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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JUITA : Jurnal Informatika
Muhammad Rizky Nurhambali
IPB University
Computer Science & IT Engineering

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Kajian Simulasi untuk Identifikasi Faktor yang Memengaruhi Kinerja LSTM dan XGBoost untuk Deteksi Anomali pada Data Deret Waktu yang Dilabelkan Muhammad Rizky Nurhambali; Yenni Angraini; Anwar Fitrianto
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26604

Abstract

Time series analysis has evolved to include forecasting and anomaly detection, which can be applied in various fields. Machine learning methods, such as long short-term memory (LSTM) and extreme gradient boosting (XGBoost), are widely developed because they are considered superior to conventional methods. Both use a forecasting approach for anomaly detection. However, the limitations of both methods on anomalies, such as data length, labeling method, and number of anomalies have not been explored. Therefore, this study aims to identify factors that affect the performance of LSTM and XGBoost in forecasting and anomaly detection through various scenarios and compare their metrics evaluation. The study utilizes Jakarta's air quality index data for 2018–2023, which was preprocessed and augmented for simulation purposes. The study shows that the LSTM method is superior to XGBoost, as shown by the lower MAPE (14.7024%), lower RMSE (13.9909), and higher balanced accuracy (0.9935). These results are reinforced by the significant Mann-Whitney test between the two methods, indicating a difference in the method's accuracy. In addition, the Kruskal-Wallis test for each combination of method and treatment showed significant results. These results indicate that data length, labeling method, and number of anomalies affect the method's accuracy
Machine learning approaches for anomaly detection of Jakarta air quality index Muhammad Rizky Nurhambali; Yenni Angraini; Anwar Fitrianto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2543-2553

Abstract

Anomalies in time series data are observations that deviate markedly from surrounding values or overall patterns. Air quality index (AQI) data, which vary over time, provide a suitable context for anomaly detection. Time series anomaly detection can be done with machine learning approaches like long short-term memory (LSTM) and extreme gradient boosting (XGBoost). These methods have advantages over conventional methods in handling nonlinearity and large data dimensions. This study compares LSTM and XGBoost methods for detecting anomalies in Jakarta's hourly AQI data. The dataset was obtained from the AirNow website and covers the period from January 1, 2018, to December 31, 2023. Anomalies in the observed data were labeled using moving range (MR) (2) and (3) approaches with three and four-sigma thresholds, and feature engineering (FE) was applied to improve model performance. The results indicate that LSTM is more suitable than XGBoost for forecasting and classification tasks in AQI data. LSTM achieved an average mean absolute percentage error (MAPE) of 10.3840%, a root mean square error (RMSE) of 10.5913, and a balanced accuracy (BACC) of 0.9424 under MR (2) labeling with the four-sigma rule. The anomalies detected mostly occurred between 21:00 and 09:00 and during the rainy season.
Co-Authors Fitrianto, Anwar Yenni Angraini