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All Journal Jurnal Informatika dan Teknik Elektro Terapan JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science (JOINTECS) Syntax Literate: Jurnal Ilmiah Indonesia JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Conference on Innovation and Application of Science and Technology (CIASTECH) J-SAKTI (Jurnal Sains Komputer dan Informatika) JURTEKSI Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Ilmu Komputer dan Bisnis Jurnal Teknologi Informasi dan Multimedia Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis Buana Ilmu Buana Information Technology and Computer Sciences (BIT and CS) Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) International Journal of Educational Review Journal of Applied Data Sciences Jurnal Cahaya Mandalika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Djtechno: Jurnal Teknologi Informasi KLIK: Kajian Ilmiah Informatika dan Komputer Instal : Jurnal Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mandiri IT Jurnal Minfo Polgan (JMP) Abdimas Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Sistem Informasi STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Innovative: Journal Of Social Science Research Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Jurnal Ilmiah Pengabdian Kepada Masyarakat (Nyiur-Dimas) Informasi interaktif : jurnal informatika dan teknologi informasi Jurnal PETISI (Pendidikan Teknologi Informasi) Journal of Information Technology
Priyatna, Bayu
Universitas Buana Perjuangan Karawang
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

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Journal : Journal of Applied Data Sciences

 1 
Classification of Starling Images Using a Bayesian Network Hananto, April Lia; Rahman, Aviv Yuniar; Paryono, Tukino; Priyatna, Bayu; Hananto, Agustia; Huda, Baenil
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.423

Abstract

The classification of starling species is vital for biodiversity conservation, especially as some species are endangered. This research investigates the effectiveness of the Bayesian Network (BayesNet) for classifying starling species and compares its performance with Artificial Neural Networks (ANN) and Naive Bayes models. The dataset comprises 300 images of five starling species—Bali, Rio, Moon, Kebo, and Uret—captured under controlled conditions. Feature extraction focused on color, texture, and shape, while data augmentation through slight image rotations was applied to enhance model generalization. The BayesNet model achieved an accuracy of 96.29% using a 90:10 training-to-testing split, outperforming ANN (90.74%) and Naive Bayes variants. Precision, recall, F1-score, and AUC-ROC values further validated the robustness of the BayesNet model, with precision at 0.90, recall at 0.91, F1-score at 0.92, and AUC-ROC at 0.95. These results demonstrate the superior performance of multi-feature Bayesian Networks in starling classification compared to other machine learning models. The novelty of this study lies in its application of a probabilistic approach using Bayesian Networks, which enhances interpretability and performance, especially in scenarios with limited data. Future work may explore additional feature sets and advanced machine learning models to further improve classification accuracy and robustness.
Improved Hybrid GoogLeNet-Based Deep Learning Optimization for Standardized Straw Mushroom Quality Classification in Indonesia Priyatna, Bayu; Abdurahman, Titik Khawa; Miskon, Muhammad Fahmi; Hananto, April Lia; Hananto, Agustia Tia; Rahman, Aviv Yuniar
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1206

Abstract

Deep learning plays a crucial role in modern computer vision due to its ability to automatically extract hierarchical features from large-scale image data. Among various architectures, Convolutional Neural Networks (CNNs) have been extensively utilized for image pattern interpretation, including in agricultural product inspection. Straw mushrooms (Volvariella volvacea) are important agro-industrial commodities in Indonesia; however, their quality assessment still relies on subjective manual evaluation based on the Indonesian National Standard (SNI:01-6945-2003), leading to inconsistency in grading results. To address this limitation, this research proposes an Improved Hybrid GoogLeNet model integrated with a YOLO-based detection framework and hybrid preprocessing to enhance feature clarity and classification robustness. The system is capable of conducting object detection, 3-class morphological quality classification (Pure White, Oval, and Black Spot/Defect), and automatic diameter measurement using calibrated pixel-to-centimeter conversion. Performance evaluation is carried out by benchmarking the proposed model against several popular deep learning architectures including YOLOv5, LeNet, AlexNet, VGGNet, and ResNet. Experimental results demonstrate that the Improved Hybrid GoogLeNet achieves the highest performance with precision of 97.99%, recall of 96.07%, and F1-score of 96.98%, along with low misclassification rates across all classes. These results indicate that the proposed method provides accurate, reliable, and efficient quality assessment that supports standardized automated grading in industrial applications. Therefore, this study contributes to the advancement of intelligent computer vision solutions for digital transformation in the Indonesian mushroom agro-industry.
Co-Authors AA Sudharmawan, AA Aang Alim Murtopo Abdullah Abdullah Abdurahman, Titik Khawa Adittia Agustian Afra, Alfina Fadhilah Ahnaf, Naufal Zubdi alzahra, alika aziza Andini, Vina Angraeni, Rahmah Nur April Lia Hananto Aprilia Putri Nardilasari Arifin, Jihan Salsabila Arip Solehudin Asep Haris Aulia, Aldi Aviv Yuniar Rahman Baenil Huda Desfianthy, Fatiya Hanifah Diningrat, Cahya Fadillah, Arya Fatlun, Aulia Fitria Nurapriani Florida Ivonia Manek Guntur, Muhamad Haerudin Haerudin Hananto, Agustia Hananto, Agustia Tia Handayani, Citra Heryana, Nono Hilabi, Shofa Shofia Hilabi, Shofa Shofia Hilabi, Shofa Shofiah Hoerudin Hoerudin Huda, Baenil Intan, Melani Ismail, Dennisa Avriel Jabar Sanjaya Julia, Ina Julifer P Manurung Juniardi Karina Kurniawan, Muhamad Dicky Lutfiah, Siti Miskon, Muhammad Fahmi Mohamad Ricky Firdaus Muhammad Nova Novalia, Elfina Nur ‘Azah Nurapriani, Fitria Paryono, Tukino Pradana Rizki Maulana Ramadanti, Anita Khansa Reswara, Hadaya Abhista Rini Mayasari Sabrina Amanda Salsabila Saepul Aripiyanto Sari, Fitria Ratna Setiawan, Revi Shofa Shofia Hilabi Shofa Shofia Hilabi Shofa Shofiah Hilabi Shofia Hilabi, Shofa Sifa, Sifa Rismawati Siregar, Amril Mutoi Siti Masruroh Siti Masruroh Sutan Faisal Syahri Susanto Tatang Rohana Teresa Abuya Topan Setiawan Topan Trianto Tukino Tukino, Tukino tukino, tukino Tukino, Tukino Wahyu Aziz Ramadhani Yazid, Muhammad Abi Zahra, Nabila Zein, Selmia Aulia Zhalifunas, Satria Dawas