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All Journal Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) Jurnal Ilmu Pertanian Indonesia 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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Review Manajemen Rantai Pasok Produk Pertanian Berkelanjutan: Konseptual, Isu Terkini, dan Penelitian Mendatang Jaya, Rachman; Yusriana, Yusriana; Fitria, Eka
Jurnal Ilmu Pertanian Indonesia Vol. 26 No. 1 (2021): Jurnal Ilmu Pertanian Indonesia
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18343/jipi.26.1.78

Abstract

Recently, the interest of academic and research institutions in sustainable agric-supply chain management (SASCM) has risen. This fact can be seen from the number of papers published as special issues. Agric-supply chain management is a substance deepening from conventional supply chain management which is discussing integration of economical, environmental, and social aspects to reach a goal of organization. The objective of this research was to describe the state of the art about this topic and future research issues. The number of papers analyzed were 111 articles published from 2003–2020. The articles were obtained from scientific provider such as Science direct, EBSCO, Cross-Reff, Researchgate, DOAJ, Academia.Edu, and Google Scholar. In this research, we cluster (SASCM) to several items such as supply chain management, sustainable supply chain management, and sustainable supply chain management for agricultural product. The content analysis was used to describe the state of the arts and novelty. The result of the study show that it is critical for the actors of agricultural business to apply sustainability concepts including economic, social, environmental, and institution on the systems of agricultural supply chain based on industry 4.0 approach to reach a sustainable business process. Synthesis and determination of main topics of research in the future is undertaken at the end. Keywords: agricultural product, management, sustainable supply chain
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 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 Rachman Jaya Ramadhan, Raisuli Ramadhani, Sendy Pratiwi Sari Chandra, Intan Purnama Sari Hanum, Sari Sari, Dewi Perwito Surya Budi, Brilian Syamsiar, Syamsiar Tati Suryati Yusriana Yusriana, Yusriana