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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal CoreIT JIEET (Journal of Information Engineering and Educational Technology) Sebatik Building of Informatics, Technology and Science Jurnal Informatika Ekonomi Bisnis JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Information System Journal (INFOS) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Bulletin of Information Technology (BIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika Ekonomi Bisnis
Suwanto Sanjaya
UIN Suska Riau
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering

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Journal : Bulletin of Computer Science Research

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Penggunaan Convolutional Neural Network NASNetLarge Dalam Klasifikasi Citra Daging Babi dan Sapi Aqilah, M Alfandri; Jasril; Sanjaya, Suwanto; Insani, Fitri
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.666

Abstract

The adulteration of beef with pork is a serious issue in Indonesia, particularly for Muslim consumers who are required to consume halal products. According to a Kompas (2020) report, a case of meat adulteration involving 100 kilograms of mixed meat sold as beef was discovered in Tangerang City. This practice not only violates religious laws but also poses threats to public health and consumer trust. To address this challenge, this study adopts a deep learning approach using NASNetLarge for the classification of pork, beef, and mixed meat images. Unlike previous research that utilized EfficientNet-B2 and achieved an accuracy of 98.23%, this study’s NASNetLarge approach produced a comparably competitive accuracy of 98.03%. The dataset used consists of 1,932 images sourced from the Kaggle platform, which were processed through preprocessing and augmentation stages. The data were then split into two distribution scenarios: the entire dataset and a balanced class dataset with 90:10 and 80:20 ratios. Evaluation results show that the best parameter combination was achieved in the first scenario with a 90:10 ratio using augmented images, a learning rate of 0.001, 128 dense units, and the Adam optimizer. The model achieved the highest accuracy of 98.03%, with a precision of 98.63%, recall of 98.40%, and an F1-score of 98.50%. These results indicate that NASNetLarge is effective in accurately and consistently classifying meat images. Image augmentation significantly improved model performance, and the 90:10 data ratio yielded more optimal results compared to 80:20. These findings have the potential to support food surveillance efforts by enabling rapid and accurate detection of meat adulteration.
Co-Authors Agustian, Surya Ahmad Fauzan Ahmad, Rizmah Zakiah Nur Al Fiqri, M. Faiz Aqilah, M Alfandri Aulia Ramadhani Eka Pandu Cynthia, Eka Pandu Elin Haerani Elvia Budianita Ersad Alfarsy Absar, Ersad Alfarsy Fadhilah Syafria Febi Yanto, Febi Fitri Insani Fitri, Dina Deswara Gusti, Siska Kurnia Harni, Yulia Iis Afrianty Israldi, Tino Iwan Iskandar Jasril Jasril Jasril Jasril Kurnia Rahman, Fikri Kurniawan, Saifur Yusuf Lestari Handayani Masaugi, Fathan Fanrita Mazdavilaya, T Kaisyarendika Muhammad Fikry Muhammad Irsyad Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Negara, Benny Sukma Pangestu, Yoga Pizaini Pizaini, Pizaini Radili, Adi Rahmad Abdillah Saputra, Nugroho Wahyu Shahira, Fayza Sugandi, Hatami Karsa Syaputra, Muhammad Dwiky Vitriani, Yelfi Yani, Susmi Syahfrida Yusra, Yusra