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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization JOURNAL OF APPLIED INFORMATICS AND COMPUTING Communications in Science and Technology Building of Informatics, Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) JURNAL PAI: Jurnal Kajian Pendidikan Agama Islam
Agastya, I Made Artha
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Sentiment Analysis of Banking Application Reviews on Google Play Store using Support Vector Machine Algorithm Prasetyo, Martinus Juan; Agastya, I Made Artha
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4536

Abstract

Banking applications are increasingly important in facilitating daily financial transactions. However, to ensure service quality, developers need to understand user feedback. Reviews on the Google Play Store provide important insights related to satisfaction, complaints, and suggestions. Therefore, this study aims to develop the Sentiment Analysis Model for Banking Application Reviews Using Support Vector Machine (SVM). Data collected from three popular banks in Indonesia is used to train and test models. This research also contributes to providing multi -bank dataset which can be a benchmark. Various scenarios of the distribution of training and test data are explored, and repeated tests are carried out with different random state values to get stable results. The results showed that the SVM model was able to achieve good accuracy, with BRI Mobile dataset reaching the highest accuracy of 92.97%, followed by a combined dataset of 90.05%, BCA Mobile 89.73%, and Livin Mandiri 87.46%. Negative reviews are dominated by technical complaints, while positive reviews highlight the ease and reliability of the application. This study shows that the approach used has succeeded in producing competitive performance, and application developers are advised to focus on improving technical aspects, such as fixing login, verification, and transaction problems, in order to increase user satisfaction.
Peramalan Multivariat Saham Bank Indonesia dengan Model ARIMA dan LSTM Ramadhan, Akhdan Ferdiansyah; Agastya, I Made Artha
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7352

Abstract

Stock price forecasting is a crucial aspect of financial market analysis, particularly in supporting more accurate and informed investment decision-making. This study compares the performance of the statistical Autoregressive Integrated Moving Average (ARIMA) model with three variants of the Long Short-Term Memory (LSTM) architecture, namely Vanilla LSTM, Bidirectional LSTM, and Stacked LSTM, in predicting closing prices and trading volumes of Indonesian bank stocks—specifically BBCA.JK, BBRI.JK, and BMRI.JK. The data were obtained from Kaggle and processed through normalization, transformation, and model training stages using Google Colab and TensorFlow. Evaluation was conducted using RMSE, MAE, and MAPE metrics. The results indicate that ARIMA performs better in forecasting closing prices, achieving an average MAPE of 1.9%, while Bidirectional LSTM yielded the best results in forecasting trading volumes, particularly for BBRI and BMRI stocks. However, the prediction error for volume data remains relatively high (average MAPE of 36.4%) due to its volatile nature. These findings suggest that data characteristics significantly influence model effectiveness. LSTM-based models demonstrate superior capabilities in capturing complex non-linear patterns and exhibit advantages in multivariate forecasting compared to the ARIMA model. This study is expected to serve as a reference for selecting appropriate forecasting models in the context of Indonesian banking stock markets. The results highlight a trade-off between ARIMA, which excels in modeling linear patterns such as closing prices, and LSTM, which is more adaptive to non-linear patterns like trading volumes.
Evaluation of SMOTE Technique in the Comparison of XGBoost and Random Forest Algorithms for Liver Disease Prediction Rohman, Wahyutri Nur; Agastya, I Made Artha
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10239

Abstract

In many countries, including Indonesia, liver disease remains a major cause of morbidity and mortality. Early detection plays a crucial role in improving treatment outcomes. This study evaluates the performance of two widely used machine learning models Random Forest and XGBoost for predicting liver disease, employing the SMOTE balancing technique to address class imbalance. The primary objectives are to enhance model fairness, reduce overfitting, and improve sensitivity toward the minority class. Model performance is assessed using accuracy, precision, recall, and F1-score metrics. The XGBoost model achieved an average accuracy of 99.74%, precision of 99.77%, recall of 99.75%, and F1-score of 99.72%, while the Random Forest model attained an average accuracy of 99.82%, precision of 99.89%, recall of 99.75%, and F1-score of 99.75%. Both models demonstrated excellent predictive capability, with Random Forest slightly outperforming XGBoost. These results highlight the importance of data balancing and robust model validation in developing reliable machine learning models for healthcare decision-making.
Co-Authors Adhani, Muhammad Azmi Afrig Aminuddin Arief Setyanto Kusrini Kusrini, K López, Alba Puelles Martínez-Béjar, Rodrigo Mohamad Firdaus, Mohamad Mohammad Haris Muzakki, Mohammad Haris Noor Akhmad Setiawan Nugroho, Agung Prasetyo, Martinus Juan Pratama, Muhammad Egy Ramadhan, Akhdan Ferdiansyah Rohman, Wahyutri Nur Saputra, Gilang Ardhi Sukaria, Petra Nugra Teguh Bharata Adji Wicaksono, Dika Wiryawan Sujana, Rajendra Artanto