Naive bayes classification and rapidminer application for analysis of lecturer institution's performance

Raja, Harmonvikler Dumoharis Lumban, Sunandar, Muhamad Agus, Azlina, Yunidyawati, Amrullah, Abdul Malik Karim, Supriyono, S. ORCID: https://orcid.org/0000-0002-4733-9189, Fauziningrum, Endah and Kundori, K. (2024) Naive bayes classification and rapidminer application for analysis of lecturer institution's performance. AIP Conference Proceedings, 3065 (1). ISSN -

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Abstract

Customer satisfaction is an essential measure that may be used to compare service performance obtained and expected by customers. The purpose of this study is to analyze lecturer satisfaction with an institution’s performance using artificial intelligence approaches. The dataset was produced via observations and questionnaires distributed to instructors. The solution approach was data mining classification with the naive Bayes algorithm. In the analysis process, Rapidminer software is employed. Based on the results of the final test with the traits of Alertness (criterion 1), Empathy (criterion 2), Reliability (criterion 3), and Responsibility, the accuracy rate was 85.48 percent, with a Precision value of 81.08 percent and a Recall value of 93.75 percent (criterion 4).

Item Type:	Journal Article
Keywords:	data mining; artificial intelligence; machine learning; educator
Subjects:	09 ENGINEERING > 0906 Electrical and Electronic Engineering
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Mufid Mufid
Date Deposited:	06 Dec 2024 15:29

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