Author : Mustakim, Novia Kumala Sari, Jasri, Ismu Kusumanto, Nurul Gayatri Indah Reza

Publish : Indonesian Journal of Electrical Engineering and Computer Science Vol. 12, No. 3, December 2018, pp. 1257~1264

Abstract :

Data mining has two main concepts of data distribution, namely supervised learning and unsupervised learning. The most easily recognizable concepts from data distribution is related to the dataset, with and without target class. Analytic Hierarchy Process (AHP) technique that carries the concept of pairwise comparison able to answer the problem related to the dataset, which is to change unsupervised to be supervised by determining eigenvalue value of each attribute and sub attribute in AHP method. The case study conducted in this issue is related to determining the target classes used to predict the success of a student learning in UIN Suska Riau. The three main attributes are Procrastination, Total Credits (SKS) and Number of Repeated Courses, each having eigenvalues of 0.319; 0.189 and 0.171 which become the feedback in the determination of the Target Timely Graduation (TG) or Possibility of Timely Graduation (PTG). The biggest consistency ratio generated in the AHP case is 9.4% in the GPA attribute. This research recommends that further research should use datasets that have been arranged based on experimental combinations of the three main attributes above, then applied to the classification or prediction algorithm. So that it would obtain a decision of accuracy from data used against the real result on the field

Conclusion :

Based on the research conducted on the case study to predict the success of students study in UIN Suska Riau, it can be concluded that the main attribute that becomes the feedback to determine the success rate of students is procrastination with the largest eigenvalue compared with others. While the sub-attributes that is very influential on procrastination are the attribute of waiting for friends and postponing the final project. Four combinations in determining the target class for supervised learning dataset in this case are procrastination, total of credits and Number of Repeated Courses. These three attributes are combined with four other attributes into a single unit in the datasets for the classification and prediction process. The consistency ratio of attributes and sub-attributes on average shows a small percentage or less than 10%, thus the assessment is considered to be very consistent

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