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Juprianus Rusman
Program Studi Teknik Informatika, Universitas Kristen Indonesia Toraja, Tana Toraja, Sulawesi Selatan
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Journal : Jurnal Mantik

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Convolutional neural network model for early detection of meatballs containing borax Aryo Michael; Juprianus Rusman
Jurnal Mantik Vol. 7 No. 3 (2023): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i3.4411

Abstract

The use of borax in meatballs to improve the texture and durability of meatballs is still rampant. Borax is very dangerous for consumers. Currently, monitoring of meatballs containing borax is done by experts in the laboratory. The public needs to know this information quickly. Therefore, a system is needed that can detect meatballs containing borax in real time. In this study we built a lightweight Convolutional Neutal Network (CNN) model and searched for optimal hyperparameters for the classification of meatballs containing borax. The results show that the proposed model outperforms other models in classifying meatballs containing borax with an accuracy value of 90%.
Co-Authors Adha, Muhammad Sofwan Amarya Sula Boas Gallaran, Ferayanti Damayanti, Irene Devi Eko Suripto Pasinggi Garonga, Melki Gidion A.N. Pongdatu Haryati, Berlian Zetikarya Irene Devi Damayanti Michael, Aryo Nerlys Amelia Padang, Semuel Yacobus Palelleng, Srivan Pallu, Seltiani Patandung, Suryaningsih Pongdatu, Gideon A.N. Pongdatu, Gidion Aryo Nugraha Romiger Rongko, Yunita Toding Sandri Matarru Srivan Palelleng Upa, Samrius