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All Journal Journal of Geoscience, Engineering, Environment, and Technology JURNAL PENDIDIKAN GLASSER Esteem Journal of English Study Programme Jurnal Kolaboratif Sains JURNAL ABDI MASYARAKAT INDONESIA (JAMIN) Jurnal Informatika Polinema (JIP) Future Academia : The Journal of Multidisciplinary Research on Scientific and Advanced Jurnal El-Hamra : Kependidikan dan Kemasyarakatan An Najah (Jurnal Pendidikan Islam dan Sosial Keagamaan)
Misbahudin
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Journal : Journal of Geoscience, Engineering, Environment, and Technology

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Identification Comparison of Landslide Potential Area with Analytical Hierarchy Process and Logistic Regression Methods in Cisarua District and Surroundings, Bogor Regency, West Java Pratama, Agun Romdha Sastra; Misbahudin
Journal of Geoscience, Engineering, Environment, and Technology Special Issue from The 2nd International Conference on Upstream Energy Technology and Digitalization
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2025.10.1.1.24031

Abstract

Landslide is one of the geological disasters that often occur in Indonesia, especially in Cisarua District and its surroundings, Bogor Regency, West Java, so mitigation activities need to be carried out, one of which is by making a map of potential areas of landslide in the research area. This study aims to analyze the spatial distribution of potential landslides in Cisarua District, Bogor Regency, using the Analytical Hierarchy Process (AHP) and Logistic Regression (LR) methods, and to determine the more effective method for landslide prediction in the study area. The results showed that the AHP method obtained the distribution of potential areas of landslide in the low category with an area of 39,592 km2 with a percentage of 38.99%, the moderate category with an area of 39,690 km2 with a percentage of 39.09% and the high category with an area of 22,253 km2 with a percentage of 21.92%. In the LR method, the potential for landslide was obtained in the low category with an area of 39,158 km2 with a percentage of 38,15%, the moderate category with an area of 39,485 km2 with a percentage of 38.86%, and the high category with an area of 22,967 km2 with a percentage of 23% and the best method in determining the potential area of landslide was the LR method with an overall accuracy level of 86.41% compared to the Analytical Hierarchy Process (AHP) method with an overall accuracy of 78.64%.
Landslide Susceptibility Mapping Using Logistic Regression Methods in Bogor Regency Assyidiqi, Sutan Vasya; Roviansah, Mohamad; Sujaka, Muhammad 'Azza; Nugroho, Rio Priandri; Misbahudin
Journal of Geoscience, Engineering, Environment, and Technology Special Issue from The 2nd International Conference on Upstream Energy Technology and Digitalization
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2025.10.1.1.24273

Abstract

Landslides are a recurrent hazard in Bogor Regency, where steep volcanic terrain, high rainfall, varied lithology, land-use changes and active faults contribute to slope instability. This study presents the first regency-wide landslide susceptibility model using Logistic Regression supported by field validation. A dataset of 220 landslide occurrences from 2017 to 2022 and multiple geospatial factors including rainfall, slope, lithology, landcover, and NDVI was analyzed using a 70:30 train–test split to generate coefficient weights, probability surfaces and a binary susceptibility map derived from ROC-AUC thresholds. Landcover shows the strongest positive influence on landslide occurrence, whereas NDVI has the strongest negative effect, reflecting the stabilizing role of vegetation. Fault proximity exhibits near-zero influence, likely due to inactive structures or limited spatial resolution. The model achieved 82 percent accuracy with an AUC of 0.86. Susceptibility clustering near historical data suggests possible inventory bias. Improving model reliability will require more evenly distributed landslide data and UAV-based mapping to detect vegetation-covered past landslides.
Co-Authors Afni, Nur' Ahmad Syahid Ai Jamilah Ajisoko, Pangkuh Aprilia Arico, Yusuf Assyidiqi, Sutan Vasya Ayu Lestari Azhar Dimas Wijaya Fadly Umar Hidayatu Munawaroh Kartika Sari Kusuma, Fanny Lulu Nazwa Diaz Medriono Morsal Lamo Megantara, Zulham Moh. Ardhi Afnan Ni Luh Fidriana Nugroho, Rio Priandri Pratama, Agun Romdha Sastra Roviansah, Mohamad Rudy Ariyanto Salam, Mochamad Fadlani Siti Nofiyah Suharni Montjai Sujaka, Muhammad 'Azza Suriani Yan Satriyo Yoppy Yunhasnawa