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Muhammad Ikhsan
Informatics Engineering, Faculty of Engineering, University of Surabaya, Surabaya, East Java, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Development of a Mobile Application Using Convolutional Neural Networks for Recognizing Indonesian Traditional Snacks Njoto Benarkah; Joko Siswantoro; Muhammad Ikhsan
Teknika Vol. 14 No. 2 (2025): July 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i2.1236

Abstract

Indonesian traditional snacks constitute a vital element of the country’s cultural heritage. However, growing modernization has contributed to a decline in public familiarity, particularly among younger generations. This study presents a mobile-based image classification desigend to automatically recognize Indonesian traditional snacks using convolutional neural networks (CNNs). A dataset of 3,240 images across 16 snack categories was collected using a smartphone camera. Five CNN architectures, which are, AlexNet, EfficientNetV2M, MobileNetV2, ResNet50V2, and VGG19, were evaluated for classification performance. MobileNetV2 achieved the highest accuracy and F1-score, both reaching 100%. The final model was deployed in a mobile application environment, with the backend developed using Flask and integrated into the Android platform. This research work demonstrates the potential of lightweight CNN models in preserving cultural knowledge through accessible mobile technology.
Co-Authors Joko Siswantoro Njoto Benarkah