PENGELOMPOKKAN POTENSI AKADEMIK SISWA RA TARBIYATUL AULAD DENGAN METODE K-MEANS
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Institusion
Universitas Muhammadiyah Gresik
Author
SA’ADAH, BANATUS
Subject
Informatics Engineering
Datestamp
2019-07-01 12:05:00
Abstract :
RA Tarbiyatul Aulad a nursery school that has a lot of students. The school is growing quite fast in comparison schools kindergarten others but in the assessment of students in schools is still using manual systems that will slow down the process of assessment. One student assessment categorization system is based on the average value by using data mining method maens k-clustering. Where k-means clustering is a method of data analysis or data mining methods that menbuat modeling making process without supervision and is one method that performs grouping of data to the system partition. K- means clustering method tries to classify the data into several groups, where data in one group have similar characteristics to each other ynag and have different characteristics from the existing data in the other groups. With the expected method k-means clustering can help in the calculation of the value of students based on grades ujikompetensi students , so that teachers can see which students are able to complete a given task .
RA Tarbiyatul Aulad a nursery school that has a lot of students. The school is growing quite fast in comparison schools kindergarten others but in the assessment of students in schools is still using manual systems that will slow down the process of assessment. One student assessment categorization system is based on the average value by using data mining method maens k-clustering. Where k-means clustering is a method of data analysis or data mining methods that menbuat modeling making process without supervision and is one method that performs grouping of data to the system partition. K- means clustering method tries to classify the data into several groups, where data in one group have similar characteristics to each other ynag and have different characteristics from the existing data in the other groups. With the expected method k-means clustering can help in the calculation of the value of students based on grades ujikompetensi students , so that teachers can see which students are able to complete a given task .
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