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Fenty Ariany
Universitas Teknokrat Indonesia, Bandar Lampung
Computer Science & IT

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Perbandingan Kinerja Model ARIMA-GARCH dan LSTM Dalam Peramalan Volatilitas Bitcoin Miezan El khoir; Fenty Ariany
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9788

Abstract

Bitcoin is a cryptocurrency aset with extreme volatility, necessitating precise forecasting models for investment risk mitigation. This study aims to analyze and forecast Bitcoin price volatility using an integrated Autoregressive Integrated Moving Average - Generalized Autoregressive Conditional Heteroskedasticity (ARIMA-GARCH) approach and compare its performance with a Deep Learning method, specifically Long Short-Term Memory (LSTM). The data used is the daily closing price of Bitcoin for the period 2018 to 2025. The results indicate that the ARIMA(1,1,1)-GARCH(1,1) model effectively captures the volatility clustering phenomenon, with a significant beta parameter value of 0.8691, indicating long-term volatility persistence. However, in terms of price prediction accuracy, the LSTM model significantly outperforms the conventional statistical model. Based on the testing, the ARIMA-GARCH model produced a Mean Absolute Percentage Error (MAPE) of 18.11%, which falls into the "good forecasting" category. In contrast, the LSTM model achieved a MAPE of 3.09%, categorized as "highly accurate forecasting." The significant difference in Root Mean Square Error (RMSE) values also reinforces that the LSTM architecture is more adaptive in processing non-linear data patterns and complex Bitcoin price fluctuations. This study concludes that while ARIMA-GARCH excels in risk structure analysis, the LSTM model provides more reliable price projection results for crypto market participants.
Komparasi Algoritma Naive Bayes dan K-Nearest Neighbor untuk Analisis Sentimen Pengguna Dompet Digital pada Google Play Store M Adhe Akbar; Fenty Ariany
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9285

Abstract

The rapid growth of digital wallet users in Indonesia, reaching millions of active users, has generated a massive volume of reviews on the Google Play Store. This textual data contains crucial insights regarding customer satisfaction but is often underutilized due to challenges in processing unstructured data. This study aims to perform a comparative performance analysis between the probabilistic Naive Bayes algorithm and the distance-based K-Nearest Neighbor (KNN) in classifying user sentiment for DANA, OVO, DOKU, and LinkAja applications. This study utilizes a dataset of 18,869 reviews which exhibits a mild class imbalance with a negative sentiment dominance of 57.54%. To preserve the representation of the large original data, this research applies Stratified Sampling without synthetic data balancing techniques (such as SMOTE), followed by comprehensive preprocessing stages aided by the Sastrawi library and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. Model optimization was systematically conducted using GridSearchCV for Naive Bayes and the Elbow Method to determine the optimal k value for KNN. Empirical test results show that the Naive Bayes algorithm with a smoothing parameter alpha of 0.1 achieved the best performance with an accuracy of 88.5% and an AUC of 0.9237, outperforming KNN at k=27 which obtained an accuracy of 87.4%. The validity of this performance difference was confirmed to be significant through the McNemar statistical test with a p-value of 0.0045. Another crucial finding is computational efficiency, where Naive Bayes proved to be 129 times faster in the prediction process compared to KNN. Based on the significant advantages in accuracy and time efficiency, Naive Bayes is recommended as the superior method for real-time sentiment analysis in the financial technology ecosystem.
Co-Authors M Adhe Akbar Miezan El khoir