OPTIMALISASI KINERJA TENAGA KEPENDIDIKAN DI MTSN 1 LABUHANBATU SELATAN STUDI KASUS PENGGUNAAN ALGORITMA NAÏVE BAYES

AIDA ZAHRAH HASANATI BR RAMBE, NPM 2109100093 (2025) OPTIMALISASI KINERJA TENAGA KEPENDIDIKAN DI MTSN 1 LABUHANBATU SELATAN STUDI KASUS PENGGUNAAN ALGORITMA NAÏVE BAYES. Tugas_Akhir(Artikel) Journal of Computer Science and Information Systems (JCoInS), 6 (3). pp. 395-408. ISSN 2747-2221(e-ISSN)

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Abstract

This study aims to optimize the performance of Education personnel in MTsN 1 Labuhanbatu Selatan through the application of Naive Bayes algorithm for performance classification. The performance of Education personnel, including administrative, administrative, and service staff for one school year was analyzed using data involving attributes such as attendance, punctuality, productivity, and work attitude. Naive Bayes algorithm was chosen because of its ability to classify data accurately and efficiently despite the large amount of data. The results showed that the use of this algorithm can produce a more objective, accurate, and data-based evaluation system, as well as provide clearer insights in improving work efficiency and service to teachers and students. The evaluation of the model was conducted using accuracy, precision, recall, and F1-score metrics to ensure that the classification of educational staff performance can be done appropriately. The study also provides recommendations to improve data quality and the use of additional attributes to improve model performance. Keywords : Education Personnel Performance, Naive Bayes, Performance Evaluation, Data mining, Performance Classification

Item Type:	Article
Uncontrolled Keywords:	Education Personnel Performance, Naive Bayes, Performance Evaluation, Data mining, Performance Classification
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	22 Oct 2025 07:21
Last Modified:	22 Oct 2025 07:21
URI:	http://repository.ulb.ac.id/id/eprint/1833

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