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All Journal Jurnal Ilmu Komputer dan Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Saragih, Frederic Morado
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Computer Science & IT Library & Information Science

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Indonesian Food Classification Using Deep Feature Extraction and Ensemble Learning for Dietary Assessment Kardawi, Muhammad Yusuf; Saragih, Frederic Morado; Rahadianti, Laksmita; Arymurthy, Aniati Murni
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10643

Abstract

Food is a cornerstone of culture, shaping traditions and reflecting regional identities. However, understanding the nutritional content of diverse cuisines can be challenging due to the vast array of ingredients and the similarities in appearance across different dishes. While food provides essential nutrients for the body, excessive and unbalanced consumption can harm health. Overeating, particularly high-calorie and fatty foods, can lead to an accumulation of excess calories and fat, increasing the risk of obesity and related health issues such as diabetes and heart disease. This paper introduces a novel ensemble learning approach with a dictionary that contains food nutrition content for addressing this challenge, specifically on Padang cuisine, a rich culinary tradition from West Sumatera, Indonesia. By leveraging a dataset of nine Padang dishes, the system employs image enhancement techniques and combines deep feature extraction and machine learning algorithms to classify food items accurately. Then, depending on the classification results, the system evaluates the nutritional content and creates a dietary evaluation report that includes the amount of protein, fat, calories, and carbs. The model is evaluated using different evaluation metrics and achieving a state-of-the-art accuracy of 85.56%, significantly outperforming standard baseline models. Based on the findings, the suggested approach can efficiently classify different Padang dishes and produce dietary assessments, enabling personalised nutritional recommendations to provide clear information on a balanced diet to enhance physical and overall wellness.
MSDFF-RCNet: A Combined Multi-Structure Data Fusion Framework and Recurrent Attention for Remote Sensing Scene Classification Hestrio, Yohanes; Persada, Bayu Satria; Saragih, Frederic Morado; Kardawi, Muhammad Yusuf; Jatmiko, Wisnu; Arymurthy, Aniati Murni
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1475

Abstract

Remote sensing scene classification faces significant challenges in distinguishing visually similar land-use categories due to high intraclass variation and interclass similarity in high-resolution imagery. Although deep learning approaches have shown promise, single-architecture methods often fail to capture the diverse spatial and hierarchical features required for robust scene discrimination. This study proposes MSDFF-RCNet, a multi-structure data fusion framework combined with recurrent attention mechanisms to enhance remote sensing scene classification performance. The framework integrates complementary feature representations from AlexNet, ResNet50, and DenseNet161 architectures, while the recurrent attention mechanism focuses on discriminative spatial regions for improved classification accuracy. Comprehensive experiments conducted on four benchmark datasets demonstrate substantial performance improvements over the baseline ARCNet architecture: UC Merced (43.8% to 84.9%, +41.1%), AID (63.8% to 94.4%, +30.6%), NWPU-RESISC45 (61.5% to 95.4%, +33.9%), and OPTIMAL 31 (47.3% to 87.9%, +40.6%). Statistical significance analysis confirmed the reliability of these improvements (p < 0.01), while comprehensive evaluation across precision, recall, and F1-score metrics validated the framework’s robustness. Although the multi-structure approach requires substantial computational resources (25.6× parameter increase), the consistent and significant accuracy improvements across diverse datasets demonstrate the effectiveness of complementary feature fusion for remote sensing scene classification. The proposed framework provides a valuable contribution to automated Earth observation systems that require high-precision land-use classification capabilities.
Co-Authors Aniati Murni Arymurthy Hestrio, Yohanes Kardawi, Muhammad Yusuf Persada, Bayu Satria Rahadianti, Laksmita Wisnu Jatmiko