Penerapan metode K-Nearest Neighbor Dalam Proses
Seleksi Penerimaan Mahasantri Baru Pesantren PeTIK
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Rahman, A. Fadli
Yudho, Satrio
Asri, Yessi
Yudho, Satrio
Asri, Yessi
Subject
Teknik Informatika
Datestamp
2022-09-23 07:29:18
Abstract :
Pesantren Information and Communication Technology PeTIK is a Vocational Education Institution (Diploma 1) based on Islamic information technology at no cost. Every year it makes a selection to find students who are given the opportunity to get a scholarship program at Pesantren PeTIK. Officers often encounter problems in the selection process, this is due to several factors, namely different test sites for each region, the quota given is not proportional to the number of registrants and also the short time given to the committee. To overcome these obstacles, the alternative proposed in this study is to select new student registration participants for PeTIK students by implementing the KNearest Neighbor Algorithm. The K-Nearest Neighbor algorithm will classify participants into two classes of participants passing and not passing. Algorithm testing is done using the confusion matrix method. In this study, there are two test scenarios, in the first scenario the best accuracy is 92.04%, while in the second scenario the best accuracy is 94,05%.
Pesantren Information and Communication Technology PeTIK is a Vocational Education Institution (Diploma 1) based on Islamic information technology at no cost. Every year it makes a selection to find students who are given the opportunity to get a scholarship program at Pesantren PeTIK. Officers often encounter problems in the selection process, this is due to several factors, namely different test sites for each region, the quota given is not proportional to the number of registrants and also the short time given to the committee. To overcome these obstacles, the alternative proposed in this study is to select new student registration participants for PeTIK students by implementing the KNearest Neighbor Algorithm. The K-Nearest Neighbor algorithm will classify participants into two classes of participants passing and not passing. Algorithm testing is done using the confusion matrix method. In this study, there are two test scenarios, in the first scenario the best accuracy is 92.04%, while in the second scenario the best accuracy is 94,05%.
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