Klasifikasi Sinyal Seismik Menggunakan Algoritma Short Term Average/Long Term Average (STA/LTA) dan K-Nearest Neighbor (KNN). (Seismic Signal Classification Using Short Term Average/Long Term Average (STA/LTA) and K-Nearest Neighbor (KNN) Algorith).

Parenza, Diko Akhbar Febyan (2024) Klasifikasi Sinyal Seismik Menggunakan Algoritma Short Term Average/Long Term Average (STA/LTA) dan K-Nearest Neighbor (KNN). (Seismic Signal Classification Using Short Term Average/Long Term Average (STA/LTA) and K-Nearest Neighbor (KNN) Algorith). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

This research explores the effectiveness of using the K-Nearest Neighbor (KNN) algorithm in seismic signal analysis for earthquake detection, with the support of the Short Term Average/Long Term Average (STA/LTA) algorithm. Earthquakes, which result from friction between Earth's plates and generate seismic waves, are typically studied using seismic signal processing technology. While the STA/LTA algorithm has been widely used as a traditional method, machine learning-based algorithms like KNN have shown promise in earthquake detection by classifying new data based on similarities to previously trained data. This study focuses on assessing the earthquake detection capabilities of KNN, including its accuracy, strengths, and weaknesses. The ultimate goal is to gain a better understanding of the application of the KNN algorithm in seismic signal analysis, as well as the contribution of the STA/LTA algorithm in enhancing earthquake detection.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	STA/LTA algorithm, KNN algorithm, seismic signal analysis, earthquake detection, machine learning
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General)
Divisions:	Fakultas Teknik > Program Studi Teknik Informatika
Depositing User:	1462000091 Diko Akhbar Febyan Parenza
Date Deposited:	05 Sep 2001 18:58
Last Modified:	04 Oct 2001 18:00
URI:	http://repository.untag-sby.ac.id/id/eprint/34649

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