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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Building of Informatics, Technology and Science
Andreas, Christopher
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Computer Science & IT Control & Systems Engineering

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Machine Learning Approaches for Predicting Seasonal Stock Trends Gunawan, Jason Miracle; Andreas, Christopher; Saputri, Theresia Ratih Dewi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112504

Abstract

The financial market is vital for economic growth yet it often experiences volatility, particularly in Indonesia’s transportation sector where stock prices are strongly affected by seasonal fluctuations. Conventional forecasting methods often neglect these recurring patterns, lowering predictive accuracy. This study assesses the capability of Machine Learning algorithms to capture seasonality in stock price prediction, using PT Garuda Indonesia (Persero) Tbk (GIAA.JK)’s monthly data from August 2019 to May 2025, retrieved from Yahoo Finance. Four models–Linear Regression, Extreme Gradient Boosting (XGBoost), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)–were trained and tested, with performance evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Hyperparameter tuning was applied to XGBoost, LSTM, and GRU, while statistical validation employed the Kruskal-Wallis test. Results showed that the tuned GRU outperformed other models, achieving MAE of 5.90, RMSE of 7.33, and MAPE of 9.67%, demonstrating ‘excellent’ accuracy in modelling both short-term and seasonal dynamics. These findings highlight the superiority of GRU in modelling both short-term fluctuations and long-term seasonal dependencies in stock prices. The results contribute practical insights for investors and emphasize the importance of integrating seasonality in predictive models for volatile sectors
Penerapan Regresi Logistik, K-NN, dan Naïve Bayes Berbasis Pendekatan CRISP-DM dalam Memprediksi Penyakit Jantung Chang, Rayna Shera; Kuswanto, Natalie Grace Widjaja; Tedja, Jessica Laurentia; Andreas, Christopher
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.v7i4.8518

Abstract

Heart disease remains the leading cause of mortality globally, despite having significant potential to be controlled through early detection and effective risk-factor management. To improve the accuracy and efficiency of early detection, machine learning technology is employed to develop predictive models for heart disease risk. The research aims to compare the performance of three classification algorithms in predicting heart disease risk to identify the most optimal model. This research applies the CRISP-DM methodology to build and compare predictive models for heart disease risk using three supervised learning algorithms: K-Nearest Neighbors (K-NN), Naïve Bayes, and Logistic Regression. The dataset used is a heart disease dataset obtained from the Kaggle platform, consisting of 10,000 records with variables such as Age, Blood Pressure, Smoking, Diabetes, Cholesterol, Triglyceride Level, Fasting Blood Sugar, and CRP Level. For the K-NN model, experiments were conducted using three values of k (k = 5, k = 10, and k = 20) to examine the effect of the number of neighbors on model performance. Meanwhile, the Naïve Bayes and Logistic Regression models were implemented using default parameters without additional tuning to ensure a consistent performance comparison. Model performance was evaluated using Accuracy and F1-Score metrics. The evaluation results indicate that the K-NN model with k = 5 achieved the best performance, with an accuracy of 0.7203 and an F1-Score of 0.7598, outperforming the Naïve Bayes and Logistic Regression models.
Co-Authors Chang, Rayna Shera Gunawan, Jason Miracle Kuswanto, Natalie Grace Widjaja Tedja, Jessica Laurentia Theresia Ratih Dewi Saputri