%X 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.
%T LANDSLIDE PREDICTION DATA PROCESSING USING LOGISTIC REGRESSION ALGORITHM
%I IEEE
%O 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
%A - Alvin Dzaki Pratama Darmawan
%A - Arjuni Budi Pantjawati
%A - Aip Saripudin
%L repoupi143773
%D 2025
%K Vibrations, Seminars, Landslides, Logistic regression, Accuracy, Machine learning, 
Humidity, Predictive models, Terrain factors, Sensor systems
%J Landslide Prediction Data Processing Using Logistic Regression Algorithm