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All Journal SISFOTENIKA Indonesian Journal on Software Engineering (IJSE) Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Informasi dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Infortech Jurnal Sistem Informasi SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK) Jurnal Pengabdian Masyarakat Bangsa Jurnal Pariwisata, Bisnis Digital dan Manajemen Journal Medical Informatics Technology International Journal of Computer Technology and Science
Daniati Uki Eka Saputri
Universitas Nusa Mandiri
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : International Journal of Computer Technology and Science

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Multiclass Meat Classification Using a Hybrid Machine Learning Approach Taopik Hidayat; Daniati Uki Eka Saputri; Faruq Aziz; Nurul Khasanah
International Journal of Computer Technology and Science Vol. 2 No. 2 (2025): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v2i2.238

Abstract

Image classification is a key field in digital image processing with broad applications, such as object recognition and disease detection. The use of artificial neural network architectures, such as MobileNetV2, has significantly advanced pattern recognition in large datasets. However, in small datasets, challenges related to accuracy and generalization are often encountered. This study explores an RGB-based approach utilizing MobileNetV2 for image feature extraction and Support Vector Machine (SVM) as the classifier. MobileNetV2 is applied to extract features from RGB images, which are then further processed by SVM to determine image classes. The results indicate that this model achieves an accuracy of 91.67%, precision of 0.9163, recall of 0.9167, and F1-score of 0.9161. Based on the confusion matrix analysis, the model effectively distinguishes between classes, despite slight overlaps. This research contributes to the development of intelligent image classification systems that can be applied in various fields, including the food industry. With these achievements, the RGB approach integrating MobileNetV2 and SVM has proven effective in enhancing image classification accuracy, even with relatively small datasets. These findings open opportunities for applying similar methods in other image processing tasks that require high accuracy in object or disease detection and classification.
Co-Authors Abdul Latif Achmad Baroqah Pohan Ade Suryadi Agus Salim Annida Purnamawati Aziz, Faruq Cahyanti, F. Lia Dwi Dwi, F Lia Dwiza Riana Eko Arif Riyanto, Eko Arif Faruq Aziz Faruq Aziz Firasari, Elly Fitra Septia Nugraha Gata, Windu Hasanah, Riyan Latifahul Juninisvianty, Tri Kekal, Harming Puja Khultsum, Umi Lili Novita Monikka Nur Winnarto Nugraha, Fitra Septia Nurfalah, Ridan Nurul Khasanah Nurul Khasanah Pratiwi, Risca Lusiana Rangga Pebrianto Risca Lusiana Pratiwi - Universitas Nusa Mandiri Rosiyadi, Didi Saelan, M. Rangga Ramadhan Salsabila, Unik Hanifah Sardiarinto Sardiarinto Seimahuira, Syarah Septian, Rendi Siti Masturoh Siti Masturoh siti nurlela Solly Aryza Sri Hadianti Taopik Hidayat Umi Khultsum Veithzal Rivai Zainal YANTO YANTO