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All Journal Jurnal Ilmu Komputer dan Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Agro Bali: Agricultural Journal
Saragih, Frederic Morado
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Agriculture, Biological Sciences & Forestry Computer Science & IT Library & Information Science

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Analysis of Food Security Index Predictions in Indonesia using Machine Learning Approach Saragih, Frederic Morado; Wibowo, Wahyu Catur
Agro Bali : Agricultural Journal Vol 8, No 2 (2025)
Publisher : Universitas Panji Sakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37637/ab.v8i2.2302

Abstract

Food is one of the basic human needs that should be available at all times. To fulfill the role of in a region, the concept of food security is established to measure sufficiency, availability and quality of food. Food security for a country is expressed using Food Security Index (FSI). FSI score for a country reflects its ability for survival. It is therefore very important to measure the score and be able to predict future score to enable control and improvement. To realize the improvement of Indonesia's food security, a model is needed to predict the Food Security Index in Indonesia. This This paper explores the models using data from the Indonesian Food Security and Vulnerability Atlas (FSVA) at the Regency and City levels in 2018-2024 period with a total of 3,598 records. We evaluated Multiple Linear Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, eXtreme Gradient Boosting, Support Vector Regression, and Ensemble Machine Learning models for predicting the FSI score. The models are evaluated using r-squared (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results shows that the XGBoost method is the best method for predicting the Food Security Index in Indonesia with an R2 value of 0.912, RMSE of 0.053, and MAE of 0.037. Meanwhile, the ensemble machine learning method provides an R2 value of 0.79, RMSE of 0.083, and MAE of 0.063. In addition, the XGBoost method predicts the Food Security Index score in 2025 to be 75.56 and in 2026 to be 75.48.
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 Wahyu Catur Wibowo Wisnu Jatmiko