WIJAYA, STEFANUS KHRISTIAN (2023) COMPARISON OF KMEDOIDS AND FUZZY CMEANS CLUSTERING ALGORIHTM TO PREDICT STUDENTS STUDY PERIOD. Other thesis, UNIVERSITAS KHATOLIK SOEGIJAPRANATA.
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Abstract
An ontime graduation rate is an important element for good accreditation. Therefore, it is necessary to monitor and evaluate the tendency of students to graduate on time or not. To overcome the above problems, help is needed from data mining that can predict the study period of students. KMedoids and Fuzzy CMeans are clustering algorithms that can be used to predict student study period. The data used in this study is dummy data that is similar to the original data. The amount of dataset is 1000. The results of the algorithms used are evaluated using the Silhouette Index validation method so that the accuracy of the prediction can be known. The results of the method above in the KMedoids algorithm of 1000 predicted students, there are 438 students who do not graduate on time and 562 students graduate on time. The results of the Fuzzy CMeans algorithm are 419 students who did not graduate on time and 581 students who graduated on time. The best results of the evaluation show that the KMedoids algorithm has an accuracy of 62.54% and the Fuzzy CMeans algorithm has an accuracy of 63.85 %. Fuzzy CMeans algorithm is better at predicting student study period with an accuracy that is 1.31% higher than the KMedoids algorithm.
Item Type:  Thesis (Other) 

Subjects:  000 Computer Science, Information and General Works > 004 Data processing & computer science 
Divisions:  Faculty of Computer Science > Department of Informatics Engineering 
Depositing User:  Mr Yosua Norman Rumondor 
Date Deposited:  05 Oct 2023 06:22 
Last Modified:  05 Oct 2023 06:22 
URI:  http://repository.unika.ac.id/id/eprint/32963 
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