Aplikasi Clustering Menggunakan Algoritma K-Means untuk Rekomendasi Peminatan Sesuai Kemampuan Akademis pada Program Studi Teknik Informatika IT-PLN
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Anggraeni, Monik
Ningrum, Rahma Farah
Kusuma, Dine Tiara
Ningrum, Rahma Farah
Kusuma, Dine Tiara
Subject
Teknik Informatika
Datestamp
2022-09-26 07:29:37
Abstract :
In the S1 Informatics Engineering study program, there is a learning system that applies specialization to students in the sixth semester which are categorized into two options, namely Information Systems and Networks. The problem faced by students in choosing a specialization is the difficulty in determining a specialization according to their abilities. This study aims to apply the clustering method with the K-Means algorithm to provide specialization recommendations in accordance with academic abilities. The attributes used in the clustering process are NIM, the average value of information system support courses and the average value of network support courses. The data mining development method follows the 6 phases of CRISP-DM (Cross-Industry Standard Process for Data Mining). The results of applying the K-Means algorithm get an accuracy value of 77%. Where this accuracy value is obtained from comparing the results of the system with the predicted value.
In the S1 Informatics Engineering study program, there is a learning system that applies specialization to students in the sixth semester which are categorized into two options, namely Information Systems and Networks. The problem faced by students in choosing a specialization is the difficulty in determining a specialization according to their abilities. This study aims to apply the clustering method with the K-Means algorithm to provide specialization recommendations in accordance with academic abilities. The attributes used in the clustering process are NIM, the average value of information system support courses and the average value of network support courses. The data mining development method follows the 6 phases of CRISP-DM (Cross-Industry Standard Process for Data Mining). The results of applying the K-Means algorithm get an accuracy value of 77%. Where this accuracy value is obtained from comparing the results of the system with the predicted value.
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