Naïve Bayes and Bidirectional Algorithm Analysis: Encoder Representations From Transformers (BERT) to Teachers' Learning Services to Students Based on the Website of SMK Multi Karya School

Abstract

This study analyzes the comparison of two algorithms, namely Naive Bayes and Bidirectional Encoder Representations From Transformers (BERT), for the evaluation of the performance of education personnel at SMK MULTI KARYA This study uses manual calculation methods and the Python application. The results showed that the Naive Bayes algorithm gave very consistent results with accuracy, precision, and recall values of 76.67% both in manual calculations and with Pyton. This indicates that the Naive Bayes algorithm is effective in grouping data on the performance of education personnel. Meanwhile, the Bidirectional Encoder Representations From Transformers (BERT) algorithm shows mixed results, while with Python it reaches 12.00%. There are significant differences in recall values and precision between these two calculation methods. Nevertheless, the performance category "Good Performance Staff" remains the most dominant. The difference in results between manual and python calculations is that Naive bayes is a more stable and consistent method across different platforms, whereas Bidirectional Encoder Representations From Transformers (BERT) shows flexibility but with smaller variation in results. Therefore, in the context of education performance evaluation, NAive bayes are more reliable to produce consistent performance categories, while Bidirectional Encoder Representations From Transformers(BERT) can be an alternative with a fairly high level of accuracy but require further consideration in the interpretation of the results..