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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Dinamika Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Format : Jurnal Imiah Teknik Informatika Jurnal Pengabdian UntukMu NegeRI RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Dinamisia: Jurnal Pengabdian Kepada Masyarakat JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Electronics and Communications Systems Jurnal Dinamika Informatika (JDI) Journal of Software Engineering and Information System (SEIS) Jurnal Pengabdian Kepada Masyarakat Ekonomi, Pendidikan, dan Sosial Humaniora JIM ( Journal International Multidiciplinary)
Unik, Mitra
Universitas Muhammadiyah Riau
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Application of Random Forest Algorithm to Analyze the Confidence Level of Forest Fire Hotspots in Riau Peatland Unik, Mitra; Sukaesih Sitanggang, Imas; Syaufina, Lailan; Surati Jaya, I Nengah
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 15 No 2 (2025): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.15.2.255

Abstract

Forest fires pose a significant challenge in Riau Province, Indonesia, especially in peatland areas. This study employs the Random Forest (RF) algorithm to analyze the confidence levels of hotspots, aiming to predict potential fire occurrences and improve fire management strategies. The research focuses on peatlands spanning 3.86 million ha, using key variables such as NDVI, surface temperature, and peat thickness derived from satellite data. The model achieved an average AUC of 0.732 and a classification accuracy of 70.3%, with medium-confidence hotspots demonstrating the best predictive performance (AUC: 0.707, F1-score: 0.804). However, the model struggled with low-confidence hotspots, reflecting challenges in distinguishing less prominent patterns in the data. Compared to other methods, RF demonstrates strong potential in handling complex environmental datasets, making it a valuable tool for hotspot prediction. This study contributes to understanding forest fire risks in peatlands and provides actionable insights for improving preparedness and mitigation efforts.
Co-Authors Aminuyati Apriansyah Apriansyah Azmi, Zul Baidarus Baskoro, Dhanu Baskoro, Dhanu Deden Girmansyah Desti Mualfah Dhanu Baskoro Diah Angraina Fitri Diah Angraini Putri Dicko Andrean Evans Fuad Evi Marlina, Evi Febby Apri Wenando Hafid, Afdhil Harun Mukhtar Hasanuddin Hasanuddin Hasanuddin Hasanuddin, Hasanuddin, Hayami, Regiolina I Nengah Surati Jaya ijra, Zul Ijra Saryendy Imas Sukaesih Sitanggang Januar Al Amien Januar Al Amien Januar Al Amin Jum’atul Zikri Lailan Syaufina Medikawati, Reny Muhammad Tantaya Nurvelly Rosanti R.Septian Armel Rahmad Firdaus Randra Aguslan Pratama Resiasa, Maskuri Riki Harianto Rizki, Yoze Royan Choiro Yahya Sandy Eka Nuggroho Sarmila, Wingki siti rodiah Soni Suci, Rama Gita Suryanto Suryanto Vicky Gema Larenda Yoze Rizki Yoze Rizki Yulia Fatma