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All Journal Journal of Soft Computing Exploration Jurnal Penelitian Pendidikan
Dewi, Meilina Taffana
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Computer Science & IT Education

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Implementation of fuzzy tsukamoto in employee performance assessment Dewi, Meilina Taffana; Zaaidatunni'mah, Untsa; Al Hakim, M. Faris; Jumanto, Jumanto
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.52

Abstract

Employees are one of the important things for the sustainability of a company, because employees are company assets. In addition, employee performance is also something that cannot be ignored because it determines the achievement of company goals. So it is important to monitor employee performance and conduct performance appraisals. With the addition of performance appraisal, the company can determine the provision of rewards, promotions, and punishments. It can be used as a work evaluation stage to improve the quality of work. Employee performance appraisal is based on several predetermined criteria, including responsibility, discipline, and attitude which in the end results in between two linguistic values, namely good or bad. One method for evaluating employee performance is the Tsukamoto fuzzy method. With the Tsukamoto fuzzy method, it is hoped that the assessment can be carried out fairly and measurably.
Prediction of Blood Sugar Levels in Type 2 Diabetes Mellitus Patients Based on Diet and Medication Compliance Using Naive Bayes and BAT Algorithms Dewi, Meilina Taffana; Putra, Anggy Trisnawan
Jurnal Penelitian Pendidikan Vol. 42 No. 2 (2025): October 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpp.v42i2.32305

Abstract

Type 2 diabetes mellitus poses a significant global health especially in Indonesia  challenge, primarily due to patient non-adherence and limited monitoring. Therefore,  technology-based approaches play a crucial role in detecting potential blood sugar  elevations early, enabling faster and more targeted interventions. This study introduces an integrated predictive framework that combines a Naive Bayes classification algorithm with a Bat-inspired metaheuristic (BAT) for automated feature selection. Optimized by the BAT algorithm, the system achieved high performance: 95% accuracy, 0.94 precision, 0.96 recall, 0.95 F1 score, and 0.90 Cohen's Kappa, indicating near-perfect agreement with actual outcomes. These results confirm the potential of the Naive Bayes and BAT approaches as reliable clinical decision support tools for proactive diabetes management.
Co-Authors Al Hakim, M. Faris Jumanto Jumanto, Jumanto Putra, Anggy Trisnawan Zaaidatunni'mah, Untsa