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Framework of Mobile Game Design as an Assistive Technology for Children with Motor Disabilities Melyani Melyani; Yaya Heryadi; Agung Trisetyarso; Bachtiar Saleh Abbas; Wayan Suparta; Ford Lumban Gaol
Journal of Games, Game Art, and Gamification Vol. 4 No. 1 (2019): Special Issue: International Conference of Games, Game Art and Gamification (IC
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v4i1.7460

Computer games have emerged in the past decade as potential media beyond entertainment. Despite its popularity, game accessibility remains a major concern of various researchers. Children population with motor disabilities is a potential target for developing entertainment or therapeutic support games due to their interest to play. This paper presents: (1) a framework for mobile games for children with motor disability using simple hand postures and (2) Xgboost decision tree as a hand posture recognizer (98.48 percent training accuracy and 96.76 percent testing accuracy) as a prototype of hand posture-based commands as assistive technology to interact with games.

Predicting the Effect of Violent Gameplaying to Violent Behavior Intention among Females using Tree Regression and AdaBoost Tree Regression Maniah Maniah; Yaya Heryadi; Agung Trisetyarso; Bachtiar Saleh Abbas; Wayan Suparta; Ford Lumban Gaol
Journal of Games, Game Art, and Gamification Vol. 4 No. 2 (2019): Special Issue: International Conference of Games, Game Art and Gamification (IC
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v4i2.7468

The issue on the effect of violent video game to aggressive behavior has gained wide interest from various communities. This paper presents some results of predicting quantitative measure of aggressive behavior from variables that measure violent video game playing. Experiment results showed that Decision Tree Regression (DTR) and Adaptive Boosting Tree Regression (AB-DTR) models predicted aggressive behavior intentions with high accuracy. For predicting Hostile variable: DTRâ€™s training and testing RMSE (0.0, 0.0); AB-DTRâ€™s training and testing RMSE (0.08, 1.08). For predicting Instru variable: DTRâ€™s training and testing RMSE (0.0, 2.18); AB-DTRâ€™s training and testing RMSE (0.0, 3.30) respectively.

Gamification Framework for Programming Course in Higher Education Winanti Winanti; Bachtiar Saleh Abbas; Wayan Suparta; Yaya Heryadi; Ford Lumban Gaol
Journal of Games, Game Art, and Gamification Vol. 5 No. 2 (2020): Special Issue: International Conference of Games, Game Art and Gamification (IC
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v5i2.7479

This paper presents a gamification framework for higher education, especially for programming language courses to increase user motivation, pleasure and satisfaction so that learning objectives can be achieved. Although student and lecturer motivation, pleasure, and satisfaction tend to increase compared to conventional techniques, gamification is not a panacea. The success of its application depends on the skill of the lecturer in choosing a game mechanic to give a sense of playing to the learning process in a more interesting way. The technique is done by dividing the class into two parts where one class uses the conventional method and one class uses the gamification method and the results will be evaluated through the assessment results before using the gamification method and after using the gamification method. The framework in this paper adds to the existing framework activities, namely adding in the field of baseline analysis, learning materials and tools used in gamification, where previous papers from three activities have not been discussed in detail. The results obtained turned out that using the gamification technique of student learning outcomes on average 15 to 25 better than using conventional techniques.

Comparison of Xbox One and Steam Joystick-based Operating System User Interface using KLM- GOMS Dodick Zulaimi Sudirman; Yaya Heryadi; Harco Leslie Hendric Spits Warnars; Benfano Soewito; Ford Lumban Gaol; Edi Abdurachman
Journal of Games, Game Art, and Gamification Vol. 5 No. 2 (2020): Special Issue: International Conference of Games, Game Art and Gamification (IC
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v5i2.7480

Despite having many advances in video game control such as voice or gesture control, most video game console manufacturers still use a joystick as the main control for their console. Currently, the problem with video game manufacturers is that instead of using the joystick as the point of user interface design, they instead used a design from another device such as a computer or tv. The research will compare Xbox One and Steam OS user interface by using KLM-GOMS Model. Based on the calculation it is concluded that the overall Xbox One has a more efficient design compared to Steam.

Penerapan Convolutional Neural Network Dan DenseNet121 untuk Identifikasi Penyakit Daun Jagung Di Daerah Toba Hutapea, Oppir; Ford Lumban Gaol; Takuro Matsuo
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8646

Corn is one of the most important agricultural commodities in the Toba region of North Sumatra. However, its productivity is often reduced due to foliar diseases that appear prior to harvest. The three most commonly observed leaf diseases include leaf spot, blight, and rust. To support early detection efforts among local farmers, this study proposes an image-based classification system employing the Convolutional Neural Network (CNN) algorithm and the DenseNet121 model as a transfer learning approach. The primary objective of this research is to automatically identify the type of disease affecting corn leaves using image data, thereby enabling farmers to promptly implement appropriate countermeasures. A series of experiments were conducted to evaluate various model configurations, including different activation functions (ReLU and Tanh), adjustments to learning rates, and the tuning of other hyperparameters such as optimizers and preprocessing methods (normalization, rotation augmentation, zooming, and contrast adjustments). The results demonstrate that DenseNet121, when trained with an optimal learning rate of 0.001, achieved the highest accuracy of 97%, outperforming the custom-built CNN model which attained an accuracy of 95%. The combination of effective preprocessing techniques and hyperparameter tuning significantly contributed to the improved performance of the models. This study highlights the potential of image-based plant disease detection technologies in agriculture, particularly in aiding real-time decision-making, enhancing land management efficiency, and supporting increased corn yield.

Penerapan Convolutional Neural Network Dan DenseNet121 untuk Identifikasi Penyakit Daun Jagung Di Daerah Toba Hutapea, Oppir; Ford Lumban Gaol; Takuro Matsuo
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8646

Corn is one of the most important agricultural commodities in the Toba region of North Sumatra. However, its productivity is often reduced due to foliar diseases that appear prior to harvest. The three most commonly observed leaf diseases include leaf spot, blight, and rust. To support early detection efforts among local farmers, this study proposes an image-based classification system employing the Convolutional Neural Network (CNN) algorithm and the DenseNet121 model as a transfer learning approach. The primary objective of this research is to automatically identify the type of disease affecting corn leaves using image data, thereby enabling farmers to promptly implement appropriate countermeasures. A series of experiments were conducted to evaluate various model configurations, including different activation functions (ReLU and Tanh), adjustments to learning rates, and the tuning of other hyperparameters such as optimizers and preprocessing methods (normalization, rotation augmentation, zooming, and contrast adjustments). The results demonstrate that DenseNet121, when trained with an optimal learning rate of 0.001, achieved the highest accuracy of 97%, outperforming the custom-built CNN model which attained an accuracy of 95%. The combination of effective preprocessing techniques and hyperparameter tuning significantly contributed to the improved performance of the models. This study highlights the potential of image-based plant disease detection technologies in agriculture, particularly in aiding real-time decision-making, enhancing land management efficiency, and supporting increased corn yield.

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