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All Journal IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi JOIV : International Journal on Informatics Visualization Syntax Literate: Jurnal Ilmiah Indonesia JOURNAL OF APPLIED INFORMATICS AND COMPUTING Indonesian Journal of Electrical Engineering and Computer Science
Rahadianti, Laksmita
Faculty Of Computer Science, Universitas Indonesia, Depok, Indonesia
Humanities Automotive Engineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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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.
Co-Authors Aniati Murni Arymurthy Caryn, Femilia Hardina EVI YULIANTI Hamidiyati, Nazifa Hartanto, Cahyo Adhi Kardawi, Muhammad Yusuf Nugraha, Made Prastha Persada, Bayu Satria Raihan Taufiq, Muhammad Feriansyah Saragih, Frederic Morado Steven Christ Pinantyo Arwidarasto Susanto, Muhammad Rifqi Priyo