Analisis Spasial Kerentanan Tanah Longsor Melalui Pendekatan Algoritma Machine Learning
DOI:
https://doi.org/10.53696/venn.v5i1.420Keywords:
Algorithm, Machine Learning, Susceptibility, Spatial, LandslidesAbstract
Landslides are among the most frequent natural disasters in Indonesia, posing significant threats to human safety and infrastructure, particularly in regions characterized by steep topography and high rainfall intensity. This study aims to spatially analyze landslide susceptibility using machine learning algorithms and to identify the most influential conditioning factors contributing to landslide occurrences. The research was conducted in Kabupaten Luwu Utara, located in Sulawesi Selatan, utilizing recorded landslide events from 2015–2025. The analysis incorporated biophysical and geospatial variables, including rainfall, humidity, and the Normalized Difference Vegetation Index (NDVI). The study employed a quantitative explanatory approach with predictive modeling based on Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms. The dataset was constructed as a balanced binary classification of landslide and non-landslide events. Model performance was evaluated using multiple metrics, including accuracy, precision, recall, F1-score, ROC-AUC, and precision–recall curves to ensure comprehensive assessment. The findings indicate that the Random Forest model achieved the highest overall accuracy, while the Artificial Neural Network demonstrated superior capability in detecting landslide occurrences within the testing dataset. However, the relatively modest AUC and Average Precision values suggest limitations in the models’ discriminative performance. Variable importance analysis revealed that rainfall is the most dominant conditioning factor, followed by humidity and NDVI. Overall, the machine learning approach successfully represents landslide susceptibility spatially. Nevertheless, further model optimization and integration of additional relevant variables are required to enhance predictive reliability and to support robust disaster mitigation and early warning systems.
Downloads
References
Abdo, H. G., & Richi, S. M. (2024). Journal of King Saud University - Science Application of machine learning in the assessment of landslide susceptibility : A case study of mountainous eastern Mediterranean region , Syria. Journal of King Saud University - Science, 36(5), 103174. https://doi.org/10.1016/j.jksus.2024.103174
Achu, A. L., Thomas, J., Aju, C. D., Vijith, H., & Gopinath, G. (2024). Geomorphology Redefining landslide susceptibility under extreme rainfall events using deep learning. Geomorphology, 448(December 2022), 109033. https://doi.org/10.1016/j.geomorph.2023.109033
Ado, M., Amitab, K., Maji, A. K., Jasińska, E., Gono, R., Leonowicz, Z., & Jasiński, M. (2022). Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey. Remote Sensing, 14(13), 3029. https://doi.org/10.3390/rs14133029
Alfarizi, R., Honesti, L., Driptufany, D. M., & Fikri, S. (2023). Mapping of the landslide area of Tanjung Gadang District, Sijunjung Regency. Journal of Science, 5(1), 123–135.
Badan Nasional Penanggulangan Bencana. (2025). Data bencana tanah longsor. https://gis.bnpb.go.id/databencana/tabel/pencarian.php
Bhattacharjee, S., Kumar, S., Shankar, U., & Mishra, O. P. (2025). Geoscience Frontiers An insight into seismotectonic scenario of the southwestern part of Delhi-NCR and delineation of new faults : Implications to seismic hazard potential. Geoscience Frontiers, 16(2), 101991. https://doi.org/10.1016/j.gsf.2024.101991
Effendi, A. Y., & Hariyanto, T. (2016). Pembuatan Peta Daerah Rawan Bencana Tanah Longsor dengan Menggunakan Metode Fuzzy logic. Jurnal Teknik ITS, 5(2), A714–A722.
Hong, H., Pourghasemi, H. R., & Pourtaghi, Z. S. (2018). Review on landslide susceptibility mapping using support vector machines. Computers & Geosciences. https://doi.org/10.1016/j.catena.2018.03.003
Jia, Z., Cheng, Z., Chang, Z., Li, Q., Huang, F., & Peng, Y. (2025). Jo ur na l P re of. Journal of Rock Mechanics and Geotechnical Engineering. https://doi.org/10.1016/j.jrmge.2025.12.011
Liu, X., Shao, S., & Shao, S. (2024). ScienceDirect Landslide susceptibility prediction and mapping in Loess Plateau based on different machine learning algorithms by hybrid factors screening : Case study of Xunyi County , Shaanxi Province , China. Advances in Space Research, 74(1), 192–210. https://doi.org/10.1016/j.asr.2024.03.074
Lyu, H., Yin, Z., Hicher, P., & Laouafa, F. (2024). Geoscience Frontiers Incorporating mitigation strategies in machine learning for landslide susceptibility prediction. Geoscience Frontiers, 15(5), 101869. https://doi.org/10.1016/j.gsf.2024.101869
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180, 60–91.
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437.
Tanesab, F. I., Dethan, J. J. S., & Selan, W. (2023). Pemetaan daerah rawan longsor di wilayah Kota Kupang berbasis geographic information system (GIS). Journal of Computer Science and Technology, 1(1), 35–40.
Yu, L., Pradhan, B., & Wang, Y. (2025). Geoscience Frontiers A comparative study of various combination strategies for landslide susceptibility mapping considering landslide types. Geoscience Frontiers, 16(2), 101999. https://doi.org/10.1016/j.gsf.2024.101999
Zhao, L., et al. (2023). Evaluating Landslide Susceptibility Using Sampling Methodology and Machine Learning. ISPRS International Journal of Geo-Information, 12(5), 197.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Irmayani Irmayani
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors certify that:
- if the manuscript is co-authored, they are authorized by their co-authors to enter into these arrangements.
- the work described has not been formally published before in a registered ISSN or ISBN media, except in the form of an abstract or as part of a published lecture, review, or thesis.
- it is not under consideration for publication elsewhere,
- its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- they secure the right to reproduce any material that has already been published or copyrighted elsewhere (it does not infringe on the rights of others).
- they agree to the license and copyright agreement.
All articles published are licensed under Creative Commons Attribution 4.0 International License.
- Authors retain copyright and other proprietary rights related to the article.
- Authors retain the right and are permitted to use the substance of the article in their own future works, including lectures and books.
- Authors grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution License (CC BY 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post or self-archive their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
