin V P, et al 2017 Intelligent Multi-Sensor Control Device for Recognition of Gas-Air Mixture Samples with the Use of Artificial Neural Networks IEEE [6] Sugiyono 2008 Metode Penelitian Kunatitatif Kualitatif dan R&D Bandung Alfabeta ... Artificial Neural Networks ANNs are frequently used in meteorology science CIE and cloud classification [40,41], solar irradiance and wind speed forecasting [42][43][44][45][46][47], atmospheric pollution distribution [48,49], and rainfall [50,51]. ANN classification models serve to classify input information into certain categories or targets. ...Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image-processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty-two image-processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scanner data. Color spaces, spectral features, and texture filters image-processing methods are applied. While the use of the traditional RGB color space for image-processing yielded good results ANN accuracy equal to other color spaces, such as Hue Saturation Value HSV, which may be more appropriate, increased the accuracy of their global classifications. The use of either the green or the blue monochromatic channels improved sky classification, both for the fifteen CIE standard sky types and for simpler classification into clear, partial, and overcast conditions. The main conclusion was that specific image-processing methods could improve ANN-algorithm accuracy, depending on the image information required for the classification WangZhi DengKe XuTao LiuIn recent years, meteorological environment has become a topic of concern to people. Various meteorological disasters threaten human life and production. Accurate and timely acquisition of meteorological data has become a prerequisite for dealing with various aspects of production and life, and also laid a foundation for weather prediction. For a long time, meteorological data acquisition system combined with modern information technology has gradually become a hot spot in the field of meteorological monitoring and computer research. The continuous development of NB-IoT technology has brought new elements to the research of meteorological monitoring system. This paper designs a weather station system based on NB-IoT, including data acquisition module, main controller module, NB-IoT wireless communication module, energy capture module, low power consumption scheme, measurement of air temperature is strongly influenced by environmental factors such as solar radiation, humidity, wind speed and rainfall. This is problematic in low-cost air temperature sensors, which lack a radiation shield or a forced aspiration system, exposing them to direct sunlight and condensation. In this study, we developed a machine learning-based calibration method for air temperature measurement by a low-cost sensor. An artificial neural network ANN was used to balance the effect of multiple environmental factors on the measurements. Data were collected over 305 days, at three different locations in Japan, and used to evaluate the performance of the approach. Data collected at the same location and at different locations were used for training and testing, and the former was also used for k-fold cross-validation, demonstrating an average improvement in mean absolute error MAE from to by applying our method. Some calibration failures were noted, due to abrupt changes in environmental conditions such as solar radiation or rainfall. The MAE was shown to decrease even when the data collected in different nearby locations were used for training and testing. However, the results also showed that negative effects arose when data obtained from widely-separated locations were used, because of the significant environmental differences between paper describes the development and experimental verification of an Instrument Fault Accommodation IFA scheme for front and rear suspension stroke sensors in motorcycles equipped with electronic controlled semi-active suspension systems. In particular, the IFA scheme is based on the use of Nonlinear Auto-Regressive with eXogenous inputs NARX Neural Networks NN employed as soft sensors for feeding the suspension control strategy back with measurement even in presence of faults occurred on the sensors. Different NN architectures have been trained and tuned by considering real data acquired during several measurement campaigns. The performance has been compared with that of the well-known Half-Car Model HCM. Very satisfying results allow the Soft sensor to be really integrated into fault-tolerant control systems. In experimental road tests an implementation of the proposed IFA scheme on a low-cost microcontroller for automotive applications, showed to be in real-time. In the paper these experimental results are shown to prove the good performance of the IFA scheme in different motorcycle operating conditions. Baowei WangXiaodu GuLi MaShuangshuang YanUsing meteorological wireless sensor network WSN to monitor the air temperature AT can greatly reduce the costs of monitoring. And it has the characteristics of easy deployment and high mobility. But low cost sensor is easily affected by external environment, often leading to inaccurate measurements. Previous research has shown that there is a close relationship between AT and solar radiation SR. Therefore, We designed a back propagation BP neural network model using SR as the input parameter to establish the relationship between SR and AT error ATE with all the data in May. Then we used the trained BP model to correct the errors in other months. We evaluated the performance on the datasets in previous research and then compared the maximum absolute error, mean absolute error and standard deviation respectively. The experimental results show that our method achieves competitive performance. It proves that BP neural network is very suitable for solving this problem due to its powerful functions of non-linear fitting.
AWS(Automatic Weather Stations) merupakan suatu peralatan atau sistem terpadu yang di disain untuk pengumpulan data cuaca secara otomatis serta di proses agar pengamatan menjadi lebih mudah. AWS ini umumnya dilengkapi dengan sensor, RTU (Remote Terminal Unit), Komputer, unit LED Display dan bagian-bagian lainnya. AWS dipasang pada ketinggian
Weather is very critical for aviation. Especially regarding safety in air transportation. Badan Meteorologi, Klimatologi, dan Geofisika BMKG in its duties and functions provides aviation weather information, conducts the updated weather observation activities for the needs of takeoff and landing at airports. The World Meteorological Organization WMO has targeted automation with a target achievement in 2017. But currently in conducting the updated weather observations, BMKG still uses conventional weather observation systems even though at some airports Automated Weather Observing Systems AWOS have been installed. The automated weather observing system is still not fully implemented yet. This study aims to create a blueprint for the implementation of automatic weather observations for aviation services in BMKG. Guidelines for making blueprint use the Enterprise Architecture Planning EAP framework. EAP defines business and architectural needs related to data, applications, and technology needed to implement automation. The final results achieved are in the form of a blueprint for the implementation of automated weather observing system for aviation services in BMKG which can be a guide for BMKG in achieving the vision related to aviation weather services. Discover the world's research25+ million members160+ million publication billion citationsJoin for free Prosiding Seminar Nasional Teknologi Informasi dan Kedirgantaraan Transformasi Teknologi untuk Mendukung Ketahanan Nasional, Yogyakarta, 13 Desember 2018 SENATIK 2018, Vol. IV, ISBN 978-602-52742-0-6 DOI THE BLUEPRINT OF AWOS IMPLEMENTATION FOR AVIATION SERVICES AT BMKG Duati Wardani1, Selo Sulistyo2, I Wayan Mustika3 Departemen Teknik Elektro dan Teknologi Informasi, Universitas Gadjah Mada Jl. Grafika 2, Kampus UGM, Yogyakarta, 55281 Email Abstract Weather is very critical for aviation. Especially regarding safety in air transportation. Badan Meteorologi, Klimatologi, dan Geofisika BMKG in its duties and functions provides aviation weather information, conducts the latest weather observation activities for the needs of takeoff and landing at airports. The World Meteorological Organization WMO has targeted automation with a target achievement in 2017. But currently in conducting the latest weather observations, BMKG still uses conventional weather observation systems even though at some airports Automated Weather Observing Systems AWOS have been installed. The automated weather observing system is still not fully implemented yet. This study aims to create a blueprint for the implementation of automatic weather observations for aviation services within the BMKG. Guidelines for making blueprint use the Enterprise Architecture Planning EAP framework. EAP defines business and architectural needs related to data, applications, and technology needed to implement automation. The final results achieved are in the form of a blueprint for the implementation of automated weather observing system for aviation services within the BMKG which can be a guide for BMKG in achieving the vision related to aviation weather services. Keyword BMKG, EAP, AWOS, aviation weather services Abstrak Cuaca merupakan hal yang sangat penting bagi dunia penerbangan. Apalagi menyangkut keselamatan dalam transportasi udara. Badan Meteorologi, Klimatologi, dan Geofisika BMKG dalam tugas dan fungsinya memberikan informasi cuaca penerbangan, melakukan kegiatan pengamatan cuaca terkini untuk keperluan tinggal landas dan pendaratan di bandara. World Meteorological Organization WMO telah menargetkan otomatisasi dengan target capaian di tahun 2017. Namun saat ini dalam melakukan pengamatan cuaca terkini, BMKG masih menggunakan sistem pengamatan cuaca konvensional meskipun di beberapa bandara telah dipasang sistem pengamatan cuaca otomatis AWOS. Sistem pengamatan cuaca otommatis juga masih belum dilaksanakan dengan penuh. Penelitian ini bertujuan untuk membuat cetak biru blueprint implementasi pengamatan cuaca otomatis untuk pelayanan penerbangan di lingkungan BMKG. Panduan dalam pembuatan cetak biru menggunakan kerangka Enterprise Architecture Planning EAP. EAP mendefinisikan kebutuhan bisnis dan arsitektur terkait data, aplikasi, dan teknologi yang dibutuhkan untuk mengimplementasikan otomatisasi. Hasil akhir yang dicapai adalah berupa blueprint implementasi pengamatan cuaca otomatis untuk pelayanan penerbangan di lingkungan BMKG yang dapat menjadi panduan bagi BMKG dalam mencapai visi terkait pelayanan cuaca penerbangan. Kata Kunci BMKG, EAP, AWOS, pelayanan cuaca penerbangan SENATIK 2018, Vol. IV, ISBN 978-602-52742-0-6 SIP-158 1. Pendahuluan Perubahan cuaca sering berdampak pada kehidupan manusia, tak terkecuali dalam dunia penerbangan yang memegang prinsip menjaga keselamatan transportasi udara. Kecelakaan dalam penerbangan umumnya diakibatkan oleh 3 faktor utama yaitu faktor teknis, faktor kesalahan manusia human error, dan faktor cuaca [1]. Pelayanan informasi cuaca penerbangan yang cepat, tepat, akurat, dan terus menerus sangat diperlukan di setiap bandar udara, terutama di bandara yang memiliki frekuensi penerbangan yang padat dan sering mengalami perubahan cuaca yang cepat. Setiap pengguna informasi meteorologi untuk penerbangan wajib menggunakan informasi yang bersumber dari Unit Pelayanan Informasi Meteorologi [2]. Dalam melaksanakan tugas dan fungsinya, unit pelayanan informasi meteorologi/ stasiun meteorologi berkewajiban memenuhi kebutuhan end-user akan informasi cuaca terkini. Update perubahan keadaan cuaca signifikan juga harus dilaporkan guna menjaga keselamatan penerbangan. Dalam melaksanakan pelayanan informasi cuaca penerbangan Badan Meteorologi, Klimatologi, dan Geofisika BMKG memiliki target sasaran strategis yaitu pemerataan pemenuhan layanan informasi peringatan dini cuaca penerbangan yang memenuhi standar pelayanan minimal bidang meteorologi yaitu dengan akurasi 100% [3]. Pada kegiatan pengamatan cuaca, BMKG masih menggunakan dua model pengamatan, yaitu pengamatan konvensional dan pengamatan otomatis. World Meteorological Organization WMO menargetkan untuk otomatisasi dengan target capaian tahun 2017 [4]. Hal ini mendorong BMKG untuk melakukan percepatan otomatisasi. Dalam dunia penerbangan, ada tiga tahap utama dalam pelayanan, yaitu pre-flight service, in-flight service, dan post-flight service. Pre-flight service merupakan kegiatan penanganan penerbangan sebelum keberangkatan di bandara asal/ origin station. In-flight service adalah kegiatan pelayanan selama penerbangan. Post-flight service adalah kegiatan penanganan penerbangan setelah kedatangan di bandara tujuan/destination [5]. Informasi cuaca dari kegiatan pengamatan cuaca permukaan dibutuhkan terlebih pada saat pre-flight dan post-flight, selama in-flight penerbang menggunakan panduan weather forecast yang disajikan oleh forecaster dalam flight document. Pengamatan cuaca diperlukan untuk mengamati keadaan cuaca secara terus menerus dan berkesinambungan untuk mengetahui perubahan cuaca guna meminimalkan efek negatif dari perubahan yang ektrim [6]. Petugas yang melaksanakan pengamatan disebut pengamat observer. Parameter yang diukur dalam pengamatan cuaca permukaan antara lain angin, suhu, kelembaban, hujan, tekanan, penyinaran matahari, jarak pandang, dan awan. Pengamatan yang akurat terus menerus sangat bermanfaat bagi pengolahan data untuk prakiraan cuaca weather forecast dan menjadi bahan penelitian untuk fenomena perubahan iklim. Terdapat dua jenis sistem pengamatan cuaca, yaitu sistem pengamatan konvensional conventional observing system dan sistem pengamatan otomatis automated observing system. Sistem pengamatan konvensional terdiri dari pengamat dan beberapa intrumentasi pengukur cuaca manual yang diletakkan di suatu taman pengamatan observing park. Sedangkan sistem pengamatan cuaca otomatis mengunakan instrumentasi pengukur cuaca otomatis. Automated Weather Observing System AWOS adalah instrumentasi pengamatan cuaca otomatis yang ditempatkan di bandara untuk mendapatkan data unsur-unsur cuaca secara otomatis [7]. Parameter cuaca diukur oleh sensor-sensor yang terpasang pada AWOS. Sensor-sensor tersebut antara lain digunakan untuk mengukur arah dan kecepatan angin, tekanan, suhu, kelembaban, hujan, awan, dan jarak pandang. Masing-masing sensor mengukur parameter cuaca, mengirimkannya hasil pengukuran ke Data Collections Platform DCP kemudian akan diproses oleh Central Data Processor CDP yang akan menyimpan SIP- 159 THE BLUEPRINT… Duati Wardani dan menyajikan data pengamatan [8]. AWOS mengolah data menjadi informasi cuaca penerbangan dalam bentuk a. MetReport, yaitu informasi cuaca rutin hanya untuk bandara setempat, tidak disebarkan keluar bandara, dan dipergunakan untuk keperluan tinggal landas dan pendaratan. b. Special, yaitu informasi cuaca khusus terpilih hanya untuk bandara setempat, tidak disebarkan keluar bandara, dilaporkan setiap saat bila ada perubahan unsur cuaca signifikan/ bermakna. c. Metar yaitu nama sandi pelaporan cuaca rutin untuk penerbangan d. Speci, yaitu nama sandi pelaporan cuaca khusus terpilih untuk penerbangan. Prosedur pelayanan informasi cuaca menggunakan AWOS adalah pengamat melihat dan mengamati hasil unsur-unsur cuaca yang terekam dalam monitor AWOS kemudian melakukan validasi dengan membandingkan data hasil pengamatan dari AWOS dengan pengamatan konvensional [7]. Saat ini peralatan pengamatan cuaca otomatis belum terpasang di semua bandara. Pengamatan otomatis belum berjalan secara penuh dalam pelayanan cuaca penerbangan di BMKG. Untuk itu diperlukan suatu perencanaan enterprise yang mampu mendefinisikan kebutuhan bisnis dan arsitektur terkait data, aplikasi, dan teknologi yang dibutuhkan untuk mengimplementasikan otomatisasi dan modernisasi pelayanan pengamatan cuaca untuk penerbangan di lingkungan BMKG. Makalah ini mengusulkan sebuah blueprint implementasi pengamatan cuaca otomatis untuk pelayanan penerbangan di lingkungan BMKG yang dapat menjadi panduan bagi BMKG dalam mencapai visi terkait pelayanan cuaca penerbangan. 2. Metodologi Penelitian Metode penelitian ini menggunakan kerangka Enterprise Architecture Planning Steven Spewak untuk menggambarkan sistem pengamatan cuaca konvensional yang sedang berjalan dan sistem pengamatan cuaca otomatis untuk masa mendatang. Enterprise Architecture EA merupakan suatu representasi dari struktur dan perilaku proses bisnis suatu perusahaan yang menggambarkan sistem yang yang sedang berjalan dan sistem di masa depan. EA meliputi pemanfaatan teknologi informasi terkini, visi untuk pemanfaatan teknologi informasi masa depan, dan road map untuk evolusi teknologi informasi dari keadaan saat ini ke masa depan [9]. Beberapa model EA yang sering digunakan antara lain model Zachman Framework, Enterprise Architecture Planning EAP, Togaf Adm, dan lain sebagainya. EAP merupakan bagian dari zachman’s framework, yaitu lapis kedua paling atas dari matrik zahman dimana tahapannya ditunjukkan pada gambar 1. Gambar 1. Enterprise Architecture Planning Steven Spewak [10] SENATIK 2018, Vol. IV, ISBN 978-602-52742-0-6 SIP-160 EAP mendefinisikan kebutuhan bisnis dan arsitektur yang menjelaskan mengenai data, aplikasi, dan teknologi yang dibutuhkan untuk mendukung bisnis tersebut [11]. EAP terdiri dari empat tahapan, yaitu inisiasi rencana, deskripsi keadaan saat ini, visi masa depan yang ingin dicapai, dan bagaimana mewujudkannya. Pada Level pertama EAP menjelaskan bagaimana inisialisasi rencana otomatisasi yang dijalankan BMKG. Dalam menjalankan tugas dan fungsi terkait pelayanan cuaca penerbangan, BMKG memiliki visi untuk memberikan informasi yang akurat, tepat sasaran, tepat guna, cepat, lengkap, dan dapat dipertanggungjawabkan. Selain itu BMKG juga harus tanggap dalam menangkap dan merumuskan kebutuhan stakeholder akan informasi, serta mampu memberikan pelayanan sesuai dengan kebutuhan pengguna jasa. Dalam konteks pelayanan cuaca untuk penerbangan, output yang diharapkan adalah untuk menjaga keselamatan penerbangan. Level kedua EAP memberikan gambaran bagaimana bisnis model yang sedang terjadi di BMKG. Teknologi dan sistem yang digunakan untuk pengamatan cuaca penerbangan. Level ketiga EAP adalah skenario pandangan arsitektur di masa yang akan datang terkait data, aplikasi, dan teknologi yang akan digunakan. Sedangkan Level terakhir menjabarkan tentang bagaimana implementasi/ migrasi dari sistem yang lama conventional ke sistem otomatis yang baru automated. 3. Hasil dan Pembahasan Dalam upaya mewujudkan otomatisasi dan modernisasi pada proses diseminasi informasi cuaca terkini penerbangan, BMKG menggunakan pendekatan EAP untuk mengimplementasikan teknologi di masa yang akan datang. Proses pemodelan EAP dijalankan dengan tujuh langkah. Diawali dengan inisiasi rencana, pemodelan bisnis, tinjauan sistem dan teknologi yang digunakan saat ini, arsitektur data, arsitektur aplikasi, arsitektur teknologi, dan bagaimana proses implementasi/ migrasi. Planning Initiation BMKG dalam menjalankan tugas pelayanan informasi cuaca penerbangan mempunyai rencana otomatisasi dan modernisasi yang tertuang dalam Peraturan Kepala Badan Meteorologi, Klimatologi, dan Geofisika Nomor 5 Tahun 2014 Tentang Rencana Induk Badan Meteorologi, Klimatologi, dan Geofisika Tahun 2015 – 2045 [12]. Disebutkan bahwa BMKG telah melakukan berbagai upaya percepatan diseminasi informasi baik itu meteorologi, klimatologi, maupun geofisika. Salah satu bentuk otomatisasi yang akan dilakukan adalah mengganti sistem pengamatan konvensional menjadi pengamatan otomatis berbasis alat instrumented yang terintegrasi. Alat otomatis yang digunakan dalam pelayanan pengamatan cuaca terkini untuk penerbangan adalah AWOS. AWOS sebagai alat bantu BMKG dalam mewujudkan visi dalam pelayanan penerbangan yaitu mewujudkan keselamatan penerbangan. Business Modeling Bisnis model yang terjadi di BMKG pada pengamatan cuaca untuk penerbangan dalam penelitian ini fokus pada pengamatan cuaca penerbangan yang memiliki tujuan akhir untuk ikut menjaga keselamatan penerbangan. Proses pelayanan cuaca penerbangan dimodelkan dengan menggunakan Value Chain Model Analysis seperti ditunjukkan pada gambar 2. SIP- 161 THE BLUEPRINT… Duati Wardani Gambar 2. Value Chain Model Analysis BMKG melalui Stasiun Meteorologi memiliki dua aktivitas utama dalam pelayanannya, yaitu Pengamatan cuaca penerbangan serta analisa dan prakiraan cuaca penerbangan. Pengamatan cuaca penerbangan dilakukan oleh seorang pengamat baik menggunakan intrumentasi konvensional maupun AWOS. Alur data yang terjadi adalah parameter cuaca diamati oleh pengamat/AWOS, yang kemudian mengirimkan hasil pengamatan ke pengelola layanan navigasi penerbangan di bawah Perusahaan Umum Lembaga Penyelenggara Pelayanan Navigasi Penerbangan Indonesia Perum. LPPNPI melalui Aeronautical Fixed Telecommunication Network AFTN dan ke BMKG melalui jaringan Computer Message Switching System CMSS, seperti ditunjukkan gambar 3. Gambar 3. Alur Pengamatan Cuaca Current System and Technology Dalam melakukan pelayanan informasi cuaca terkini untuk penerbangan, BMKG saat ini masih menggunakan dua jenis sistem pengamatan, yaitu pengamatan konvensional dan otomatis. Pada pengamatan konvensional, pengamat mengamati cuaca menggunakan instrumentasi konvensional di taman pengamatan kemudian mencatat data pengukuran, dan mengirimkannya sebagai informasi cuaca terkini kepada LPPNPI dalam bentuk MetReport/ Special dan kepada BMKG dalam bentuk Metar/ Speci. Alur Sistem Pengamatan konvensional ditunjukkan pada gambar 4. Gambar 4. Alur Sistem Pengamatan Konvensional SENATIK 2018, Vol. IV, ISBN 978-602-52742-0-6 SIP-162 Sistem Pengamatan Otomatis melibatkan pengamat dan intrumen pengukur cuaca otomatis/ AWOS. Display AWOS sebagai alat bantu pengamat untuk mengetahui nilai parameter unsur cuaca tanpa harus melakukan pengamatan ke taman pengamatan observation park. Input data masih dilakukan secara manual oleh pengamat baik itu ke jaringan AFTN maupun ke jaringan CMSS. Sistem Pengamatan Otomatis yang digunakan BMKG menggunakan AWOS seperti ditunjukkan pada gambar 5. Gambar 5. Alur Sistem Pengamatan Otomatis Data Architecture Untuk melakukan otomatisasi pengamatan secara penuh, BMKG membutuhkan cetak biru blueprint arsitektur terkait data, aplikasi, dan teknologi yang akan digunakan. Arsitektur data yang digunakan dalam perencanaan ini menggunakan pendekatan Two Layer Data Warehouse Architecture yang terdiri dari 4 lapisan. Model data warehouse ini memisahkan media penyimpanan antara sumber data dan data warehouse. Data Architecture ditunjukkan pada gambar 6. Gambar 6. Data Architecture SIP- 163 THE BLUEPRINT… Duati Wardani Lapisan pertama adalah Source Layer. Lapisan ini merupakan sumber data yang berasal dari pengamatan parameter cuaca yang diperoleh dari sensor-sensor AWOS. Data yang diperoleh pada lapisan ini adalah data semua parameter cuaca seperti angin, suhu, kelembaban, hujan, tekanan, penyinaran matahari, jarak pandang, dan awan. Data masing-masing parameter diukur dalam frekuensi waktu tertentu yang sudah diatur sebelumnya, misalnya setiap 2 menit, 10 menit, 1 jam, atau 24 jam. Lapisan kedua adalah Data Staging. Disinilah terjadi proses Extract, Transform, dan Load ETL. Data parameter cuaca dari sensor diekstrak oleh DCP. Kemudian hasil ekstrak ini menjalani proses transformasi yang pada prinsipnya mengubah dalam bentuk standar. Proses Load adalah proses pengiriman data yang sudah menjalani transformasi ke gudang data yang berada dalam CDP. Lapisan ketiga adalah Data Warehouse Layer. Informasi cuaca yang sudah tersimpan dalam gudang data dapat langsung digunakan atau dipisah-pisah dalam data mart sesuai peruntukannya. Pada pelayanan pengamatan cuaca untuk penerbangan, data mart yang dibuat, sesuai peruntukannya, adalah informasi yang berupa MetReport/ Special untuk AFTN dan Metar/ Speci untuk CMSS. Data mart yang lain yang dapat dibentuk adalah untuk keperluan analisis/ prakiraan cuaca terkait cuaca penerbangan dan iklim. Lapisan keempat adalah Analysis Layer. Lapisan ini digunakan untuk melakukan pemanfaatan informasi dari data mart. Proses yang terjadi pada lapisan ini adalah penyandian data untuk pengiriman MetReport/ Special ke jaringan AFTN Bandara, pengiriman Metar/Speci pada jaringan CMSS, dan Analisa Prakiraan Cuaca ke jaringan lokal. Application Architecture Arsitektur aplikasi yang baik untuk digunakan dalam model pelayanan informasi pengamatan cuaca penerbangan ini adalah berbasis client server Two-Tier Application. Server sebagai penyedia data dan client adalah pengguna data end-user. Service bisnis yang terjadi dikelola di sisi server server-centric. Hal ini akan memudahkan ketika terdapat perubahan service bisnis. Perubahan lebih cepat karena cukup hanya dilakukan di sisi server saja. Gambar 7 menunjukkan model Server Centric. Gambar 7. Server Centric Model Aristektur aplikasi perlu dipetakan untuk mengintegrasikan seluruh kebutuhan bisnis organisasi akan informasi. Kebutuhan bisnis dalam konteks ini adalah kebutuhan informasi cuaca penerbangan terkini yang didapat dari hasil pengamatan parameter cuaca. Arsitektur aplikasi pada pelayanan pengamatan cuaca penerbangan ditunjukkan pada gambar 8. SENATIK 2018, Vol. IV, ISBN 978-602-52742-0-6 SIP-164 Gambar 8. Aplication Architecture Sensor-sensor merupakan sumber data dari data warehouse. Data warehouse menyediakan data mart untuk masing-masing aplikasi sesuai dengan kebutuhannya. Khusus untuk aplikasi-aplikasi dengan tipe broadcast messages, diperlukan proses validasi, untuk memastikan tidak terjadi kesalahan pada pengukuran sensor. Technology Architecture Arsitektur teknologi adalah skenario teknologi yang digunakan untuk mengimplementasikan otomatisasi pelayanan informasi cuaca terkini untuk penerbangan di lingkungan BMKG. Arsitektur teknologi mendeskripsikan kebutuhan infrastruktur, termasuk jaringan, yang dibutuhkan dalam mewujudkan visi yang ingin dicapai. Skenario arsitektur teknologi ditunjukkan pada gambar 9. Gambar 9. Technology Architecture Source Layer terdiri dari sensor-sensor pengukur parameter cuaca yang akan memberikan nilai pengamatan pada waktu tertentu yang akan dikumpulkan oleh DCP, diekstrak, dan dikirimkan ke CDP pada Core Layer. Pada lapisan inilah data-warehouse dan data-mart disimpan yang kemudian akan didistribusikan melalui Distribution Layer untuk selanjutnya dibagi sesuai kebutuhan akses end-user pada Access Layer. Konektivitas dari sumber data source hingga ke core layer dirancang menggunakan saluran fiber optic. Fiber optik merupakan media transfer data paling efektif, memiliki tingkat SIP- 165 THE BLUEPRINT… Duati Wardani loss data dan gangguan yang rendah, serta bandwith yang tinggi untuk menjaga keberlangsungan data yang berkesinambungan secara realtime. Semua lapisan dalam jaringan yaitu core layer, distribution layer dan access layer menggunakan jalur ganda pada switch yang dipakai, sehingga ketika salah satu perangkat switch rusak/ off, otomatis akan melalui switch yang lain agar tetap terhubung. Begitu juga untuk koneksi ke luar internet menggunakan lebih dari satu provider sehingga konektivitas tetap terjaga guna mendukung proses data sharing layanan penerbangan. Implementation/ Migration Plans Proses implementasi pelayanan pengamatan cuaca otomatis di BMKG diawali dengan pengadaan AWOS untuk stasiun-stasiun yang masih menggunakan sistem pengamatan konvensional. Sedangkan untuk stasiun yang sudah menggunakan AWOS supaya dapat merealisasikan otomastisasi penuh pada kegiatan pengamatan, sehingga mengurangi campur tangan manusia dalam proses ini. Pengamat dibutuhkan hanya untuk melakukan validasi ketika ada sensor otomatis yang tidak bekerja dengan semestinya. Peralatan pengukur cuaca konvensional dialihfungsikan menjadi alat bantu validator dari informasi AWOS. Proses otomatisasi ini tidak dapat serta merta dilakukan dengan semata-mata menggantikan sistem pengamatan manual menjadi otomatis begitu saja. Di masing-masing stasiun perlu dilakukan dual observation pengamatan bersama otomatis dan manual secara overlapping selama 2 hingga 3 tahun berturut-turut untuk menentukan dan mengidentifikasi faktor-faktor koreksi yang harus dicakup dalam data analisis. Pemeliharaan AWOS yang berkesinambungan dan kalibrasi yang terjadwal menjadi poin penting yang harus diperhatikan untuk menjaga kualitas data pengamatan. BMKG juga perlu melakukan integrasi semua peralatan AWOS yang sudah terpasang dengan memperhatikan prinsip-prinsip interoperabilitas agar tercipta standar-standar konektivitas untuk memudahkan proses pengembangan sistem di masa yang akan datang. Teknologi Cloud menjadi referensi untuk dapat mengkoneksikan data AWOS dengan data dari instrumentasi otomatis lainnya seperti Automatic Weather Stations AWS, Agroclimate Auotomatic Weather Stations AAWS, maupun Automatic Rain Gauge ARG sehingga tercipta integrasi yang baik di lingkungan BMKG maupun dengan instansi terkait lainnya. 4. Kesimpulan Enterprise Architecture Planning EAP dapat digunakan untuk membuat cetak biru blueprint implementasi pengamatan cuaca untuk pelayanan penerbangan di lingkungan BMKG. EAP mampu mendefinisikan kebutuhan bisnis dan arsitektur terkait data, aplikasi, dan teknologi yang dibutuhkan untuk mengimplementasikan otomatisasi. Overlapping pada proses migrasi diharapkan mampu menjadi bahan evaluasi dalam implementasi otomatisasi. Daftar Pustaka [1] Poerwanto, E., & Mauidzoh, U. 2016. Analisis Kecelakaan Penerbangan Di Indonesia Untuk Peningkatan Keselamatan Penerbangan. Angkasa Jurnal Ilmiah Bidang Teknologi, 82, 9-26. SENATIK 2018, Vol. IV, ISBN 978-602-52742-0-6 SIP-166 [2] Kementrian Perhubungan, Peraturan Menteri Perhubungan Republik Indonesia No. PM 9 Tahun 2015 tentang Peraturan Keselamatan Penerbangan Sipil Bagian 174 Civil Aviation Safety Regulations Part 174 Tentang Pelayanan Informasi Meteorologi Penerbangan Aeronautical Meteorological I. Jakarta, 2015. [3] BMKG, Peraturan Kepala Badan Meteorologi, Klimatologi, dan Geofisika Republik Indonesia Tahun 2017. Jakarta, 2017. [4] BMKG, Peraturan Kepala BMKG Tahun 2015 Tentang Rencana Strategis Badan Meteorologi Klimatologi dan Geofisika Tahun 2015–2019. Jakarta, 2015. [5] Poerwanto, E., & Gunawan, G. 2015. Analisis Beban Kerja Mental Pekerja Bagian Ground H Andling Bandara Adisutjipto untuk Mendukung Keselamatan Penerbangan. Angkasa Jurnal Ilmiah Bidang Teknologi, 72, 115-126. [6] E. Buyukbas, L. Yalcin, Z. T. Dag, and S. Karatas, “Instruments and Observing Methods,” Alanya, Turkey, 2005. [7] BMKG, SOP Tahun 2017 Tentang Pelayanan Cuaca Untuk Informasi Cuaca Penerbangan Bila Sarana AWOS Terjadi Malfungsi. Jakarta, 2017. [8] A. W. Inc., Automated Weather Observing System AWOS 3000 User’s Manual, Sacramento, CA, USA All Weather Inc., 2017. [9] Bente, S., Bombosch, U., & Langade, S. 2012. Collaborative enterprise architecture Enriching ea with lean. Agile, and Enterprise practices, Eds. Elsevier. [10] Spewak, S. H., & Hill, S. C. 1993. Enterprise architecture planning developing a blueprint for data, applications and technology. QED Information Sciences, Inc.. [11] S. Kasus, B. Pendidikan, D. Kab, and L. Tengah, “Perancangan arsitektur sistem informasi menggunakan enterprise arsitecture planning,” vol. 13, no. 1, pp. 41–51, 2013. [12] P. B. Rencana Induk Badan Meteorologi, Klimatologi, dan Geofisika Tahun 2015-2045. Jakarta, 2014. ... The Meteorology, Climatology, and Geophysics Agency BMKG is a strategic agency in Indonesia regarding weather whose interests extend to aviation security [1]. The BMKG processes many weather data with complex problems that require advanced artificial intelligence skills, such as earthquake prediction, fire prediction, and wind power prediction [2]- [4]. ...... We use several regression testing metrics in this study, namely r 2 , M SE, and M BE. The value of r 2 is the squared value of the result of Equation 1. The value range is from 0 to 1. Results closer to 1 show that the regression model has good performance, the opposite if it is close to 0. While the M SE formula is as follows ...Ikke Dian Oktaviani Aji Gautama PutradaThe prediction of rain duration based on data from the Meteorology, Climatology, and Geophysics Agency BMKG is an important issue but remains an open problem. At the same time, several studies have shown that missing values can cause a decrease in the performance of the model in making predictions. This study proposes k-nearest neighbors KNN imputation to overcome the problem of missing values in predicting rain duration. The source of the rain duration prediction dataset is the BMKG data. We compared gradient boosting regression GBR, adaptive boosting regression ABR, and linear regression LR for the regression model for predicting rain duration. We compared the KNN imputation method with several benchmark methods, including zero imputation, mean imputation, and iterative imputation. Parameters r2, mean squared error MSE and mean bias error MBE measure the performance of these imputation methods. The test results show that for rain duration prediction using the regression method, GBR shows the best performance, both for train data and test data with r2 = and respectively. Then our proposed KNN imputation has the best performance for missing value imputation compared to the benchmark imputation method. The prediction values of r2 and MSE when using KNN imputation at Missing Percentage = 90% are and respectively.... Salah satu pelayanan yang didorong percepatannya adalah pengefisiensian waktu agar mengurangi waktu kerja yang dibutuhkan sehingga pekerjaan dilakukan dengan cepat [1]. Pelayanan tersebut meliputi pelayanan pre-flight service, in-flight service, dan post-flight service [2]. Untuk mendukung peningkatan pelayanan pada moda transportasi udara perlu didukung oleh personel yang memiliki kompetensi dan sarana keselamatan penerbangan yang efektif dan tepat guna [3]. ...... Perjalanan pesawat terbang yang mengambil rute tertentu dapat dilihat dan dipantau [2], hal ini dilakukan agar keselamat penerbangan lebih terjamin. Faktor dalam keselamatan penerbangan dipengaruhi oleh cuaca [3] dan penanganan pesawat saat didarat [4]. Penanganan kecelakaan pesawat terbang di Indonesia semakin baik [5], hal ini didukung dengan pembelajaran mengenai peswat terbang [6] dan pengenalan ruangan dalam pesawat terbang [7]. ...Eko PoerwantoGunawan GunawanIncreased need for air transport will increase the activity of ground handling at airports. Increased activity of this will affect the mental workload received personnel who carry it out. Any increase in mental workload will affect the occurrence of human error and affect flight safety. Analysis of mental workload ofpart o f ground handling personnel is very important to ensure acceptable personnel workloads according to workload capacity available. This mental workload research using NASA-TLX method, that the procedure uses a multi-dimensional rating, and divide the workload on the basis of the average loading 6 dimensions, namely Mental Demand, Physical Demand, Temporal Demand, Effort, Own Performance, and frustation. NASA-TLX is divided into two phases, namely a comparison of each scale Paired Comparison and giving value to the work Event Scoring. The research objective is to ensure the mental workload of part of ground handling Adisucipto airport in Yogyakarta, in accordance with their capacity, so as to avoid human error and to support aviation safety. The results showed that the mean score of mental workload ground handling activities by PT. Gapura Air and PT. Kokapura Avia in Yogyakarta Adisucipto airport in the mental workload optimization group, which indicates mental workload received by workers are safe no overload.Eko PoerwantoUyuunul MauidzohAchievement level of aviation safety can be achieved with the proper function of all components of the system in the aviation industry which consists of airport operators, airline operators, air traffic operators and aircraft maintenance operator, as well as the regulations set by the regulator. Every incident should be investigated aviation accidents to fin d the cause. This is to provide appropriate recommendations so that the same airline accident does not happen again. The increasing number of flights that are needed with safety guarantees. So it is importance to analyzed routine flight accident to improve the safety performance of airlines. This research is descriptive analysis with qualitative methods. Flight accidents data that have investigated from NTSC and DGCA grouped causes are then recommendations have been made by the NTSC also grouped for each operator stakeholders. Improved system of aviation safety in Indonesia can be done with a thorough analysis based on the results of investigation of NTSC whose recommendations have been given to all stakeholders in the aviation industry. The results showed that the causes of flight accidents in Indonesia is dominated by the human factor the percentage reached 60%. The highest number of the recommendations given by the NTSC to DGCA as many as 208 recommendations during the period 2007-2014 but the trend o f declining. On other side of the trend of the recommendations given to aviation operators showed an increase. This shows an increase in the duty on DGCA to always supervise, and set the standard flight operations carried out by several airline operators in H. SpewakSteven C. HillThe emphasis of this book is on the interpersonal skills and techniques for organizing and directing an EAP project, obtaining management commitment, presenting the plan to management, and leading the transition from planning to Menteri Perhubungan Republik Indonesia No. PM 9 Tahun 2015 tentang Peraturan Keselamatan Penerbangan Sipil Bagian 174 Civil Aviation Safety Regulations Part 174 Tentang Pelayanan Informasi Meteorologi Penerbangan Aeronautical Meteorological IKementrian PerhubunganKementrian Perhubungan, Peraturan Menteri Perhubungan Republik Indonesia No. PM 9 Tahun 2015 tentang Peraturan Keselamatan Penerbangan Sipil Bagian 174 Civil Aviation Safety Regulations Part 174 Tentang Pelayanan Informasi Meteorologi Penerbangan Aeronautical Meteorological I. Jakarta, BmkgKepala BadanMeteorologiBMKG, Peraturan Kepala Badan Meteorologi, Klimatologi, dan Geofisika Republik Indonesia Tahun 2017. Jakarta, Rencana Strategis Badan Meteorologi Klimatologi dan Geofisika TahunPeraturan BmkgBmkg KepalaNoBMKG, Peraturan Kepala BMKG Tahun 2015 Tentang Rencana Strategis Badan Meteorologi Klimatologi dan Geofisika Tahun 2015-2019. Jakarta, and Observing MethodsE BuyukbasL YalcinZ T DagS KaratasE. Buyukbas, L. Yalcin, Z. T. Dag, and S. Karatas, "Instruments and Observing Methods," Alanya, Turkey, Pelayanan Cuaca Untuk Informasi Cuaca Penerbangan Bila Sarana AWOS Terjadi MalfungsiSop NoBMKG, SOP Tahun 2017 Tentang Pelayanan Cuaca Untuk Informasi Cuaca Penerbangan Bila Sarana AWOS Terjadi Malfungsi. Jakarta, Weather Observing System AWOS 3000 User's ManualA W IncA. W. Inc., Automated Weather Observing System AWOS 3000 User's Manual, Sacramento, CA, USA All Weather Inc., enterprise architecture Enriching ea with lean. Agile, and Enterprise practicesS BenteU BomboschS LangadeBente, S., Bombosch, U., & Langade, S. 2012. Collaborative enterprise architecture Enriching ea with lean. Agile, and Enterprise practices, Eds. Elsevier.
. de270iu0ji.pages.dev/121de270iu0ji.pages.dev/161de270iu0ji.pages.dev/251de270iu0ji.pages.dev/386de270iu0ji.pages.dev/38de270iu0ji.pages.dev/384de270iu0ji.pages.dev/381de270iu0ji.pages.dev/293de270iu0ji.pages.dev/354
automatic weather station bmkg
