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All Journal International Journal of Business, Economics, and Social Development International Journal of Research in Community Services Operations Research: International Conference Series International Journal of Quantitative Research and Modeling International Journal of Ethno-Sciences and Education Research International Journal of Mathematics, Statistics, and Computing International Journal of Humanities, Law, and Politics International Journal of Health, Medicine, and Sports International Journal of Linguistics, Communication, and Broadcasting
Pirdaus, Dede Irman
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Journal : International Journal of Mathematics, Statistics, and Computing

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Analysis Testing Black Box and White Box on Application To-Do List Based Web Pirdaus, Dede Irman; Hidayana, Rizki Apriva
International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 2 (2024): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v2i2.95

Abstract

The rapid development of information technology has led to the creation of numerous web-based applications designed to assist human activities and work. One such application is the To-Do List, which helps users manage their tasks and increase productivity. This study aims to analyze the quality of web-based To-Do List applications through black box and white box testing. The research focuses on the login and main pages of the application, where various scenarios are tested to ensure that the system functions as intended. The testing process includes designing test scenarios, creating test cases, executing the test cases, and collecting and processing test result data. The study also includes an analysis of the program's source code using flowcharts and flowgraphs to identify the number of independent logic execution paths and design test cases for white box testing. The results of the testing will help identify errors and weaknesses in the application, ensuring that the final product is of high quality.
Comparison of Activation Functions in Recurrent Neural Network for Litecoin Cryptocurrency Price Prediction Saputra, Moch Panji Agung; Azahra, Astrid Sulistya; Pirdaus, Dede Irman
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 3 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v3i3.233

Abstract

The rapid advancement of information technology and digitalization has significantly transformed the financial sector, particularly with the emergence of cryptocurrencies characterized by high price volatility and complex movement patterns. Accurate price prediction of these crypto assets is essential to support investment decision-making and risk management. This study aims to compare the performance of six activation functions ReLU, Tanh, Sigmoid, Softplus, Swish, and Mish in a Simple Recurrent Neural Network (RNN) model for predicting the price of Litecoin, a widely traded cryptocurrency. Using historical daily closing price data from May 2020 to April 2025, the data were preprocessed through Min-Max normalization and sliding window sequence formation to fit the RNN input requirements. Each activation function was applied in the RNN model under consistent training conditions, and model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²). Results indicate that the Swish activation function outperforms others by achieving the lowest RMSE of 4.58 and the highest R² score of 0.9578, demonstrating superior prediction accuracy and stable convergence. Tanh also showed competitive results, while Sigmoid and Softplus performed less effectively. In conclusion, Swish is recommended as the most suitable activation function for RNN-based cryptocurrency price forecasting due to its balance of accuracy and computational efficiency.
Co-Authors Agung Prabowo Amal, Moh Alfi AS, Nurnisaa Azahra, Astrid Sulistya Chayandari, Rini Dwiputra, Muhammad Bintang Eighista Hidayana, Rizki Apriva Ibrahim, Riza Andrian Ismail, Muhammad Iqbal Al-Banna kalfi kalfin Kalfin Kalfin, Kalfin Laksito, Grida Saktian Lianingsih, Nestia Rusyn, Volodymyr Saefullah, Rifki Salih, Yasir Saputra, Jumadil Saputra, Renda Sandi Sidiq, Fahmi Suhaimi, Nurnisaa binti Abdullah Surya, Dhika Vera Yohandoko, Setyo Luthfi Yohandoko, Setyo Luthfi Okta Yuningsih, Siti Hadiaty