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All Journal ROUTERS: Jurnal Sistem dan Teknologi Informasi
Rahmat Budiarsa
Departemen of Informatic Engineering, Faculty of Computer Science University of Esa Unggul
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

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Perbandingan Algoritma Long Short-Term Memory (LSTM) Dan XGBoost Dalam Memprediksi Kualitas Udara Di Jakarta Luthfi radyansyah; Nizirwan; Riya Widayanti; Rahmat Budiarsa
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 4 No. 2, Juli 2026 (In Progress)
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v4i2.5013

Abstract

This study compares the Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) algorithms in predicting air quality in Jakarta using the Air Pollution Standard Index (ISPU) dataset. The dataset consists of 2,874 observations collected from January 1, 2024, to July 31, 2025. The results show that both models are capable of producing accurate predictions; however, XGBoost demonstrates superior performance. XGBoost achieves an RMSE of 1.9704, MAE of 1.0509, MAPE of 1.54%, and an R² of 0.9913. In contrast, LSTM produces an RMSE of 3.0667, MAE of 2.0968, MAPE of 3.71%, and an R² of 0.9790. These findings indicate that XGBoost has lower prediction error and a stronger ability to explain data variability. Additionally, particulate pollutants such as PM2.5 and PM10 are identified as the dominant factors influencing air quality. Overall, XGBoost proves to be more effective, stable, and efficient in modeling air quality data.
Co-Authors Luthfi radyansyah Nizirwan Riya Widayanti