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All Journal Journal of Tropical Soils JTSL (Jurnal Tanah dan Sumberdaya Lahan) Journal of Degraded and Mining Lands Management Agriprima : Journal of Applied Agricultural Sciences Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal HPT (Hama Penyakit Tumbuhan)
Istika Nita
Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Engineering Environmental Science Social Sciences Other

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PELUANG PENGGUNAAN YOLO V3 - DEEP LEARNING UNTUK DETEKSI LONGSOR PADA LEVEL SUB DAERAH ALIRAN SUNGAI Rahmadani, Rizka Nur; Arifin, Syamsul; Nita, Istika; Putra, Aditya Nugraha
JTSL (Jurnal Tanah dan Sumberdaya Lahan) Vol. 13 No. 2 (2026)
Publisher : Departemen Tanah, Fakultas Bio-industri Pertanian dan Kehutanan, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2026.013.2.5

Abstract

In the past two decades, remote sensing-based landslide detection methods have advanced significantly. However, these methods generally remain broad in scope and are not yet capable of identifying landslides as distinct objects with precise locations. This study aims to integrate YOLO v3 into remote sensing applications to enhance the accuracy of landslide detection in sub-watershed (sub-DAS) areas. The study focuses on two sub-watersheds with a history of frequent landslides, namely Kali Konto and Sumber Brantas. Sentinel-2A imagery from 2024 was downloaded for all months and composited to achieve the most stable visualization. Landslide location points were collected through an exploratory approach, incorporating information from the National Disaster Management Agency (BNPB) and local communities. A total of 155 landslide points were recorded in the field and subsequently used as training and validation data. Seventy percent of these points were analyzed using a deep convolutional neural network—YOLO v3—implemented in ArcGIS Pro, while the remaining 30% were used for validation against actual field occurrences.The detection model developed for the Kali Konto and Sumber Brantas sub-watersheds achieved an accuracy of 77%, demonstrating reliable performance in predicting landslide locations. The landslides identified predominantly consisted of slope failures and rockfalls occurring along cut-fill sections of roadside areas. However, this study has certain limitations: the developed model is unable to classify different types of landslides, and the satellite imagery used has a medium resolution, which is not yet advanced enough to fully meet the requirements for sub-watershed-level analyses.
Co-Authors Achmad Bima Fauzi Achmad Wildanul Khakim Adelyanti, Martiana Aditya Nugraha Putra Aditya Nugraha Putra, Aditya Nugraha Aditya Putra Aksha Karunia Satria Albert Fernando Sitorus Alberth Fernando Sitorus Alia Fibrianingtyas Alia Fibrianingtyas Alia Fibrianingtyas Andriyanto, Chandra Anggraini Aurina Putri Anggraini Aurina Putri Awang Satya Kusuma Ayuningtyas, Priska Deffi Armita Dekan Rahmat Wahyudianto Elina Endang Listyarini Fikri Hadi Rusdianto Hana Kusumawati Hanifah Ainur Dienna Hayyuna Khairina Albayani Ivena Hafshah Khairunnisa Kristiawati, Wanda Kurniawan Sigit Wicaksono Kurniawan Sigit Wicaksono Kurniawan Sigit Wicaksono, Kurniawan Sigit Luqmanul Hakim Martiana Adelyanti Melati Julia Rahma Michelle Talisia Sugiarto Michelle Talisia Sugiarto Mochtar Lutfi Rayes Mochtar Luthfi Rayes Mualif Adi Saputra Muhamad Taufiq Hidayat Naafi Tiara Windari Novandi Rizky Prasetya Novandi Rizky Prasetya Nurhutami, Shofie Rindi Prasetyo Adi Nugroho Priska Ayuningtyas Qoid Luqmanul Hakim Rahmadani, Rizka Nur Reni Ustiatik Reza Muhammadi Riza, Sativandi Rony Irawanto Rosy Lesmono Putri S Sudarto Sativandi Riza Sektiono, Antok Wahyu Sena Rizki Triyudanto Sitorus, Albert Fernando Sri Agustiningsih Sri Rahayu Utami Sudarto Sudarto Sudarto Sudarto Sudarto Sugeng Prijono Syamsu Ridzal Indra Hadi Syamsul Arifin Widianto Widianto Zaenal Kusuma Zaenal Kusuma Zaenal Kusuma