Klasifikasi Risiko Bencana di Indonesia Menggunakan SVM dan Random Forest
- Erland Adhe Sharendra Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
-
Tri Widodo
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
- Damayanti Damayanti Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
- Okma Arnilia Universitas Islam Negeri Siber Syekh Nurjati, Cirebon, Indonesia (*) Corresponding Author
Abstract
Indonesia is a country with a high level of disaster vulnerability, requiring effective methods to accurately classify disaster risk levels. This study aims to analyze and compare the performance of Support Vector Machine (SVM) and Random Forest algorithms in disaster risk classification. The dataset used consists of disaster event data from 2019–2024, including disaster type, region, number of victims, and population density. Disaster risk levels were classified into three categories, namely low, medium, and high, based on the total impact calculated from the number of victims. The proposed method includes data preprocessing, normalization, and train-test data splitting. The results show that both models achieved high performance, where Random Forest obtained an accuracy of 95.66% and SVM achieved 95.28%, with ROC-AUC values of 0.9823 and 0.9769, respectively. Random Forest demonstrated slightly better performance with an accuracy difference of 0.38% and more consistent prediction results. The high performance indicates that the models were able to recognize the main patterns within the dataset, although the results were also influenced by the characteristics of the data used. Overall, Random Forest is more suitable for disaster risk classification on data with complex characteristics.
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References
United Nations Office for Disaster Risk Reduction (UNDRR), Global Assessment Report on Disaster Risk Reduction 2022: Our World at Risk: Transforming Governance for a Resilient Future. Geneva: United Nations, 2022. Accessed: May 10, 2026. [Online]. Available: https://www.undrr.org/GAR2022
Fitri Adi Setyorini, “Menakar Paradigma Penanggulangan Bencana Melalui Analisis Undang-Undang No. 24 Tahun 2007 Tentang Penanggulangan Bencana,” Journal of Social Politics and Governance (JSPG), vol. 5, no. 2, pp. 97–96, Dec. 2023, doi: 10.24076/jspg.v5i2.1339.
M. Ganjirad and M. R. Delavar, “Flood Risk Mapping Using Random Forest and Support Vector Machine,” in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus Publications, Jan. 2023, pp. 201–208. doi: 10.5194/isprs-annals-X-4-W1-2022-201-2023.
A. Ageenko, L. C. Hansen, K. L. Lyng, L. Bodum, and J. J. Arsanjani, “Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study,” ISPRS Int. J. Geoinf., vol. 11, no. 6, Jun. 2022, doi: 10.3390/ijgi11060324.
A. A. Nurkhaliza and A. W. Wijayanto, “Perbandingan Algoritma Klasifikasi Support Vector Machine dan Random Forest pada Prediksi Status Indeks Mitigasi dan Kesiapsiagaan Bencana (IMKB) Satuan Kerja BPS di Indonesia,” Jurnal Informatika Universitas Pamulang, vol. 7, no. 1, pp. 54–59, 2022, doi: 10.32493/informatika.v7i1.16117.
A. Brijith, “Data Preprocessing for Machine Learning,” CSIM, 2023, Accessed: May 10, 2026
J. S. Aguilar-Ruiz and M. Michalak, “Classification Performance Assessment for Imbalanced Multiclass Data,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-61365-z.
A. Rajab et al., “Flood Forecasting by Using Machine Learning: A Study Leveraging Historic Climatic Records of Bangladesh,” Water (Switzerland), vol. 15, no. 22, Nov. 2023, doi: 10.3390/w15223970.
M. Asif, M. M. Kuglitsch, I. Pelivan, and R. Albano, “Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting,” Water Resources Management, vol. 39, no. 5, pp. 1971–1991, Mar. 2025, doi: 10.1007/s11269-025-04093-x.
A. Faruq and A. Syaiful Amal, “Desain Sistem Prediksi Wilayah Terdampak Banjir dengan Machine Learning berbasis Data Sistem Informasi Geografis,” Seminar Keinsinyuran, p. 1, 2024, Accessed: May 10, 2026. [Online]. Available: https://research-report.umm.ac.id/index.php/psppi/article/view/362
C. Zeng and D. Bertsimas, “Global Flood Prediction: a Multimodal Machine Learning Approach,” ArXiv, vol. 2301.12548, Jan. 2023, [Online]. Available: http://arxiv.org/abs/2301.12548
E. Hermawan, S. Darmawan Panjaitan, and E. Faja Ripanti, “Sistem Prediksi Banjir Rob Kota Pontianak Berbasis Machine Learning Menggunakan Framework,” JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 10, no. 3, pp. 351–361, Dec. 2024, Accessed: May 10, 2026. [Online]. Available: https://jurnal.untan.ac.id/index.php/jepin/article/view/79955?
Parag Ghorpade, Hitesh Chordiya, Basavaraj Hooli, Aditya Gadge, Gita Gosavi, and Yashwant S. Ingle, “Flood Forecasting Using Machine Learning: A Review,” 2021 8th International Conference on Smart Computing and Communications (ICSCC), 2021, doi: 10.1109/ICSCC51209.2021.9528099.
S. Triyanto, A. Sunyoto, and M. R. Arief, “Analisis Klasifikasi Bencana Banjir Berdasarkan Curah Hujan Menggunakan Algoritma Naïve Bayes,” JOISIE Journal Of Information System And Informatics Engineering, vol. 5, no. 2, pp. 109–117, 2021, Accessed: May 10, 2026. [Online]. Available: https://jurnalamikom.ac.id/index.php/joisie/article/view/687
S. E. Purwati and Y. Pristyanto, “Model Random Forest and Support Vector Machine for Flood Classification in Indonesia,” sinkron, vol. 8, no. 4, pp. 2261–2268, Oct. 2024, doi: 10.33395/sinkron.v8i4.13973.
M. Sinsirimongkhon, S. Arwatchananukul, and P. Temdee, “Multi-Class Classification Method with Feature Engineering for Predicting Hypertension with Diabetes,” Journal of Mobile Multimedia, vol. 19, no. 3, pp. 799–822, 2023, doi: 10.13052/jmm1550-4646.1937.
M. Fauzi, Muhsi, and Hozairi, “Analisis Penggunaan Model Random Forest dalam Memprediksi Resiko Banjir di Daerah Rawan Bencana Kabupaten Pamekasan,” Seminar Nasional Humaniora dan Aplikasi Teknologi Informasi (SEHATI), vol. 11, no. 1, pp. 17–25, Oct. 2025, Accessed: May 10, 2026. [Online]. Available: https://ejournalwiraraja.com/index.php/SEHATI/article/view/4669
M. Mar, W. Aprizal Arifin, and A. Armelita Rosalia, “Perbandingan Random Forest Dan Support Vector Machine Dalam Memprediksi Banjir Rob di Teluk Lampung,” Jurnal Algoritma, vol. 22, no. 2, pp. 1116–1126, 2025, doi: 10.33364/algoritma/v.22-1.2145.
R. Santosa, A. Fariza, and F. Arifin, “Classification of Flood Disaster Level News Articles Using Machine Learning,” Indonesian Journal of Computer Science Attribution, vol. 13, no. 1, pp. 264–275, 2024, Accessed: May 10, 2026. [Online]. Available: https://ijcs.stmikindonesia.ac.id/ijcs/index.php/ijcs/article/view/3550
R. Hermawan, A. Maulana, and A. Zulianto, “Penyusunan Model Untuk Predkisi Bencana Banjir Menggunakan Machine Learning,” Jurnal InfoSecure (JISEC), vol. 2, no. 1, pp. 1–10, Apr. 2021, Accessed: May 10, 2026. [Online]. Available: https://jurnal-pasca.unla.ac.id/infosecure/article/view/v2n1_01
I. A. Purnomo, J. Indra, E. E. Awal, and T. Rohana, “Analisis Prediksi Banjir di Indonesia Menggunakan Algoritma Support Vector Machine dan Random Forest,” Journal of Information System Research (JOSH), vol. 6, no. 1, pp. 219–228, Oct. 2024, doi: 10.47065/josh.v6i1.5958.
T. Prasetyo and Ardianto, “Analisis Prediksi Risiko Banjir Menggunakan Algoritma Random Forest,” Jurnal Teknik Informatika Unika ST. Thomas (JTIUST), vol. 10, no. 02, pp. 314–319, 2025, Accessed: May 10, 2026. [Online]. Available: https://ejournal.ust.ac.id/index.php/JTIUST/article/view/5944
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