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Journal : Journal of Computer Networks, Architecture and High Performance Computing

Implementation Of The Data Mining Cart Algorithm In The Characteristic Pattern Of New Student Admissions Ahmad Syahban Rifandy Siregar; Yunita Sari Siregar; Mufida Khairani
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.1975

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.
Analysis Of Decision Support Systems Edas Method In New Student Admission Selection Yunita Sari Siregar; Ahmad Zakir; Nenna Irsa Syahputri; Herlina Harahap; Divi Handoko
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.2057

Abstract

University of Harapan Medan is one of the private tertiary institutions in North Sumatra which has an informatics engineering study program. The informatics engineering study program is a study program that has many enthusiasts. Every year this study program graduates more than 200 students. To produce graduates who have potential, reliability and competence in the field of technology and information, it is necessary to make a selection at the beginning, namely at the time of admission of new students. There are 5 criteria used in the selection process, including the average report card score, basic ability test, computer ability test, psychological test, and interview. Each criterion has 5 weights of values, namely very high, high, medium, low and very low.  The selection process for admission of new informatics engineering students with a decision support system for the EDAS (Evaluation Based On Distance From Average Solution) method.  Where the stages in this method are by normalizing the decision matrix and looking for the average from alternatives, then from these results calculate the average positive distance (PDA) and negative distance (NDA) as well as the assessment of the weighted attribute weights of SPi and SNi, after that the normalization of positive and negative distance weights is carried out for determining the ranking score. From the results of the analysis carried out using the EDAS method, with a sample of 10 prospective students it was concluded that the 6th order student candidate had the highest score with a score of 0.519 and the lowest score in the 7th order student with a score of 0.14. Therefore, the level of accuracy of the EDAS method in selecting new student admissions is around 20%. Of course, this accuracy value will change with large data samples.