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All Journal JTIM : Jurnal Teknologi Informasi dan Multimedia Journal Of Artificial Intelligence And Software Engineering
Sujaka, Tomi Tri
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Computer Science & IT

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Comparative Analysis of Stock Price Prediction Using Deep Learning with Data Scaling Method Switrayana, I Nyoman; Hammad, Rifqi; Irfan, Pahrul; Sujaka, Tomi Tri; Nasri, Muhammad Haris
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.650

Abstract

The dynamic and unpredictable nature of stock prices makes accurate forecasting an important challenge in financial analysis. This study aims to compare the performance of three deep learning models, namely, Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) in predicting stock prices on historical daily banking data from Yahoo Finance. The main objective is to determine the model that is best able to capture sequential patterns and temporal dependencies in stock price movements. Each model was trained and op-timized through data scaling, namely MinMax Scaler and Standard Scaler, with performance evaluated using Root Mean Square Error (RMSE) as the primary metric. Results show that while the RNN provides a basic approach, the GRU and LSTM models produce higher prediction accuracy, with GRU achieving the lowest RMSE thanks to its better ability to maintain long-term depend-encies. The RMSE achieved by RNN, GRU, and LSTM were 211.47, 158.89, and 197.45, respectively. The lowest error results were achieved when using MinMax Scaler. The use of MinMax Scaler here shows a better performance improvement with an average improvement of 22.57% compared to using Standard Scaler. This comparative analysis contributes to providing empirical insight into the relative effectiveness of the tested architectures. The findings suggest that the combination of GRU and MinMax Scaler can be a more reliable tool for financial forecasting, with the potential to develop more robust stock prediction applications under fluctuating market conditions.
Development of an Android-Based Educational Game to Introduce Sumbawa's Art and Culture to Elementary School Students Attaqwa, M.Aswin Syarif; Hammad, Rifqi; Sujaka, Tomi Tri
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6937

Abstract

Technology is increasingly being used to facilitate work in various fields, including education. One of the uses of technology in education is as a learning medium. However, the use of learning media, especially technology-based media, is still very limited. This affects the learning process, where children often become easily bored due to a lack of interest in studying. Therefore, based on this issue, the researcher developed an Android-based educational game to help children learn about the art and culture of Sumbawa, specifically Satera Jontal (Sumbawa script) and traditional ceremonies of the Sumbawa region. This study uses the Multimedia Development Life Cycle (MDLC) method, which consists of six stages: concept, design, material collecting, assembly, testing, and distribution. Based on the results of media expert validation, a score of 92% was obtained; material expert validation scored 95%; and user testing produced a result of 82%. These results indicate that the developed Android-based educational game is suitable for use. Based on pretest results from users, the average score was 56.36, while the posttest results showed an average score of 74.24. These results demonstrate that the developed educational game can improve student learning outcomes
Co-Authors Attaqwa, M.Aswin Syarif Hammad, Rifqi I Nyoman Switrayana Muhammad Haris Nasri Pahrul Irfan