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Derby Brylian Adinara
MCG-Instrumentation Study Program, State College of Meteorology, Climatology, and Geophysics, Tangerang, Banten, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Comparative Analysis of Machine Learning and Deep Learning Models for PM2.5 and PM10 Time Series Forecasting Using the SISANAPAS Web Platform Dwi Indra Prasetyo; Agustina Rachmawardani; Bayu Satrio; Derby Brylian Adinara
Teknika Vol. 14 No. 3 (2025): November 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i3.1335

Abstract

Particulate matter (PM2.5 and PM10) pollution remains a significant environmental concern in Indonesia. This study employed the SISANAPAS web platform to compare machine learning (ML) and deep learning (DL) algorithms for PM2.5 and PM10 forecasting. Using 3-hourly data from Cibeureum (January-October 2024), which underwent comprehensive pre-processing (K-Nearest Neighbors imputation, Z-score outlier removal, Min-Max scaling, feature engineering), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Random Forest, and XGBoost models were evaluated. XGBoost Regression provided the most accurate forecasts, with an R² of 0.85, RMSE of 5.05 µg/m³, and MAE of 3.19 µg/m³ for PM2.5, and an R² of 0.85, RMSE of 9.71 µg/m³, and MAE of 6.55 µg/m³ for PM10. These results, significantly outperforming LSTM and GRU, highlight XGBoost's potential for reliable air quality prediction and demonstrate SISANAPAS as a valuable tool for environmental data analysis, crucial for informing public health and environmental policies.
Co-Authors Agustina Rachmawardani Bayu Satrio Dwi Indra Prasetyo