Algoritme deteksi kedatangan tsunami otomatis untuk sistem observasi tinggi muka air laut
| Main Authors: | Sriyanto, Sesar Prabu Dwi, Angmalisang, Ping Astony, Manu, Lusia, Schaduw, Joshian N. W., Sondak, Calvyn F. A., Mantiri, Rose O. S. E, Luasunaung, Alfret, Sumilat, Deiske A. |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | ind |
| Terbitan: |
Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro
, 2021
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| Subjects: | |
| Online Access: |
https://jtsiskom.undip.ac.id/article/view/14009 https://jtsiskom.undip.ac.id/article/view/14009/12706 |
Daftar Isi:
- Agar dapat menginformasikan kedatangan tsunami dengan cepat kepada masyarakat, sistem observasi muka air laut perlu dilengkapi dengan algoritme deteksi tsunami otomatis. Penelitian ini bertujuan merancang algoritme deteksi tsunami yang terdiri dari 3 subalgoritme, yaitu eliminasi spike, pengisian data kosong, dan pendeteksi tsunami. Subalgoritme eliminasi spike dan pengisian data kosong digunakan untuk memperbaiki data observasi tinggi muka air laut yang sering terganggu oleh spike dan data kosong akibat faktor elektronik peralatan. Hasil perancangan diuji dengan data historis tide gauge saat terjadi tsunami antara tahun 2007-2019. Hasilnya, spike telah tereliminasi sebanyak 54,52 % dari 409 kemunculan, sedangkan data kosong berhasil diisi 100%. Pendeteksian tsunami yang menggunakan metode DART (Deep-ocean Assessment and Reporting of Tsunamis) dan TEDA (Tsunami Early Detection Algorithm) mampu mendeteksi 7 dari 10 sinyal tsunami, namun masih ada 3 sinyal yang tidak terdeteksi dan 1 kesalahan deteksi. Selain itu, rata-rata waktu pendeteksian tsunami sekitar 7,7 menit setelah tiba di lokasi tide gauge.
- The automatic tsunami detection algorithm needs to be put in the sea level observation system to give society a quick warning when a tsunami happens. This study designs an automatic tsunami detection algorithm consisting of three sub-algorithm: spike elimination, gap data filling, and tsunami detection. Spike elimination and gap data filling are used to improve the sea level data, which is often disturbed by spikes and gap data due to electronic factors. This algorithm was tested using time-series tide gauge data that contain tsunami waveforms in Indonesia from 2007 to 2019. About 54.52 % of 409 spikes have been eliminated while the gap data were successfully filled. Furthermore, tsunami detection, which uses DART (Deep-ocean Assessment and Reporting of Tsunamis) and TEDA (Tsunami Early Detection Algorithm) methods, can detect 7 of 10 tsunami waveforms. However, there are three undetected tsunamis and one false detection. This algorithm has an average delay of 7.7 minutes in detection time.