LANDSLIDE PREDICTION DATA PROCESSING USING LOGISTIC REGRESSION ALGORITHM

Alvin Dzaki Pratama Darmawan, - and Arjuni Budi Pantjawati, - and Aip Saripudin, - (2025) LANDSLIDE PREDICTION DATA PROCESSING USING LOGISTIC REGRESSION ALGORITHM. S1 thesis, Universitas Pendidikan Indonesia.

Abstract

Landslides pose a significant threat to life and property. This research aims to develop a machine-learning model to predict landslide occurrences, focusing on logistic regression. Key factors such as inclination, vibration, humidity, and precipitation thresholds are considered to categorize areas into risk levels: safe, warning, alert, and danger. To address the challenges of real world data acquisition, a simulated environment was employed to generate a comprehensive dataset. The developed model achieved an accuracy of 84% on the validation dataset, demonstrating its potential for accurate landslide prediction. This research contributes to the advancement of early warning systems and risk mitigation strategies for landslide-prone areas.

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Item Type:	Thesis (S1)
Additional Information:	SINTA ID DOSEN PEMBIMBING Arjuni Budi Pantjawati: 5994602 Aip Saripudin: 6002410 Karya ini adalah tugas akhir setara dengan skripsi sesuai dengan SK Dekan Fakultas Pendidikan Teknik dan Industri Nomor: 6891/UN40.A5/PK.03.03/2025
Uncontrolled Keywords:	Vibrations, Seminars, Landslides, Logistic regression, Accuracy, Machine learning, Humidity, Predictive models, Terrain factors, Sensor systems
Subjects:	Q Science > Q Science (General) Q Science > QE Geology T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Fakultas Pendidikan Teknik dan Industri > Jurusan Pendidikan Teknik Elektro
Depositing User:	Alvin Dzaki Pratama Darmawan
Date Deposited:	21 Oct 2025 09:38
Last Modified:	21 Oct 2025 09:38
URI:	http://repository.upi.edu/id/eprint/143773

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