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Optimalisasi Saluran Komunikasi Berbasis Gelombang Mikro Sebagai Alternatif Sistem Pemantauan Curah Hujan R. Yudha Mardyansyah; Budhy Kurniawan; Santoso Soekirno; Danang Eko Nuryanto
Elektron : Jurnal Ilmiah Volume 14 Nomor 1 Tahun 2022
Publisher : Teknik Elektro Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/eji.14.1.278

Abstract

As a vast archipelagic country with diverse topographic conditions and has an annual average rainfall of more than 2000 mm, Indonesia is prone to hydrometeorological disasters. Based on Indonesia's disaster data, throughout 2021 there were 3,658 incidents of floods and landslides distributed throughout Indonesia. This makes real-time rainfall monitoring with high density indispensable. Indonesia currently has a rainfall monitoring system about 1000 automatic rain gauges, so an increase in the spatial resolution of network is necessary. The increasing density of monitoring equipment using rain gauges and weather radar poses the problem of high procurement and operational costs. Therefore, several alternative rainfall monitoring systems are needed. In this article, we review several studies that focus on the utilization of terrestrial and satellite communication link operating in high frequency bands as an alternative for measuring rainfall. Optimization of the satellite communication system network is more suitable than terrestrial networks to be applied in Indonesia with archipelagic areas because it has a large number of point distributions with wider coverage. The use of artificial intelligence with deep learning techniques such as one dimensional convolutional neural network (1D-CNN) is also very promising to estimate rainfall intensity because it has a high accuracy of 93%..
Penggunaan Perangkat Lunak dari Open Source dan Metode Komputasi untuk Prediksi Kebisingan yang Ditimbulkan oleh Airframe Pesawat Terbang Aprilia Sakti Kusumalestari; Santoso Soekirno
Wahana Fisika Vol 5, No 2 (2020): December
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/wafi.v5i2.27986

Abstract

Prediksi kebisingan airframe pesawat terbang, sudah banyak dilakukan dengan berbagai perangkat lunak oleh para peneliti di berbagai negara. Tetapi hanya beberapa peneliti yang menggunakan perangkat lunak dari Open Source. Studi ini merupakan bagian dari desertasi penulis tentang pemodelan peredaman kebisingan airframe pesawat terbang. Mengacu pada penelitian Jarosz, dkk (2016) dan pada diskusi internal di bagian aerodinamika, Direktorat Teknologi dan Pengembangan PTDI (2019), pada studi ini dianalisis kemungkinan penggunaan perangkat lunak OpenFOAM® dari Open Source untuk prediksi kebisingan salah satu pesawat buatan Indonesia di hangar PTDI. Kebisingan airframe pesawat terbang, yang merupakan fluktuasi rapat massa dan momentum dalam medium fluida yang bergerak dengan kecepatan tinggi, dinyatakan dalam persamaan diferensial Ffowcs Williams – Hawkings. Persamaan ini terdiri dari suku-suku yang menginterpretasikan medan quadrupol, dipol, dan monopol dari rapat massa dan momentum terhadap posisi dan waktu. Dengan menganalisis sifat-sifat persamaan, bentuk airframe pesawat, dan asumsi fisis yang diterapkan, maka disimpulkan kepastiannya untuk penggunaan OpenFOAM, dengan metode komputasi yang menggunakan simulasi untuk kebisingan yang ditimbulkan oleh aliran murni aerodinamiknya, dan menggunakan pemodelan untuk kebisingan yang ditimbulkan oleh aliran turbulensinya. Hal ini diharapkan menjadi salah satu benchmark dalam penelitian perancangan pesawat terbang di Indonesia yang memperhitungkan peredaman kebisingan sebagai bagian dari optimasi perancangan.
Communication Satellite-Based Rainfall Estimation for Flood Mitigation in Papua Raden Yudha Mardyansyah; Budhy Kurniawan; Santoso Soekirno; Danang Eko Nuryanto
Jurnal Penelitian Pendidikan IPA Vol 10 No 12 (2024): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i12.8409

Abstract

Papua, an equatorial region in Indonesia, faces unique geographical and natural challenges, including heavy annual rainfall. This heavy rainfall increases flooding risks and impacts infrastructure, the economy, and daily life. Despite the importance of rain gauges for monitoring floods and climate change, Papua's difficult geography and limited transportation infrastructure hinder their installation and maintenance. In this work, we investigate a deep learning one-dimensional convolution neural network (1DCNN) model to estimate rainfall intensity using energy per symbol to noise power density ratio (Es/No) of the signals received from a communication satellite signal coupled with additional data representing satellite daily movement. The findings of this study demonstrate that the performance of the proposed model has a higher accuracy for moderate to heavy rainfall than for light rainfall. The NRMSE values for light rain, moderate rain, and heavy rain are 47.09, 31.78, and 33.58%, respectively. These results show that this method is promising for monitoring heavy rainfall as a flood mitigation effort. However, there is still room to improve the accuracy of the estimation such as using other secondary data that is highly correlated with rain at the satellite transceiver location.