Determining Appropriate Classification Method Based on Influential Factors for Predicting Students'Academic Success

Ermatita, Ermatita (2022) Determining Appropriate Classification Method Based on Influential Factors for Predicting Students'Academic Success. In: 2022 International Conference on Data Science and Its Applications (ICoDSA), 06-07 July 2022, Bandung.

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Abstract

The need for accuracy in a prediction is a non-negotiable thing. One of the determinants of the accuracy of a prediction model is the classification method. Data mining offers various classification methods for predicting. Therefore, determining appropriate classification methods that produce high accuracy prediction model is a must. Several previous studies have shown excellent results based on influential factors for predicting students’ academic success. However, the research only focuses on one influential factor category rather than a combination of multiple influential factor categories. It becomes a serious issue since there are influential factors on the dataset that not only have one influential factor category but mostly multiple factor categories. Therefore, the best classification method for a multiple influential factor category has not been known yet. This research analyzes the performance of classification methods based on multiple categories of influential factors. The result will help the researcher find the best combination of factor category and classification method should they used. Among multiple factor category and classification methods have been tested show combination of certain classification method give the best result for certain multiple factor category.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Dr Ermatita zuhairi
Date Deposited:	07 Apr 2023 12:34
Last Modified:	07 Apr 2023 12:34
URI:	http://repository.unsri.ac.id/id/eprint/94204

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