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This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Data and Science SKANIKA: Sistem Komputer dan Teknik Informatika International Journal of Artificial Intelligence in Medical Issues
Jullev Atmadji, Ery Setiyawan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

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Journal : Indonesian Journal of Data and Science

 1 
Evaluating the Performance of Voting Classifier in Multiclass Classification of Dry Bean Varieties Adi Pratama, I Putu; Jullev Atmadji, Ery Setiyawan; Purnamasar, Dwi Amalia; Faizal, Edi
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.124

Abstract

This study explores the application of a voting classifier, integrating Decision Tree, Logistic Regression, and Gaussian Naive Bayes models, for the multiclass classification of dry bean varieties. Utilizing a dataset comprising 13,611 images of dry bean grains, captured through a high-resolution computer vision system, we extracted 16 features to train and test the classifier. Through a rigorous 5-fold cross-validation process, we assessed the model's performance, focusing on accuracy, precision, recall, and F1-score metrics. The results demonstrated significant variability in the classifier's performance across different data subsets, with accuracy rates fluctuating between 31.23% and 96.73%. This variability highlights the classifier's potential under specific conditions while also indicating areas for improvement. The research contributes to the agricultural informatics field by showcasing the effectiveness and challenges of using ensemble learning methods for crop variety classification, a crucial task for enhancing agricultural productivity and food security. Recommendations for future research include exploring additional features to improve model generalization, extending the dataset for broader applicability, and comparing the voting classifier's performance with other ensemble methods or advanced machine learning models. This study underscores the importance of machine learning in advancing agricultural classification tasks, paving the way for more efficient and accurate crop sorting and grading processes.
Predictive Modeling of Air Quality Levels Using Decision Tree Classification: Insights from Environmental and Demographic Factors Iwan Sudipa, I Gede; Habibi, Muhammad; Jullev Atmadji, Ery Setiyawan; Arfiani, Ika
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.201

Abstract

Air pollution poses a significant global challenge, adversely impacting public health and environmental sustainability. Understanding the factors influencing air quality is essential for developing effective mitigation strategies. This study aims to analyse key environmental and demographic factors, such as PM2.5 concentration, population density, and proximity to industrial areas, to predict air quality levels using a Decision Tree model. The dataset, comprising 5000 samples, was pre-processed by encoding the target variable and applying Z-score normalization to numerical features. The model was trained on 80% of the data and evaluated on the remaining 20%, achieving an accuracy of 93%. Evaluation metrics, including a classification report and confusion matrix, demonstrated the model's effectiveness in distinguishing between four air quality categories: Good, Moderate, Poor, and Hazardous. PM2.5 emerged as the most critical predictor, followed by demographic and industrial factors. These findings underscore the potential of machine learning models in providing actionable insights for air quality management. The results contribute to public policy by highlighting the need for targeted interventions in high-risk areas and the importance of incorporating environmental data into urban planning. Future work should focus on expanding the feature set and exploring ensemble techniques to further enhance predictive accuracy and robustness.
Co-Authors Adityo Permana Wibowo Agus Hariyanto Angga Gumilang, Mukhamad Apriliano, Galuh Bekti Maryuni Susanto Edi Faizal Elly Antika, Elly Gumilang, Mukhamad Angga Gurohman, Diki Taufik I Gede Iwan Sudipa I Putu Adi Pratama Ika Arfiani Maryuni Susanto, Bekti Muhammad Habibi Mukhlisoh, Nanik Anita Pramulintang, Ardian Hilmi Purnamasar, Dwi Amalia Surateno, Surateno Wulansari, Tanti