TY  - THES
AV  - restricted
M1  - other
TI  - LANDSLIDE PREDICTION DATA PROCESSING USING LOGISTIC REGRESSION ALGORITHM
KW  - Vibrations
KW  -  Seminars
KW  -  Landslides
KW  -  Logistic regression
KW  -  Accuracy
KW  -  Machine learning
KW  -  
Humidity
KW  -  Predictive models
KW  -  Terrain factors
KW  -  Sensor systems
SN  - 2832-1456
UR  - https://ieeexplore.ieee.org/document/10963634
ID  - repoupi143773
N2  - 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.
Y1  - 2025/07/30/
PB  - Universitas Pendidikan Indonesia
N1  - 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
A1  - Alvin Dzaki Pratama Darmawan, -
A1  - Arjuni Budi Pantjawati, -
A1  - Aip Saripudin, -
ER  -