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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan Panrita Abdi - Jurnal Pengabdian pada Masyarakat JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) Jurnal Ilmiah FIFO Equilibrium: Jurnal Ilmiah Ekonomi, Manajemen dan Akuntansi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) JOURNAL OF SCIENCE AND APPLIED ENGINEERING Applied Technology and Computing Science Journal Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS Systematics Buana Information Technology and Computer Sciences (BIT and CS) Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pengabdian Masyarakat Multidisiplin Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Conference on Industrial Revolution for Polytechnic Education International Journal of Community Service (IJCS) Prosidia Widya Saintek Jurnal Abdimas Mahakam
Aviv Yuniar Rahman
Universitas Widyagama Malang
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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 Abdurahman, Titik Khawa Adi Saputra, Deni Adya Hermawati Affi Nizar Suksmawati Ahmad Farhan, Ahmad Ahmad Fauzi Anderias Bai Seran April Hananto April Lia Hananto arrozi, lalu muhamad alawi Bagus Setyawan Budiawan, Renaldi Widi Chauliah Fatma Putri Da Costa, Zania Abuk Daiman, Claudia Nila Dio Amin Putra Eko Sediyono Fauzi Ahmad Muda Firman Nurdiansyah Firman Nurdiyansyah Firman Nurdiyansyah, Firman Hananto, Agustia Hananto, Agustia Tia Hendry Hero Diogenes Adoe Huda, Baenil Ilhamsyah Ilhamsyah Indra, Jamaludin Istiadi Istiadi Istiadi Joko Purwanto khusniyatul latifah Kristianingrum Kristianingrum, Kristianingrum Luruk, Maria Ovalia Ma'rifani Fitri Arisa Mamba’us Sa’adah Maria Alfanita Bhia, Meisya Miskon, Muhammad Fahmi Muhammad Romadloni Putra Muslim, Shinta Nilam Sari Napulun, Kanisius Nasharuddin Mas Neno, Adi nisti, Melita Novalia, Elfina Nudiyansyah, Firman Nusman, Bayu Pahlevi Putra, Rangga Paryono, Tukino Priyandoko Gigih Priyatna, Bayu Putera, Rangga Pahlevi Putra, Rangga Pahlevi Putra, S.Pd., M.T., Rangga Pahlevi Putra Ratna Hidayati Rudy Joegijantoro, Rudy Rukmanta Jayawiguna Sabar Setiawidayat Setiawan, Feddy Wanditya Sidi, Husri Suhada, Karya Suksmawati, Affi Nizar Syah, Adryan Syahroni Wahyu Iriananda, Syahroni Wahyu Ursaputra Pratama, Lionardi Wisnu, Alif Dio Raka Yuliana Rachmawati