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All Journal SEINASI-KESI Journal of Information Systems and Informatics Klik - Jurnal Ilmu Komputer
Yudha Pratomo
Universitas Sriwijaya
Computer Science & IT Engineering

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Journal : Journal of Information Systems and Informatics

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Performance Analysis of Convolutional Neural Network in Pempek Food Image Classification with MobileNetV2 and GoogLeNet Architecture Pratomo, Yudha; Roseno, Muhammad Taufik; Syaputra, Hadi; Antoni, Darius
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1026

Abstract

This research develops a pempek food image classification system using two Deep Learning architectures, namely MobileNetV2 and GoogLeNet. The dataset consists of five types of pempek with a total of 446 images, which are divided for training (70%), validation (15%), and testing (15%). The model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that GoogLeNet achieved a validation accuracy of 96.21%, higher than MobileNetV2 which was only 70.58%. GoogLeNet is also more stable in convergence and more accurate in recognizing different types of pempek. This research shows that GoogLeNet is more optimal for pempek classification. In the future, this research can be extended by adding more datasets, exploring more sophisticated models, and developing mobile or web-based applications.
Co-Authors Antoni, Darius Diah Triesia Ermatita - Febriady, Mukhlis Muhammad Taufik Roseno, Muhammad Taufik Nurqolbiah, Fatihani Roseno, M. Taufik Saputra, Tommy Sukemi Sukemi Syaputra, Hadi Ubaidillah Ubaidillah