Implementation Of The Data Mining Cart Algorithm In    The Characteristic Pattern Of New Student Admissions

Ahmad Syahban Rifandy Siregar; Yunita Sari  Siregar; Mufida  Khairani

doi:10.47709/cnahpc.v5i1.1975

Authors

Ahmad Syahban Rifandy Siregar Universitas Harapan Medan
Yunita Sari Siregar Universitas Harapan Medan
Mufida Khairani Universitas Harapan Medan

DOI:

10.47709/cnahpc.v5i1.1975

Keywords:

Data Mining, CART (Classification And Regression Tree), Pattern, Student, Informatics Engineering Study Program

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Abstract

University of Harapan Medan is one of the private universities in North Sumatra which has an Informatics Engineering Study Program with Good Accreditation. With better accreditation, the number of students who register is also increasing. At the admission of new students, the committee has a huge pile of data, making it difficult in the process of whether the student passed or did not pass. Therefore, in this study, we will implement data mining with the CART (Classification And Regression Tree) algorithm. Data mining is a technique to determine the characteristic pattern of a variable or data criteria with a large amount. In the CART method, the data is first converted into testing data, which will then be used to form a classification tree by calculating the value of information gain, Gini index and goodness of split. From the results obtained, it will be re-determined terminal nodes, marking class labels and finally pruning the classification tree which produces a decision tree. In this study, the number of testing data was 75 with 3 criteria, namely the average value of report cards, CAT test scores, and interview scores. The results of testing data testing using RapisMiner 5.3 software produce 23 number of characteristic pattern rules, where node 1 is the CAT test score, level 1 branch node is the interview score criteria and level 2 branch node is the average report card value.

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