@phdthesis{repoupi143773,
          school = {Universitas Pendidikan Indonesia},
            note = {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},
            year = {2025},
         journal = {Landslide Prediction Data Processing Using Logistic Regression Algorithm},
       publisher = {IEEE},
           month = {July},
           title = {LANDSLIDE PREDICTION DATA PROCESSING USING LOGISTIC REGRESSION ALGORITHM},
          author = {Alvin Dzaki Pratama Darmawan, - and Arjuni Budi Pantjawati, - and Aip Saripudin, -},
             url = {https://ieeexplore.ieee.org/document/10963634},
        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.},
        keywords = {Vibrations, Seminars, Landslides, Logistic regression, Accuracy, Machine learning, 
Humidity, Predictive models, Terrain factors, Sensor systems},
            issn = {2832-1456}
}