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All Journal REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Journal La Multiapp
Kurniawan, Muhammad Randy
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Comparison of AlexNet and ResNet50 Model Performance in Classifying Images of Indonesian Traditional Food Kurniawan, Muhammad Randy; Christanto, Yulison Herry; Abdillah, Gunawan
Journal La Multiapp Vol. 6 No. 4 (2025): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v6i4.2269

Abstract

Image classification using deep learning has become an effective approach in various fields, including visual object recognition such as food identification. This study aims to compare the performance of two well-known Convolutional Neural Network (CNN) architectures, AlexNet and ResNet50, in classifying traditional Indonesian food images. The dataset used in this research is a combination of two sources: a traditional Indonesian cake dataset from Kaggle and an additional set of images of Cirebon's traditional dishes. The final dataset consists of 24 food categories with more than 4,000 images in total. Each image was preprocessed through several steps including resizing to 224x224 pixels, applying data augmentation to training samples to enhance variation, and normalization based on standard input formats of the models. The training process was carried out using the 5-Fold Cross Validation method, while performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that ResNet50 consistently outperformed AlexNet across all evaluation metrics. ResNet50 achieved an average accuracy of 92%, compared to 86% obtained by AlexNet. Additionally, ResNet50 demonstrated superior performance in terms of precision, recall, and F1-score. This difference indicates that deeper and more complex architectures like ResNet50 are more effective in learning visual patterns in diverse traditional food images. The study concludes that ResNet50 is a more optimal choice for the task of traditional Indonesian food image classification. These findings serve as a basis for future development of image-based food recognition systems and support the preservation of culinary heritage through artificial intelligence technology.
KutaBaca: Developing and Implementation of an Offline Digital Library System to Enhance Literacy in the Low-Connectivity Environment of Wiyata Tech Village Purwakarta Venica, Liptia; Sodikin, Reisa Aulia; Herlina, Nina; Elviani, Ulva; Zahron, Aulia Aufa; Kurniawan, Muhammad Randy; Nugraha, Ariel Dwika; Putri, Dewi Indriati Hadi
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 7, No 1 (2026): Reka Elkomika
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v7i1.60-69

Abstract

Achieving Quality Education (Sustainable Development Goals, or SDGs, Point 4) remains a significant challenge in rural areas characterized by low digital infrastructure. Specifically, Kutamanah Village in Purwakarta faces critical literacy issues, with its primary school's literacy rate tragically categorized as ‘red,’ compounded by a low regional Quality Education Index score of 46.70. This research aims to address this digital and literacy gap through the systematic development and implementation of KutaBaca, an Offline Digital Library system. Utilizing a Research and Development (R&D) methodology based on the Software Development Life Cycle (SDLC) model, the system was designed with a local server and an internal wireless network, allowing students and teachers to access a vast collection of e-books and learning modules without relying on an external internet connection. Evaluation focused on rigorous Functional Testing and a System Usability Scale (SUS) assessment involving 30 students and 7 teachers. The results demonstrate a 100% functional success rate and an overall average SUS score of 83.1 ('Excellent'). This confirms that KutaBaca is a reliable, user-friendly, and replicable technological innovation, effectively increasing access to information and serving as a sustainable solution to boost literacy and support SDGs 4, 10, and 17 in low-connectivity regions.
Co-Authors Christanto, Yulison Herry Elviani, Ulva Gunawan Abdillah, Gunawan Hadi Putri, Dewi Indriati Liptia Venica Nina Herlina Nugraha, Ariel Dwika Sodikin, Reisa Aulia Zahron, Aulia Aufa