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

Benarkah, Njoto and Siswantoro, Joko and Ikhsan, Muhammad (2025) Development of a Mobile Application Using Convolutional Neural Networks for Recognizing Indonesian Traditional Snacks. Teknika, 14 (2). pp. 239-245. ISSN 2549-8037, EISSN 2549-8045

PDF Njoto Benarkah_Development of a Mobile Application.pdf Download (1MB)

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.

Item Type:	Article
Uncontrolled Keywords:	CNN, Traditional Snack, Deep Learning, Mobile Application, Classification
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	Ester Sri W. 196039
Date Deposited:	02 Jul 2025 02:17
Last Modified:	02 Jul 2025 04:15
URI:	http://repository.ubaya.ac.id/id/eprint/48831

Actions (login required)

View Item