K-Means and Naive Bayes Algorithms for Evaluation of Education Personnel Performance Based on SPMI Standards
DOI:
10.33395/sinkron.v8i3.13890Keywords:
Performance evaluation, educational staff, K-Means, Naive Bayes, SPMIAbstract
This research compares the K-Means and Naive Bayes algorithms in evaluating the performance of educational staff based on SPMI standards at STMIK Triguna Dharma. The main objective is to identify the effectiveness of the two algorithms in grouping performance evaluation data and determine the advantages and disadvantages of each method. Primary data was obtained through surveys and interviews, while secondary data came from institutional archives. The K-Means algorithm shows 100% accuracy with the ability to group educational staff into very good, good, quite good, poor and poor performance categories. Meanwhile, the Naive Bayes algorithm shows 91% accuracy, with 100% precision results for the "good" and "fairly good" categories. These results indicate that K-Means is more effective in grouping educational staff based on performance evaluation compared to Naive Bayes. This research makes a significant contribution in the field of evaluating the performance of educational staff and offers insights for a more effective implementation of SPMI in higher education.
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Copyright (c) 2024 Eko Wahyudi, Rian Farta Wijaya, Khairul
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Muhammad Khoiruddin harahap
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