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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding SNATIF ANDHARUPA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Community: Jurnal Pengabdian Pada Masyarakat Scientific Journal of Informatics
Nova Rijati
Teknik Informatika Universitas Dian Nuswantoro
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Deep Learning-Based Rice Grain Classification with Class Imbalance Handling Using Weighted Sampling and Data Augmentation Muhammad Aulia Anhar; Nova Rijati
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12930

Abstract

Rice grain quality assessment is important for maintaining product consistency and quality standards in the food industry. Manual inspection is still widely used in practice, but it is often subjective and time-consuming especially in fine-grained classification problems where the visual differences between classes are relatively subtle. Another common challenge in rice grain datasets is class imbalance where the number of normal samples is much larger than the number of defective classes. In this paper, we investigate various imbalance handling strategies for deep learning-based rice grain classification using the GrainSet dataset, which contains 30,962 rice grain images divided into eight quality classes with a highly imbalanced class distribution. The proposed approach combines weighted sampling, data augmentation, and hyperparameter optimization to address class imbalance, while Focal Loss and Class-Balanced Loss are used as comparison methods for performance evaluation. Three deep learning architectures, namely MobileNetV3, ResNet50, and ViT-Small, were evaluated using Accuracy, Macro F1-score, and Weighted F1-score metrics. Experimental results show that the proposed approach achieved the most consistent overall performance. Among the tested models, ResNet50 achieved the best result with 98.87% accuracy and a Macro F1-Score 0.9799. The results also show that convolution-based architectures are more stable than transformer-based models for texture-oriented datasets with limited training data. Furthermore, the combination of sampling-based balancing, augmentation and suitable hyperparameter configuration contributed to better recognition performance on minority classes.
An optimation of advanced encryption standard key expansion using genetic algorithm and least significant bit integration Aris Marjuni; Nova Rijati; Ajib Susanto; Daurat Sinaga; Purwanto Purwanto; Zainal Arifin Hasibuan; Noorayisahbe Mohd. Yaacob
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8367

Abstract

Ensuring data security in today’s digital landscape is of paramount importance, driving the exploration of advanced techniques for safeguarding confidential information. This study introduces a robust approach that combines advanced encryption standard (AES) encryption with key expansion, genetic algorithms (GA), and least significant bit (LSB) embedding to achieve secure data concealment within digital images. Motivated by the pressing need for enhanced data protection, our work addresses the critical challenge of securing sensitive information from unauthorized access. Specifically, we present a systematic methodology that integrates AES encryption for robust data security, GA for optimization, and LSB embedding for subtle information concealment. Through comprehensive experimentation, involving images such as ‘Lena.jpg,’ ‘Peppers.jpg,’ and ‘Baboon.jpg,’ we demonstrate the efficacy of our approach. The imperceptible modification rates mean squared error (MSE) of 0.199, 0.101, and 0.105, coupled with high peak signal-to-noise ratios (PSNR) of 10.04 dB, 9.95 dB, and 9.79 dB respectively, underscore the fidelity and subtlety of the embedded information. This study contributes to the ongoing discourse on data security by offering a comprehensive and innovative approach that addresses the evolving challenges in safeguarding digital information.
Co-Authors Abdul Syukur Ajib Susanto Aris Marjuni Astuti, Yani Parti Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Cahaya Jatmoko Christy Atika Sari Desi Purwanti Kusumaningrum Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Edi Sugiarto Erna Zuni Astuti Etika Kartikadarma Firdaus, Iqbal Galih Sabilah Rosat Heribertus Himawan Ifan Rizqa Marjuni, Aris Md Kamruzzaman Sarker Muhammad Aulia Anhar Noorayisahbe Mohd. Yaacob Nurhuda, Yeni Agus Pratama, Ahmad Sabil Deva Pratama, Yudha Hady Pratiwi, Vila Rusantia Priyo Haryoko Pujiono Pujiono Pulung Nurtantio Andono Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Rahmasari, Erisa Adyati Sinaga, Daurat Sinaga, Daurat Sindhu Rakasiwi Soeleman, Arief Sugiyanto - Toto Haryadi Urfiyati, Novia Wicaksana, Endra Maulia Yuliman Purwanto Yuliman Purwanto Zainal Arifin Hasibuan