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

Luthfi radyansyah (Fakultas Ilmu Komputer)
Nizirwan (Universitas Esa Unggul)
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)

ROUTERS: Jurnal Sistem dan Teknologi Informasi
Vol. 4 No. 2, Juli 2026 (In Progress)

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.