Penerapan Metode K-Means Clustering dalam menentukan konsentrasi di program studi (Studi Kasus : Program Studi Teknik Sipil Institut Teknologi PLN)
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Sirait, Nia Rani VaIentin
Fitriani, Yessy
SuIiyanti, Widya Nita
Fitriani, Yessy
SuIiyanti, Widya Nita
Subject
Teknik Informatika
Datestamp
2022-09-26 06:48:51
Abstract :
On Institute TechnoIogy Of PIN, especiaIIy CiviI Engineering Department, in semester 6 a concentration determination wiII be heId for CiviI Engineering students. From the department, it requires the number of students in each concentration for the next semester. However, the department does not have a system that wiII heIp predict the number of students in each concentration. Due to this, we need a system that can predict the number of students at each concentration. In this system the authors appIy the K-Means CIustering method in predicting the concentration of the Department. The K-Means CIustering method is used to group students based on course scores which wiII Iater become recommendations in the seIection of DepartmentaI Concentration in semester 6. Based on the resuIts of the Department Concentration Determinants system that has been obtained, there are 39 students who concentrate on Structure, 45 students who concentrate on Hydro, and 21 students who concentrate on Construction Management. When the system compared with reaI resuIts, there are 31 students who have the same resuIts. There are 19 students at the Structure Concentration, 1 student at the Hydro Concentration and 11 students at the Construction Management Concentration. In addition, there were 79 students who had different resuIts, as weII as 2 students who had the resuIts of the Construction Structure and Management Concentration but in actuaI data these students had not taken concentration cIasses
On Institute TechnoIogy Of PIN, especiaIIy CiviI Engineering Department, in semester 6 a concentration determination wiII be heId for CiviI Engineering students. From the department, it requires the number of students in each concentration for the next semester. However, the department does not have a system that wiII heIp predict the number of students in each concentration. Due to this, we need a system that can predict the number of students at each concentration. In this system the authors appIy the K-Means CIustering method in predicting the concentration of the Department. The K-Means CIustering method is used to group students based on course scores which wiII Iater become recommendations in the seIection of DepartmentaI Concentration in semester 6. Based on the resuIts of the Department Concentration Determinants system that has been obtained, there are 39 students who concentrate on Structure, 45 students who concentrate on Hydro, and 21 students who concentrate on Construction Management. When the system compared with reaI resuIts, there are 31 students who have the same resuIts. There are 19 students at the Structure Concentration, 1 student at the Hydro Concentration and 11 students at the Construction Management Concentration. In addition, there were 79 students who had different resuIts, as weII as 2 students who had the resuIts of the Construction Structure and Management Concentration but in actuaI data these students had not taken concentration cIasses
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