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All Journal JOIV : International Journal on Informatics Visualization
Sato-Shimokawara, Eri
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Computer Science & IT

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Prediction Analysis of Greeting Gestures Based on Recurrent Neural Networks Wibowo, Angga; Kurnianingsih, -; Sato-Shimokawara, Eri
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2917

Abstract

Human activity recognition, such as rehabilitation, sports, human behavior, etc., is developing rapidly. A Recurrent Neural Network (RNN) is a practical approach to human activity recognition research and sequential data. However, studies on recognizing human activities rarely study culture, including greeting gestures. And studies seldom use small datasets when employing the RNN approach, as they typically utilize large amounts of data to conduct such studies. This study aims to predict greeting gestures from Japan and Indonesia with limited data. This study proposes and compares six RNN architecture methods, including Long Short-Term Memory (LSTM), Bidirectional RNN (BRNN), Gated Recurrent Unit (GRU), Vanilla RNN (VRNN), Deep RNN (DRNN), and Hierarchical RNN (HRNN), which have been modified with regularization to handle overfitting. We evaluate using Mean Squared Error (MSE), Root Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). The experimental results show that LSTM has the best MSE, RMSE, and MAE values, with MSE of 0.0773479, RMSE of 0.2781149, and MAE of 0.2402451, while GRU has the best R² value of 0.0267571. The conclusion of this study indicates that LSTM and GRU are more suitable than other models for solving this problem. Therefore, it can be beneficial for future research to address the challenges of small data and overfitting in sequential data and human activity recognition, particularly in the context of greeting gestures. Future work can utilize data augmentation, proper parameter selection, and incorporate data from multiple individuals to enhance the accuracy of the model.
(PANDEMIC Covid-19): A Shooter Game for Education - the Impact Measurement of War Games on Virus Eradication Lessons for Students Wibowo, Angga Wahyu; Karima, Aisyatul; Thohari, Afandi Nur Aziz; Santoso, Kuwat; Sato-Shimokawara, Eri
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1167

Abstract

(PANDEMIC Covid-19) is an educational shooter game inspired by the Covid-19 pandemic which occurred from the end of 2019 until early 2022. There are 2 game modes, namely Third-Person Shooter, or TPS, and First-Person Shooter, or FPS. This study was carried out to highlight the absence of a shooter genre game used in the student learning process. The research methodology in the development of this game applied the pressman method, and the stages include planning, analysis, game development and artificial intelligence, implementation, as well as  evaluation. Furthermore, the testing phase used software testing techniques based on the ISO 9126 standard and involved a total of 100 participants. The age range was between 17 and 20 years, while the participants' gender percentages were 55% male and 45% female. Some of the factors tested include functionality, reliability, portability, usability, efficiency, and maintainability. There were 2 choices only in this test, i.e. agree and disagree. The functionality factor had an agreed rate of 85%; reliability 79%, portability 86%, usability 83%, efficiency 79%, and maintainability 87%. Therefore, it was concluded that this game is suitable for use in student learning in the shooter genre. Furthermore, this research was inspired because shooter games have not been developed for the student learning process. This game genre is currently used for hobbies and for profit by developers and professional players. Further research should develop game levels, enable features to play online together with other users, and should be extended to Android and IOS. 
Co-Authors Afandi Nur Aziz Thohari, Afandi Nur Aziz Aisyatul Karima Kurnianingsih, - Santoso, Kuwat Wibowo, Angga Wibowo, Angga Wahyu