A. Rahman, R. A. Mutiarawan, A. Darmawan, Y. Rianto and M. Syafrullah, “Prediction Of Students Academic Success Using Case Based Reasoning,” 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Bandung, Indonesia, 2019, pp. 171-176.
Abstract:
Academic success for a student is influenced by many factors during their study period. Factors such as student gender, student absenteeism, parental satisfaction with schools, relations and parents who are responsible for students can influence student success in the academic field. Researchers try to find out what are the most dominant factors in determining academic success for a student at different levels of education such as elementary, middle and high school level. Previous research grouped the level of student academic success into three levels, namely low, medium, high and obtained 15 Association Rules Generated By Apriori Algorithm. This study tried to find out and predict the possible level of academic success of students by using 9 Association Rules Generated By Apriori Algorithm from previous research. The method used to predict the level of student academic success is case based reasoning with the nearest neighbor algorithm. By using the Association Rules Generated By Image Algorithm and with the data set from the xAPIEducational Mining Dataset the case similarity value was obtained with knowledge data that is 1 with a percentage of 81%, and data that had a similarity value of less than 1 was 19%. While in the previous study the best classification accuracy was 80.6% by the Voting classifier. And the grouping of success data is divided into two, namely low and high.
Academic success for a student is influenced by many factors during their study period. Factors such as student gender, student absenteeism, parental satisfaction with schools, relations and parents who are responsible for students can influence student success in the academic field. Researchers try to find out what are the most dominant factors in determining academic success for a student at different levels of education such as elementary, middle and high school level. Previous research grouped the level of student academic success into three levels, namely low, medium, high and obtained 15 Association Rules Generated By Apriori Algorithm. This study tried to find out and predict the possible level of academic success of students by using 9 Association Rules Generated By Apriori Algorithm from previous research. The method used to predict the level of student academic success is case based reasoning with the nearest neighbor algorithm. By using the Association Rules Generated By Image Algorithm and with the data set from the xAPIEducational Mining Dataset the case similarity value was obtained with knowledge data that is 1 with a percentage of 81%, and data that had a similarity value of less than 1 was 19%. While in the previous study the best classification accuracy was 80.6% by the Voting classifier. And the grouping of success data is divided into two, namely low and high.
Published in: 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
Date of Conference: 18-20 Sept. 2019
Date Added to IEEE Xplore: 03 February 2020
ISBN Information:
Electronic ISBN: 978-602-0737-30-0
Print ISBN: 978-602-0737-28-7
USB ISBN: 978-602-0737-29-4
Print on Demand(PoD) ISBN: 978-1-7281-2739-2
DOI: 10.23919/EECSI48112.2019.8977104
Publisher: IEEE
Date of Conference: 18-20 Sept. 2019
Date Added to IEEE Xplore: 03 February 2020
ISBN Information:
Electronic ISBN: 978-602-0737-30-0
Print ISBN: 978-602-0737-28-7
USB ISBN: 978-602-0737-29-4
Print on Demand(PoD) ISBN: 978-1-7281-2739-2
DOI: 10.23919/EECSI48112.2019.8977104
Publisher: IEEE
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