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Damayanti, Damayanti
Universitas Teknokrat Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : building of informatics technology and science

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Klasifikasi Risiko Bencana di Indonesia Menggunakan SVM dan Random Forest Erland Adhe Sharendra; Tri Widodo; Damayanti Damayanti; Okma Arnilia
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9818

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.
Co-Authors ., Rusliyawati aan setiawan Ade Dwi Putra Ade Dwi Putra Ade Putra Adi Sucipto, Adi Ady Candra Nugroho Agung Tri Prastowo Ahmad Ari Aldino Aldi Bagus Prasetyo Alfiah Alfiah Algho Niyu Behainksa Amdi Rizal Ana Loria Andi Nurkholis Andre Wijaya Anton Mahendra Anton Mahendra Arfinia Rahma Ari Sulistyawati Arief Budiman Ayesha Ziky Iftikhor Ayu Amalia Ayu Febriani Bahtiar Ayu Febriyani Bachtiar Ayu Ningtiara Berlintina Permatasari Chris Mario Clarisa Armanda Fernandis Creani Handayani Dani Saputra Dedi Darwis Desi Oktarina Devita Tri Yulianti Dian Novita Dian Widiarti Dimas Elang Setyoko Donaya Pasha Donaya Pasha Dyah Ayu Megawaty Erland Adhe Sharendra Erliyan Redi Susanto Erliyan Redi Susanto Erliyan Redy Susanto Fitrah Amalia Fitrah Amalia Rahmadianti Habib Rizky A.Siregar Heni Sulistiani Ida Nurwanti Imam Ahmad Joni Alfian Jupriyadi, Jupriyadi Leny Meilisa M Ghufroni An'ars M Ghufroni An’ars M. Ghufroni An'ars M. Ghufroni An'ars M. Ghufroni An’ars M. Tova M.Ghufroni An'ars Maulana Aziz Assuja Miswanto Miswanto Mohamad Ghazi Rio Muhammad Al Kahfi Masputra Muhammad Alfarizi Muhammad Bakri Muhammad Fitratullah Muhammad Yusuf Hernandez Nadia Aftirah Nirwana Hendrastuty Novia Agus Mila Okma Arnilia Pahlewi, Anita Diah Pangestu Agung Prabowo Prabowo Priliyani Hana Primawan Adrian Sitinja Putri Lestari Rifki Yudiantara Rifli Yanto Rohmat Indra Borman Rubiyah Rubiyah S. Samsugi Samuel Hutagalung Sefrizal Nata Prawira Setiawansyah Setiawansyah suaidah suaidah Tiara Azizul Andika Tri Widodo Tri Widodo Wahyudi, Agung Deni Warnars, Harco Leslie Hendric Spits Wisnu Mukti Dwiyudho Putro Wiwid Ayu Nisa Wulandia Wulandia Lestari Yeni Anggraini Yohanes Aji Pratama Yuri Rahmanto Zakaria Sani Assubhi Zurfah Muzrifah