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All Journal Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Intek Akuakultur Jurnal Ilmiah Samudra Akuatika Jurnal Penelitian Agrisamudra Journal of Computer Networks, Architecture and High Performance Computing Global Science Society GABBAH : Jurnal Pertanian Dan Perternakan
Hanisah
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Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Education Energy Engineering Environmental Science Public Health Social Sciences Other

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Alternative Feed with Fermented Kayambang to Reduce Cholesterol Levels and Increase the Antioxidant Content of Duck Eggs and Meat in Batee Puteh Village, Langsa, Aceh: Pakan Alternatif dengan Kayambang Fermentasi Sebagai Penurun Kadar Kolesterol dan Peningkat Kandungan Antioksidan Telur dan Daging Itik di Desa Batee Puteh, Langsa, Aceh Hanisah; Nurjannah; Mentari Darma Putri; Basriwijaya, Kiagus Muhammad Zain
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 6 (2023): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v7i6.15925

Abstract

Hasil survei awal yang dilakukan oleh tim pengabdian, permasalahan yang dihadapi oleh mitra peternak itik di Desa Batee Puteh, Langsa Lama, Kota Langsa, Aceh yaitu: 1) Kurangnya pemahaman mitra tentang budidaya ternak itik yang dapat meningkatkan produktivitas dan kualitas telur serta daging itik; 2) Pengeluaran untuk pakan ternak yang terbilang cukup mahal sehingga diperlukan alternatif pakan dari bahan alami. Kayambang dapat dimanfaatkan untuk bahan membuat pakan ternak yang kaya nutrisi. Solusi yang ditawarkan oleh tim pengabdian berupa: 1) Penyuluhan mengenai cara meningkatkan produktivitas dan kualitas telur serta daging itik; 2) Pelatihan dan pendampingan mitra dalam pembuatan pakan dari fermentasi kayambang. Hasil dari kegiatan yaitu mitra mengalami peningkatan pengetahuan dan keterampilan dalam membuat pakan dari kayambang sebesar 39,3 %; tingkat kepuasan peserta terhadap kegiatan PKM yaitu sebesar 47,35 % untuk kategori sangat setuju dan 52,65 % lainnya setuju dan berharap keberlanjutan program untuk kegiatan pemberdayaan lainnya.
Deep Learning-Based Multi-Tooth Segmentation on Panoramic Radiographs Using YOLOv8 Architecture Ridwan, Ahmad; Pramawahyudi; Purnama Sari, Desy; Hanisah; Musunuri, Yogendra Rao
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7710

Abstract

This research introduces a multi-class tooth-level segmentation framework on panoramic radiographs using YOLOv8, trained on clinically annotated Indonesian dental data. A dataset of 302 annotated panoramic radiographs from patients at Universitas Andalas Dental Hospital was utilized, with each tooth precisely labeled according to international dental nomenclature. The model was trained using transfer learning with the YOLOv8 variant, optimized with the Adam algorithm, and evaluated using precision, recall, F1-score, and Intersection over Union (IoU). The results demonstrate that YOLOv8 is not only effective for lesion detection but also robust for fine-grained anatomical dental segmentation. The performance achieved 93.72% accuracy, 92.67% precision, 98.88% recall, and 95.58% F1-score, indicating high accuracy in tooth detection and boundary delineation. Qualitative analysis confirmed accurate segmentation across a wide range of anatomical variations, including crowding, impaction, and prosthetics. This research establishes YOLOv8 as a highly effective tool for dental image segmentation, offering significant potential to improve diagnostic efficiency, support odontological forensics, and enable automated patient record management. Future work will focus on integrating multi-class pathology detection and 3D reconstruction.
Co-Authors A.P., Ginting Abda Abda Agus Putra AS Ahmad Ridwan Basriwijaya, Kiagus Muhammad Zain Bs, Risma Anggraini Desy Pratiwi Fiddini Alham gustiana, cut M Sareza Mentari Darma Putri Muhammad Fauzan Isma Muhammad Syahril Musunuri, Yogendra Rao Mutia Nurjannah Nurul Husna Pramawahyudi Purnama Sari, Desy Putri Ansari Rozaliana Rozalina, Rozalina Rozalina Saragih, M.Sc, Faoeza Hafiz SIMANGUNSONG, ROKI Siregar, Dolly Sojuangan