Performance Analysis of XGBoost and LSTM Methods in Air Quality Time Series Prediction

  • Teguh Herlambang Universitas Nahdlatul Ulama Surabaya
  • Anas Tifa Rahma Siswanti Universitas Nahdlatul Ulama Surabaya
  • Bambang Suharto
  • Zuraini Othman Universiti Teknikal Malaysia Melaka
  • Mohd Sanusi Azmi Universiti Teknikal Malaysia Melaka
DOI: https://doi.org/10.19139/soic-2310-5070-3587
Keywords: Forecasting, ISPU, XGBoost, LSTM, Time Series

Abstract

Air pollution in urban areas like Surabaya poses signifcant risks to public health. Accurate forecasting ofAir Pollution Index (ISPU) parameters is essential for early warning systems. Previous studies often overlook temporaldependencies by focusing on instantaneous correlations. This study proposes a temporal forecasting framework usingExtreme Gradient Boosting (XGBoost) with autoregressive features and Long Short-Term Memory (LSTM) networks. Datafrom 2021 to 2024 were processed using a sliding window approach (lags) to capture historical patterns. Results indicatethat while XGBoost provides robust baseline predictions, LSTM’s ability to retain long-term dependencies yields superiorstability in capturing complex pollutant fluctuations. Although RMSE and MAE values were signifcantly improved throughtemporal modeling, the moderate R2 scores suggest that external meteorological factors, such as wind speed and humidity,remain critical latent variables. This study contributes a methodological benchmark for urban air quality monitoring usinghybrid machine learning approaches.

Author Biographies

Anas Tifa Rahma Siswanti, Universitas Nahdlatul Ulama Surabaya
Department of Information System, Faculty of Economic Business and Digital Technology, Universitas Nahdlatul Ulama Surabaya, Indonesia
Bambang Suharto
Department of Tourism and Hospitality, Faculty of Vocational, Airlangga University, Surabaya, Indonesia
Zuraini Othman, Universiti Teknikal Malaysia Melaka
Department of Diploma Studies, Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Malaysia
Mohd Sanusi Azmi, Universiti Teknikal Malaysia Melaka
Department of Software Engineering, Fakulti Teknologi Maklumat dan Informasi, Universiti Teknikal Malaysia Melaka, Malaysia
Published
2026-03-06
How to Cite
Herlambang, T., Siswanti, A. T. R., Suharto, B., Othman, Z., & Azmi, M. S. (2026). Performance Analysis of XGBoost and LSTM Methods in Air Quality Time Series Prediction. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-3587
Issue
Online First
Section
Research Articles
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