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All Journal Jurnal Ilmu Komputer dan Informasi EMITTER International Journal of Engineering Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Natural
Kardawi, Muhammad Yusuf
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Earth & Planetary Sciences Energy Immunology & microbiology Library & Information Science Neuroscience Physics

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A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates Suwida, Katon; Kardawi, Muhammad Yusuf; Purwitasari, Diana; Mabahist, Fahril
EMITTER International Journal of Engineering Technology Vol 11 No 1 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i1.768

Abstract

When the COVID-19 pandemic hit, the use of vaccines was advertised as the end of the pandemic by the entire world. However, the chances of vaccination depended on the sentiments of society and individuals about the vaccine. People's acceptance of vaccines can change depending on conditions and events. Social media platforms such as Twitter can be used as a source of information to find out the conditions and attitudes of the community toward the program. By implementing a machine learning technique on the COVID-19 vaccine dataset, we hope to impact the classification result with text. This study suggests three distinct machine learning models for classifying texts of the COVID-19 vaccination, namely a model based on the first lexicon using the feature extraction method; second, using the word insertion technique to utilize distribution representation; and third, a combination model of distribution representation and feature extraction based on the lexicon. From the evaluation that has been carried out, we found that a combination of lexicon-based and distributional representation methods succeeded in giving the best results for classifying the level of acceptance of the COVID-19 vaccine in Indonesia with an accuracy score of 71.44% and an F1-score of 71.43%.
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.
Electrofacies classification of a mixed carbonate-siliciclastic reservoir using machine learning techniques ADHARI, MUHAMMAD RIDHA; WIRANDHA, FREDDY SAPTA; YANIS, MUHAMMAD; KARDAWI, MUHAMMAD YUSUF
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Many scientific fields, including the geosciences, have successfully employed machine learning to address numerous significant issues. Current studies show that the application of machine learning within the geosciences is still in its early stages, and there is a huge potential for this technique that need to be explored. This research focuses on the Late Permian Beekeeper Formation from the Perth Basin, Australia. It aims to improve our understanding of the application of machine learning to characterise subsurface rock formations. The objectives of this study are threefold: (1) to conduct cutting, crossplot, and modern machine learning analyses on a mixed carbonate-siliciclastic reservoir; (2) to compare the results from the aforementioned analyses and to interpret the electrofacies and lithofacies; and (3) to understand the degree of accuracy of the application of machine learning in the characterisation of the subsurface rock formations. Cutting, crossplotting, and modern machine learning analyses have been conducted to achieve the aim and objectives of this study. Seven electrofacies, associated with nine lithofacies, were identified within the studied data, and these were classified into carbonate-dominated facies group, siliciclastic-dominated facies group, and mixed carbonate-siliciclastic facies group. Results also show the presence of stratal and compositional mixing within the Beekeeper Formation. A combination of cutting, crossplot, and machine learning analyses can provide a better, more accurate, and more reliable interpretation of the facies of the Beekeeper Formation. This study is expected to advance our understanding of the application of machine learning in geosciences.
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 Diana Purwitasari Hestrio, Yohanes Mabahist, Fahril Muhammad Ridha Adhari, Muhammad Ridha Muhammad Yanis Persada, Bayu Satria Rahadianti, Laksmita Saragih, Frederic Morado Suwida, Katon Wirandha, Freddy Sapta Wisnu Jatmiko