eprintid: 143773 rev_number: 22 eprint_status: archive userid: 220648 dir: disk0/00/14/37/73 datestamp: 2025-10-21 09:38:21 lastmod: 2025-10-21 09:38:21 status_changed: 2025-10-21 09:38:21 type: thesis succeeds: 143764 metadata_visibility: show creators_name: Alvin Dzaki Pratama Darmawan, - creators_name: Arjuni Budi Pantjawati, - creators_name: Aip Saripudin, - creators_nim: NIM2104188 creators_nim: NIDN0007066403 creators_nim: NIDN0016047007 creators_id: alvindlink@gmail.com creators_id: arjunib@upi.edu creators_id: aipsaripudin@upi.edu contributors_type: http://www.loc.gov/loc.terms/relators/THS contributors_type: http://www.loc.gov/loc.terms/relators/THS contributors_name: Arjuni Budi Pantjawati, - contributors_name: Aip Saripudin, - contributors_nidn: NIDN0007066403 contributors_nidn: NIDN0016047007 contributors_id: arjunib@upi.edu contributors_id: aipsaripudin@upi.edu title: LANDSLIDE PREDICTION DATA PROCESSING USING LOGISTIC REGRESSION ALGORITHM ispublished: pub subjects: Q1 subjects: QE subjects: TK divisions: JPTE full_text_status: restricted keywords: Vibrations, Seminars, Landslides, Logistic regression, Accuracy, Machine learning, Humidity, Predictive models, Terrain factors, Sensor systems 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 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. date: 2025-07-30 date_type: published publication: Landslide Prediction Data Processing Using Logistic Regression Algorithm publisher: IEEE institution: Universitas Pendidikan Indonesia department: KODEPRODI83201#Pendidikan_Teknik_Elektro_S1 thesis_type: other thesis_name: other refereed: TRUE issn: 2832-1456 official_url: https://ieeexplore.ieee.org/document/10963634 related_url_url: https://perpustakaan.upi.edu/ related_url_type: org citation: 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. document_url: http://repository.upi.edu/143773/3/TA_ART_S_TE_2140188_SK.pdf document_url: http://repository.upi.edu/143773/2/TA_ART_S_TE_2104188_ART.pdf