Data Mining Analysis to Predict Student Skills Using Naïve Bayes Method

Authors

  • Yaslinda Lizar UIN Imam Bonjol Padang, Indonesia
  • Alya Sahira Firrizqi UIN Imam Bonjol, Indonesia
  • Asriwan Guci STIKes MERCUBAKTIJAYA Padang, Indonesia
  • Joko Sunadi Universitas Putra Indonesia "YPTK" Padang, Indonesia

DOI:

https://doi.org/10.30983/knowbase.v3i2.7481

Keywords:

Data Mining, Naïve Bayes Method, Data Classification, Students, Skills

Abstract

The possession of specific skills by students not only has a positive impact on the students themselves but also on the Study Program within a Faculty and the University as a whole. However, Study Programs sometimes face difficulties in determining the skills of numerous students even after they have completed 7 semesters of study. Therefore, a method to extract available data in order to determine student skills quickly and accurately is essential. This research aims to apply a data mining method to predict student skills in the Information Systems Study Program at UIN Imam Bonjol Padang. The study focuses solely on predicting student skills in the fields of data processing and programming. The method employed in this data mining analysis is the Naïve Bayes method. Data will be collected from student course grades related to data processing and programming. The data will be processed using an application and subsequently tested using a Confusion Matrix. The research results indicate that predicting the determination of student skills in the Information Systems Study Program at UIN Imam Bonjol can be achieved using the Naïve Bayes algorithm, which yielded a Naïve Bayes model accuracy of 93%, precision of 81%, and recall of 81%. The obtained model can be implemented in the form of an application to determine decision-making strategies for students.

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Published

2023-12-31

How to Cite

Lizar, Y., Firrizqi, A. S., Guci, A., & Sunadi, J. (2023). Data Mining Analysis to Predict Student Skills Using Naïve Bayes Method. Knowbase : International Journal of Knowledge in Database, 3(2), 150–159. https://doi.org/10.30983/knowbase.v3i2.7481

Issue

Vol. 3 No. 2 (2023): December 2023

Section

Articles

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