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Data Mining Technique to Analysis of Student Library Usage Behavior using Apriori Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 49 |
| Year of Publication: 2023 |
| Authors: Tewa Promnuchanont, Rujipan Kosarat |
10.5120/ijca2023922602
|
Tewa Promnuchanont, Rujipan Kosarat . Data Mining Technique to Analysis of Student Library Usage Behavior using Apriori Algorithm. International Journal of Computer Applications. 184, 49 ( Mar 2023), 13-17. DOI=10.5120/ijca2023922602
Abstract
The purpose of this research was to analyze student library access behavior by using association rule data mining. The data about students' access to library services is based on four factors: the number of times the service was accessed, total time of service, number of borrowed books and the cumulative grade point average of students. There are 84,977 data sets used in the experiment. The research method is divided into three steps: pre-processing or data preparation, data selection, data modification and data completion. Then, the data mining process was used to find association rules using an Apriori algorithm to analyze library service behavior that affects student grade point averages. Finally, post-processing was done to take the knowledge base obtained from the data mining process, to test and determine if it is correct or not. The results showed that students who had a high frequency of using the library, spent time in the library, and had a high frequency of borrowing books, had a good average score. Students who never borrowed library books, attended libraries less often, and spent less time in libraries had lower GPAs. It can be concluded that library use behavior affects students’ academic performance.
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