Comparative Analysis of Expert System Methods for Early Diagnosis of Online Game Addiction A Systematic Review
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Abstract
The phenomenon of online game addiction has become a serious problem that requires early detection and prevention. This study aims to analyze and compare several expert system methods that have been applied to diagnose online game addiction, to provide an overview of the most effective and efficient methods. The methods analyzed include Backward Chaining, Forward Chaining, Certainty Factor, Euclidean Distance, Fuzzy Tsukamoto, Fuzzy Sugeno, Fuzzy Mamdani, Case-Based Reasoning, and K-Nearest Neighbor. Based on a review of 15 literature sources, the results of the analysis show that the diagnostic accuracy of each method varies. Certainty Factor achieves an accuracy of 79-81.2%, the combination of Certainty Factor and Forward Chaining reaches 99.64%, Backward Chaining and Certainty Factor at 80%, while Fuzzy Tsukamoto with Fuzzy Sugeno and Case-Based Reasoning is able to achieve perfect accuracy of 100%. The conclusion of this study shows that hybrid methods that combine fuzzy inference and reasoning techniques, such as Fuzzy Tsukamoto-Fuzzy Sugeno and Case-Based Reasoning, show a very high level of accuracy and have the potential to be the most effective approach for an expert system for diagnosing online game addiction, mimicking the diagnostic capabilities of a psychologist.
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