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All Journal Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Selvy megira
universitas serelo lahat
Computer Science & IT

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Pengembangan E-Learning Berbasis Chatbot AI untuk Pembelajaran IPA Inklusif di SD Lahat Selvia Damayanti; Selvy megira
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/zd3rgy33

Abstract

The advancement of Artificial Intelligence (AI) has significantly influenced various fields, including education. In the digital era, integrating technology into education has become indispensable, particularly in fostering inclusive and adaptive learning systems. The urgency of this study arises from challenges in elementary-level Science (IPA) learning, where students frequently struggle to comprehend material outside school hours. Limited instructional time, disparities in students’ understanding, and the scarcity of interactive media remain key obstacles in the learning process. To address these issues, this research proposes the development of an AI-based chatbot integrated into an e-learning platform as an innovative solution to support Science learning in elementary schools within Lahat Regency. The chatbot is designed to function as a virtual learning assistant, capable of providing real-time responses, delivering adaptive explanations, and enhancing student engagement through personalized interaction. This study adopts a Research and Development (R&D) methodology, employing the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). Each development phase involves both students and teachers to ensure alignment with learning needs and to evaluate the effectiveness of the chatbot in the Science learning context. The expected outcome of this research is the establishment of an AI-based chatbot learning medium that enhances students’ comprehension, facilitates independent learning, and offers an alternative strategy to mitigate teachers’ limitations in Science instruction. Ultimately, this study contributes to advancing inclusive, interactive, and adaptive innovations in elementary education within the digital era.  
AI-Powered GRU untuk Prediksi Saham Syariah Indonesia Berbasis Analisis Multi Faktor Selvy Megira; Arief Zikry; Nina Dwi Putriani
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/5dfrdm67

Abstract

This study aims to develop a predictive model for Indonesian Sharia stock prices using the Gated Recurrent Unit (GRU) algorithm with a multi-factor analysis approach. The main challenge in analyzing Sharia stocks lies in the high volatility influenced by fundamental, technical, bandarmology, and macroeconomic factors. GRU was chosen because it has a simpler structure compared to LSTM while remaining effective in processing complex time-series data. The dataset includes variables such as EPS, PER, PBV, ROA, ROE, MA, RSI, MACD, foreign buy/sell, and the IHSG index, normalized using the Min-Max Scaler. The results show that the GRU model achieves high predictive accuracy, with a MAPE of 0.0286 and an RMSE of 74.92. Visualizations including training vs validation loss, scatter plots, residual plots, and error distribution confirm that the model avoids overfitting and generalizes well. Furthermore, the deployment of an interactive interface based on Gradio enables real-time prediction simulations, making this research not only academically significant but also practically useful for investors and policymakers in the Sharia capital market.
Explainable Boosted Ensemble Penjualan Video Game Release Tahun 1980-2020 Nina Dwi Putriani; Yusi Nurmala Sari; Selvy Megira
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/8a771z88

Abstract

This study aims to analyze video game sales trends of game released on 1980 to 2020 using the Explainable Boosted Ensemble approach. The XGBoost algorithm was selected for its strong predictive ability on tabular data, while SHAP integration provides transparency regarding the factors influencing predictions. The dataset includes variables such as genre, platform, publisher, and both regional and global sales, enabling a comprehensive analysis of market preferences in North America, Europe, Japan, and other regions. Findings reveal that regional sales, particularly in North America and Europe, contribute most significantly to global sales, while Japan shows dominance in Role-Playing and Platform genres. Model evaluation produced an R² score of 0.7788, indicating reliable accuracy in explaining sales variations. Furthermore, genre recommendations highlight Platform, Shooter, and Role-Playing as the backbone of the industry, with Action, Racing, Fighting, and Sports remaining relevant in specific segments. This research is expected to provide both academic and practical contributions, offering insights for developers to design more effective distribution strategies and genre portfolios.
Prediksi Risiko Penyakit Jantung dengan Decision Tree yang Dioptimasi Algoritma Bald Eagle Search Yusi Nurmala Sari; Selvy Megira; Salamudin Salamudin
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/d0q2c524

Abstract

Heart disease remains one of the leading causes of death worldwide, making early detection of its risk crucial to reducing mortality and morbidity rates. This study aims to develop a heart disease risk prediction model based on machine learning using a Decision Tree algorithm optimized with Bald Eagle Search (BES). The research employed a quantitative approach utilizing a clinical dataset containing demographic and medical variables such as age, gender, blood pressure, cholesterol levels, electrocardiographic results, and heart disease status. The baseline Decision Tree model was compared with the BES-optimized model (BES-DT) through evaluations of accuracy, confusion matrix, prediction probability distribution, feature importance analysis, and learning curves with respect to the max_depth parameter. The analysis revealed that the baseline Decision Tree achieved an accuracy of 70.5%, with 43 correct predictions out of 61 test samples, while the BES-DT model achieved an accuracy of 68.9%, with 42 correct predictions. Although the overall accuracy showed a slight decrease, BES-DT demonstrated greater consistency in identifying at-risk patients, with fewer misclassifications (4 cases compared to 7 in the baseline). Furthermore, the prediction probability distribution in BES-DT was more stable, with values concentrated near 0 and 1, indicating higher confidence in classification. The feature importance analysis highlighted chest pain type, oldpeak, and thal as dominant variables in risk classification. The learning curve confirmed that BES-DT reduced the risk of overfitting and improved the model’s generalization capability. This study contributes to the development of more accurate and interpretable machine learning classification methods in healthcare. Future work may involve testing the model on larger and more diverse datasets, integrating other optimization algorithms for performance comparison, and implementing web-based or clinical applications to support medical decision-making.
Co-Authors Arief Zikry damayanti, selvia Nina Dwi Putriani salamudin salamudin Yusi Nurmala Sari