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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Maharina Maharina
Universitas Buana Perjuangan Karawang
Computer Science & IT Electrical & Electronics Engineering Engineering

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Air quality prediction using boosting-based machine learning models for sustainable environment Ahmad Fauzi; Maharina Maharina; Jamaludin Indra; Ayu Ratna Juwita; Agustia Hananto; Euis Nurlaelasari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp515-523

Abstract

High levels of air pollution are extremely harmful to humans and the environment. They increase the risk of respiratory infections and lung cancer, especially among vulnerable populations. Therefore, developing effective pollution control measures is crucial for mitigating these negative impacts. We need to implement effective methods to predict and manage air quality for the sake of public health and a healthier environment. In recent years, machine learning (ML) methods have been increasingly utilized in air quality prediction due to their ability to analyze datasets and identify complex patterns. However, the reliability and accuracy of air quality prediction models remain a challenge. This study proposes a boosting-based ML model for predicting air quality. We implemented three stages in the proposed method. In the first stage, we conducted data preprocessing and analysis to eliminate noise, remove redundant data, and encode categorical features. In the second stage, we predicted air quality categories by leveraging 25 ML models, dividing them into three distinct categories. The results show that the extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and adaptive boosting (AdaBoost) models outperform the others in air quality prediction, achieving an accuracy of 99%. Finally, we compared these three models using 10-fold cross validation to ensure they generalize well in last stage.
Transfer learning-based malnutrition classification using VGG16 and comparative analysis of CNN architectures Ahmad Fauzi; Haerul Yuda Aditiya; Maharina Maharina; Sihabudin Sihabidin; Muhammad Ansari Adista; Iflan Naufal; Natasya Eka Nanda Sonia Puri; Candra Zonyfar
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11346

Abstract

Childhood malnutrition remains a critical public health challenge in developing countries, with Indonesia ranking fifth globally for stunting burden. Traditional anthropometric assessment methods are time-consuming, resource-intensive, and require trained personnel, necessitating efficient computer-based early detection approaches. This study proposes a deep learning-based method for automated nutritional status classification using facial image analysis. We developed and compared multiple transfer learning architectures including visual geometry group 16 (VGG16), densely connected convolutional network 121 (DenseNet121), mobile network version 2 (MobileNetV2), and residual network 50 (ResNet50) for classifying children into three categories: healthy, malnutrition, and stunting. Results demonstrated that VGG16, a simpler architecture trained for only 10 epochs, achieved optimal performance with 91.8% accuracy, and significantly outperforming more complex modern architectures like ResNet50. This finding challenges the conventional assumption that newer, deeper models invariably perform better, and particularly when working with limited medical datasets. The study revealed that longer training durations led to performance degradation due to overfitting, emphasizing the importance of balancing model complexity with dataset characteristics. These findings support the development of practical artificial intelligence (AI)-based malnutrition screening systems suitable for resource-constrained environments, potentially improving early detection capabilities, and public health outcomes in developing regions.
Co-Authors Ahmad Fauzi Ayu Ratna Juwita Candra Zonyfar Euis Nurlaelasari Haerul Yuda Aditiya Hananto, Agustia Iflan Naufal Indra, Jamaludin Muhammad Ansari Adista Natasya Eka Nanda Sonia Puri Sihabudin Sihabidin