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Saputra, Ramzi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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OPTIMIZING K-MEANS ALGORITHM WITH ELBOW AND SILHOUETTE METHODS FOR NATIONAL EXAM SCORE DATA CLUSTERING Saputra, Ramzi; Purnama, Iwan
Jurnal Ilmu Komputer Ruru Vol. 1 No. 1 (2024): Edisi Januari
Publisher : Yayasan Grace Berkat Anugerah

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Abstract

The national examination is an evaluation system for basic education standarts that supports student graduation. In accordance with the regulations of the Government of the Republic Indonesia, the evaluation of learning outcomes aims to evaluate the achievement of national graduate students. As the data obtained by the author, namely the National Vocational Exam Value Data for the Vocational High School in Central Java Province for the class of 2019. But the data displayed is still random and less information. Then data mining techniques are needed to classify which schools is carried out using the k-means clustering method and using elbow and silhouette optimization, with optimum k obtained K=3 and K=2 with calculations using RStudio tools. It is expected to produce the best cluster for the grouping
Co-Authors Iwan Purnama Pane, Rahmadani