PEMANTAUAN MUSANG PANDAN MENGGUNAKAN METODE COMPUTER VISION DENGAN MODEL YOLOV11
DOI:
https://doi.org/10.23960/jitet.v13i2.6251Abstract Views: 259 File Views: 246
Abstract
The Asian palm civet (Paradoxurus hermaphroditus) plays a crucial role in the ecosystem as a seed disperser and has economic value in the civet coffee industry. However, monitoring civet activity in captivity is still conducted manually, which is time-consuming and prone to human error. This study aims to develop an automatic monitoring system based on Computer Vision using the YOLOv8 method with the Python programming language. The system can detect civet activities such as eating, drinking, moving, and sleeping through surveillance cameras in real time. The model was trained using a specialized dataset collected from civet enclosures under various lighting conditions and camera angles. Evaluation results show that the model achieved a mean Average Precision (mAP) of 99.5%, precision of 100%, and recall of 99.3%, indicating excellent detection capability. The implementation of this system is expected to assist captive management in monitoring efficiently, reducing reliance on manual supervision, and improving animal welfare through more accurate observation. Furthermore, this system has the potential to be further developed for real-time video-based monitoring and applied to other animal species. Thus, this study not only contributes to the efficiency of Asian palm civet monitoring but also opens opportunities for the application of Computer Vision technology in conservation and wildlife-based industries.Downloads
References
S. S. M. Marpaung et al., “Analisis Perkembangan Pengenalan Satwa Prioritas Indonesia Pada Pendidikan Anak dengan Vosviewer,” Jurnal Ilmiah Profesi Pendidikan, vol. 8, no. 2, pp. 1257–1263, 2023.
S. S. M. Marpaung, B. Masy’ud, and T. Sunarminto, “Manfaat sosial ekonomi penangkaran rusa sambar (Rusa unicolor) di KHDTK Aek Nauli, Sumatera Utara,” Jurnal Agribisnis, vol. 11, no. 1, pp. 31–41, 2022.
S. S. M. Marpaung, B. Masy’ud, D. H. Has, N. A. Paputungan, and I. M. A.-S. Siregar, “The Analysis of sustainability factors underlying human-monkeys (Macaca fascicularis) conflict at IPB University,” Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), vol. 13, no. 4, pp. 505–596, 2023.
R. A. Pradipta, P. B. Wintoro, and D. Budiyanto, “Perancangan Pemodelan Basis Data Sistem Informasi Secara Konseptual Dan Logikal,” Jurnal Informatika Dan Teknik Elektro Terapan, vol. 10, no. 2, 2022.
D. H. Has, S. S. M. Marpaung, and R. Sari, “Pelatihan Pengelolaan Penangkaran Rusa Sambar (Rusa unicolor) Pada Masyarakat di KHDTK Aek Nauli, Sumatera Utara,” SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan, vol. 7, no. 2, pp. 923–930, 2023.
S. S. M. Marpaung et al., “Konservasi Hutan dan Ekowisata,” Tangguh Denara Jaya Publisher, 2024.
H. Herdianto, I. Sulistianingsih, and I. F. Rahmad, “IMPROVING THE ACCURACY OF SMALL OBJECT DETECTION ON YOLO BY INCREASING THE NUMBER OF INPUT GRIDS,” PROSIDING UNIVERSITAS DHARMAWANGSA, vol. 4, no. 1, pp. 324–332, 2024.
M. Tscharke and T. M. Banhazi, “A brief review of the application of machine vision in livestock behaviour analysis,” Agrárinformatika/Journal of Agricultural Informatics, vol. 7, no. 1, pp. 23–42, 2016.
C. K. Sastradipraja, “Sistem Pemantauan Kesehatan Lobster (Lhms) Menggunakan Machine Learning,” Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI), vol. 2, no. 1, pp. 1–9, 2020.
H. Herdianto, H. Hafni, D. Nasution, and S. Ramadhan, “Implementasi Metode Yolo pada Deteksi Objek Manusia,” METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi, vol. 8, no. 2, pp. 234–240, 2024.
A. P. U. Siahaan, “Fingerprint Pattern Recoqnition Using LVQ,” 2017.
E. Ligtermoet, C. E. Ramalho, J. Foellmer, and N. Pauli, “Greening urban road verges highlights diverse views of multiple stakeholders on ecosystem service provision, challenges and preferred form,” Urban For Urban Green, vol. 74, p. 127625, 2022.
C. Mou, A. Liang, C. Hu, F. Meng, B. Han, and F. Xu, “Monitoring endangered and rare wildlife in the field: A foundation deep learning model integrating human knowledge for incremental recognition with few data and low cost,” Animals, vol. 13, no. 20, p. 3168, 2023.
A. Gat, H. Gaikwad, R. Giri, and A. Chaudhari, “Animal Classifier System for Video Surveillance and Forest Monitoring Using Raspberry-pi”.
