PuslitbangBMKG tahun 2014 tentang perbandingan data pengamatan parameter meteorologi antara metode manual dan otomatis melalui otomatisasi instrument cuaca dan iklim menggunakan Agroclimate Automatic Weather Station di Stasiun Dramaga, Stasiun Sicincin, Stasiun Banyuwangi, dan Stasiun Kediri melaporkan bahwa rata-rata error
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. 7Sensor Weather Station Sekarang Telah Memiliki Sertifikat BMKG. Senin, 21 Maret 2022. Kalibrasi adalah proses pengaturan dan pengecekan tingkat keakurasian alat ukur dengan menggunakan cara membandingkan dengan standar atau tolak ukur. Kalibrasi sangat diperlukan untuk memastikan bahwa hasil dari pengukuran yang dilakukan nantinya akurat 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.
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Metadatastasiun BMKG di Kota Bandung. Terdiri dari stasiun Automatic Weather Station (AWS), pos hujan kerjasama dan UPT. Data and Resources. Tahun 2016 - Metadata Stasiun BMKG CSV.
Jakarta ANTARA News - The Meteorology, Climatology and Geophysics Agency BMKG plans to set up automatic weather stations to provide weather information during the 18th Asian Games. "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. The devices will be installed in the venues of sports which are sensitive to weather such as paragliding, he said. Earlier, BMKG said it will provide a weather forecast map with micro scale around the venues of the 18th Asian Games. The map is important to ensure that the pan Asian multi sports event will run smoothly without weather obstacles. The map will provide detailed weather forecast up to the level of sub-district and even venues of competition such as Ancol, Senayan, Sunter and Jakabaring. Indonesia will host the 18th Asian Games in Jakarta and Palembang from August 18 to September 2, 2018, the peak of this year`s dry season which began late April. Around 15,000 athletes and distinguished delegates from 45 members of the Olympic Council of Asia OCA will take part in the sports event held every four years. The number will be the largest in the Asian Games history. Reported by Desi Purnamawati

AAWS(Automatic Agroclimate and Weather Station) AAWS (Automatic Agroclimate and Weather Station) atau Pos Pemantau Otomatis Agroklimat dan Cuaca adalah alat yang dipasang BMKG untuk mengamati unsur-unsur cuaca dan iklim secara otomatis untuk pemanfaatannya diarahkan ke sektor pertanian. Peralatan observasi agroklimat otomatis menghasilkan data

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 Z5r4.
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