Development of a Mobile Application Using Convolutional Neural Networks for Recognizing Indonesian Traditional Snacks

Njoto Benarkah; Joko Siswantoro; Muhammad Ikhsan

doi:10.34148/teknika.v14i2.1236

Authors

Njoto Benarkah Informatics Engineering, Faculty of Engineering, University of Surabaya, Surabaya, East Java, Indonesia
Joko Siswantoro Informatics Engineering, Faculty of Engineering, University of Surabaya, Surabaya, East Java, Indonesia
Muhammad Ikhsan Informatics Engineering, Faculty of Engineering, University of Surabaya, Surabaya, East Java, Indonesia

DOI:

https://doi.org/10.34148/teknika.v14i2.1236

Keywords:

CNN, Traditional Snack, Deep Learning, Mobile Application, Classification

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.

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