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All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal ULTIMATICS Jurnal Penelitian, Pendidikan dan Pengajaran: JPPP Ar-Rasyid: Jurnal Pendidikan Agama Islam Educenter: Jurnal Ilmiah Pendidikan sudo Jurnal Teknik Informatika Hello World Blend Sains Jurnal Teknik EDUCENTER JURNAL PENDIDIKAN Tsabit
Basri, Mhd.
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Comparative Analysis of the Performance of VGG16 and ResNet50 Architectures in Multi-Class Classification of Rice Plant Diseases Based on Convolutional Neural Networks (CNN) Aditya, Krisna; Basri, Mhd.
Tsabit Journal of Computer Science Vol. 2 No. 2 (2025): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit55

Abstract

Rice plant diseases significantly affect crop productivity and food security, making early and accurate disease detection essential for effective agricultural management. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN), have demonstrated strong potential in image-based plant disease classification. This study presents a comparative analysis of the performance of VGG16 and ResNet50 architectures for multi-class classification of rice plant diseases using CNN-based approaches. A dataset of rice leaf images representing multiple disease classes and healthy conditions was collected and preprocessed through image resizing, normalization, and data augmentation to enhance model generalization. Both pre-trained models were fine-tuned using transfer learning to adapt them to the rice disease classification task. Model performance was evaluated using standard metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that both architectures achieve high classification performance; however, ResNet50 demonstrates superior accuracy and better generalization capability compared to VGG16, particularly in handling complex disease patterns and intra-class variations. Meanwhile, VGG16 offers a simpler architecture with faster convergence and lower computational complexity. The findings of this study provide insights into the selection of appropriate CNN architectures for rice plant disease classification and support the development of intelligent decision support systems in precision agriculture.
Development of a Decision Support System to Determine Best-Selling Menu Canteen Employees of the Bank Indonesia Representative Office in North Sumatra Province using the Topsis Method Adhari, M. Rizki; Basri, Mhd.
Tsabit Journal of Computer Science Vol. 2 No. 2 (2025): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit60

Abstract

The availability of accurate sales information is essential for supporting managerial decision-making in institutional food services. At the Bank Indonesia Representative Office in North Sumatra Province, determining the best-selling menu for employee canteen services is still largely based on manual evaluation, which may lead to inefficiencies and subjective judgments. This study aims to develop a Decision Support System (DSS) to identify the best-selling canteen menu using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The system evaluates menu alternatives based on multiple criteria, including sales volume, price, menu availability, and employee preferences. Data were collected from historical sales records and questionnaires distributed to canteen employees. The TOPSIS method was applied to rank menu alternatives by calculating their relative closeness to the ideal positive and ideal negative solutions. The DSS was implemented as a computerized system to facilitate data processing, ranking, and visualization of decision results. The results show that the proposed system is able to objectively determine the best-selling menu and provide consistent rankings compared to conventional methods. The developed DSS improves accuracy, efficiency, and transparency in menu evaluation, thereby supporting better planning and inventory management for the employee canteen. This study demonstrates that integrating multi-criteria decision-making methods into a DSS can effectively enhance decision quality in institutional food service management.
Co-Authors Abdul Fadlil Adhari, M. Rizki Aditya, Krisna Aldi, Defri Andi Zulherry Andi Zulherry Anton Yudhana Baehaqi Hayani, Wirda Hazidar, Al Hamidy Indah Purnama Sari Ismail Hanif Batubara Khairullah, Muhammad Zulfahmi Lutfi Basit Mahardika Abdi Prawira Syafrayani, Putri Rizki Syahri, Alfi Zein, Liza Azzahra