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All Journal Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) Media Penelitian dan Pengembangan Kesehatan Majalah Kesehatan FKUB Midwifery Journal: Jurnal Kebidanan UM. Mataram Media Mahardhika Jurnal Teknik Informatika (JUTIF) Farmasis: Jurnal Sains Farmasi Jurnal Sains, Teknologi dan Kesehatan Journal of Innovation in Teaching and Instructional Media Journal of Social Work and Science Education Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal)
Eka Fitria
Loka Litbang Biomedis Aceh
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Detection of Endangered Indonesian Species Across Multiple Taxonomic Classes Using Faster R-CNN Mubarok, Moh. Jabir; Fitria Haya, Rizky; Fitria, Eka; Surya Budi, Brilian
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4793

Abstract

Indonesia’s rich biodiversity includes many endangered species across various taxonomic groups. This study presents a Faster R-CNN deep learning model to detect ten endangered Indonesian species, covering birds, reptiles, mammals, and fishes. A custom dataset with diverse images was annotated and used to train the model with transfer learning on the Detectron2 framework. Evaluation using COCO metrics yielded an average precision (AP) of 54.93%, with the Komodo Dragon achieving the highest AP (82.57%) and Wallace’s Standardwing the lowest (30.82%). The model excels at detecting larger, distinct species but has difficulty with smaller or camouflaged ones in complex environments. Training results confirm that transfer learning aids performance despite limited data. Analysis of misclassifications suggests the need for additional data modalities or context to improve accuracy. This work highlights the potential of Faster R-CNN for automated endangered species monitoring in Indonesia and recommends dataset expansion, data augmentation, and model refinement to enhance detection, particularly for challenging species. This study contributes to computer vision applications in conservation, particularly within low-resource biodiversity contexts.
Co-Authors Abidah Nur Anada, Dhea Rizma Asis Cahyany, Renilda Dewi Perwito Sari Fahmi Ichwansyah Faisal, Debri Rizki Fardani, Roushandy Asri Fitria Haya, Rizky Happy Fitria, Happy Kesumawaty, Elly Mentari, Ika Nurfajri Mubarok, Moh. Jabir Mukti, Asri Wido Mulyadi Mulyadi Nelly Marissa Nur Ramadhan, Nur Nurul Hidayat Onetusfifsi Putra Ramadhan, Raisuli Ramadhani, Sendy Pratiwi Sari Chandra, Intan Purnama Sari Hanum, Sari Surya Budi, Brilian Syamsiar, Syamsiar Tati Suryati