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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JUITA : Jurnal Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
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.
Penerapan Pemodelan Konvensional dan Deep Learning pada Data Saham dengan Pencilan Muhammad Firlan Maulana; Salsabila Fayiza; Bulan Cahyani Suhaeri; Ardelia Rahma Febyan; Thariq Hambali; Yenni Angraini; Muhammad Rizky Nurhambali
Jurnal Teknik Informatika dan Sistem Informasi Vol 12 No 1 (2026): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v12i1.10587

Abstract

Apple Inc. stock (AAPL), one of the leading technology companies, is one of the concerns of investors as it continues to see an increase in the number of users every year. Therefore, forecasting Apple's stock price is important to help investors mitigate risks and optimize investment decisions. This forecasting can be done using two main approaches, namely conventional approaches such as Autoregressive Integrated Moving Average (ARIMA) and deep learning-based approaches such as Long Short-term Memory Network (LSTM). This study aims to find the best model using both methods, as well as compare the accuracy of the models based on datasets with outliers and datasets with handled outliers. The dataset analyzed in this study comes from weekly AAPL stock closing price data for 500 periods, from January 26, 2015 to August 19, 2024 obtained from Yahoo Finance. This study obtained the ARIMA(1,1,1) model as the best model for both datasets, with the outlier-handled dataset producing better test MAPE, while the dataset with outliers had better training MAPE. The LSTM method produced smaller MAPE values than ARIMA, demonstrating its superiority in capturing the fluctuating patterns of the AAPL stock data. Outlier handling was shown to improve model accuracy, as seen in the outlier-handled dataset. This research provides insight into the effectiveness of statistical and deep learning methods in modeling stock prices, and emphasizes the importance of outlier handling in financial data analysis.
Co-Authors Ardelia Rahma Febyan Bulan Cahyani Suhaeri Fitrianto, Anwar Muhammad Firlan Maulana Salsabila Fayiza Thariq Hambali Yenni Angraini Yenni Angraini