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All Journal Jurnal E-Komtek
Noka Prakasa Rhomadona
Institut Teknologi PLN
Computer Science & IT Electrical & Electronics Engineering

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Integrasi Fuzzy Sugeno & Fuzzy Tsukamoto untuk Readiness Deployment Aplikasi Perbankan berbasis dokumen checklist QA Noka Prakasa Rhomadona; Luqman
Jurnal E-Komtek Vol 10 No 1 (2026)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v10i1.2946

Abstract

This research proposes a decision support expert system for the readiness deployment process based on a QA checklist by integrating the Fuzzy Tsukamoto and Fuzzy Sugeno methods. This research is very necessary because application release failures to production still often occur due to inconsistent testing and reliance on the experience of Quality Assurance (QA), especially in the workplace of the researcher. The variables used for analysis are five, namely error handling, multi-platform testing, performance testing, defect handling, and UAT results. Fuzzification is mapped into 3 categories: LOW/MEDIUM/HIGH, which is implemented using a trapezoid function; Tsukamoto output is in the form of a monotonous curve shaped L/R (rising or falling). Sugeno uses constants or linear functions. In this study, an internal dataset was used with a total of 500 data points, with 185 ready data, 220 partially ready data, and 95 not ready data. I performed numerical mapping on the fuzzification categories [LOW, MEDIUM, HIGH] as [0,30, 0,60, 0,90], for a weight λ of 0.70 and showed an accuracy of 83.8%. This system provides consistent recommendations and conservative safeguards against critical conditions before production release.
Comparative Analysis of Expert System Methods for Early Diagnosis of Online Game Addiction A Systematic Review Noka Prakasa Rhomadona; Luqman
Jurnal E-Komtek Vol 9 No 2 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i2.2699

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.
Co-Authors Luqman