Perbandingan Algoritma Long Short-Term Memory (LSTM) Dan XGBoost Dalam Memprediksi Kualitas Udara Di Jakarta

Luthfi radyansyah; Nizirwan; Riya Widayanti; Rahmat Budiarsa

doi:10.25181/rt.v4i2.5013

Authors

Luthfi radyansyah Fakultas Ilmu Komputer
Nizirwan Universitas Esa Unggul https://orcid.org/0000-0003-1189-9093
Riya Widayanti Departemen of Informatic Engineering, Faculty of Computer Science University of Esa Unggul
Rahmat Budiarsa Departemen of Informatic Engineering, Faculty of Computer Science University of Esa Unggul

DOI:

https://doi.org/10.25181/rt.v4i2.5013

Keywords:

Air Quality, Pollution, LSTM, XGBoost, Confusion Matrix

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 PM_2.5 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.

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