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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Kusuma Dewa, Chandra
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Computer Science & IT Decision Sciences, Operations Research & Management

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Prediction of Student Performance Based on Behavior using E-Learning During the Covid-19 Pandemic using Support Vector Machine Widarta, Agung Eka; Luthfi, Ahmad; Kusuma Dewa, Chandra
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.12857

Abstract

The COVID-19 crisis has profoundly impacted many sectors globally, including education, necessitating the shift from traditional in-person learning to independent or online learning through various digital platforms. The integrity of e-learning can be ensured by leveraging e-learning behavioral data. The objective of this research is to develop a novel data model to navigate the educational challenges of the COVID-19 era. Previous studies employed the Support Vector Machine (SVM) technique to predict student performance in an e-learning setting, yet they failed to contrast different SVM kernels and their outcomes. In contrast, this study uses SVM and compares three types of kernels: Radial, Polynomial, and Linear. The dataset used for this research was procured from X-API-Edu-Data. The SVM technique was utilized in a unique way to process the data, which comprised 17 variables and 40 observations. Notably, all 17 variables were character variables, with only four being numeric. Two variables, Raisedhands and Discussion, were selected for analysis due to their key role in effective learning and their association with student performance in an e-learning environment. The evaluation of the model was performed using the Topic variable, which represents the subjects in the dataset. The research findings revealed a marked improvement in accuracy compared to earlier studies. Among the three SVM kernels tested - Radial, Polynomial, and Linear, the Polynomial kernel demonstrated superior accuracy with a score of 0.9979. Therefore, the Polynomial model was deemed most appropriate for analyzing the Topic variable. In conclusion, the study indicates that the application of the e-learning method, specifically during the COVID-19 pandemic, proved highly effective in forecasting student performance.
Implementasi Unity-Gymnasium sebagai Alternatif Metode Reinforcement Learning dalam Pengembangan Environment Sliding Puzzle menggunakan Game Engine Unity Julpian Alwi, Agus; Kusuma Dewa, Chandra
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.7008

Abstract

Artificial intelligence (AI) plays an important role in the gaming industry, its application commonly found in non-player character (NPC) and procedural content generation (PCG). One of the popular methods for developing AI is reinforcement learning (RL). Unity, as one of the most dominant game engines, has its own RL framework called Unity ML-Agents Toolkit. Unity with its capabilities to simulate realistic environments. allowing Unity ML-Agents Toolkit to develop and test RL agents in various complex scenarios. However, Unity ML-Agents Toolkit only has limited RL algorithms. This study aims to introduce an alternative method for implementing reinforcement learning in Unity, while addressing the Unity ML-Agents Toolkit’s limitations. The proposed method is Unity-Gymnasium, which integrates Unity with Gymnasium, and tested by developing a sliding puzzle environment. The result of this study demonstrates that the Unity-Gymnasium method works well and allows access to total 38 different RL algorithms from various RL libraries that compatible with Gymnasium, such as Stable Baseline 3, CleanRL, Tianshou, Ray Rllib, and Dopamine, this number is significantly higher compared to Unity ML-Agents Toolkit which only offer five RL algorithm options.
Co-Authors Ahmad Luthfi Julpian Alwi, Agus Widarta, Agung Eka