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Kembuan, Djubir Ruslan Edy
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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Journal : Journal of Intelligent Decision Support System (IDSS)

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Optimization of K-Means algorithm in grouping data using the statistical gap method Alfiansyah Hasibuan; Djubir R.E. Kembuan; Christine Takarina Meitty Manoppo; Medi Hermanto Tinambunan
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i3.149

Abstract

In this study, we study the core concepts of the K-Means algorithm, explore its algorithmic framework, computation steps, and practical applications. Using data that is used as a basic need to perform calculations from the k-means algorithm optimization method. Using data taken from the r studio dataset with the EuStockMarkets dataset. The purpose of this study is to optimize the k-means algorithm and cluster the clustering process from a dataset, minimizing the objective function that has been set in the clustering process. The tools used are R Studio. Based on the results of this study, profiling of each group formed can be carried out. Based on the grouping results that have been carried out, the grouping results are 75.7% the accuracy of the statistical Gap method in optimizing clusters from existing datasets and the results of 92.9% are obtained from the results of minimizing the object functions in the dataset from grouping with k-means. The smaller the percentage in this grouping process the better it is in optimizing the clusters from the dataset. The author applies the k-means clustering algorithm to minimize objects for grouping from the EuStockMarkets dataset which consists of 4 variables. And the author uses the Statistical Gap method to optimize the clusters from the dataset.
Co-Authors Alfiansyah Hasibuan Alfrina Mewengkang Christine Takarina Meitty Manoppo Felly Ferol Warouw GRIFFO SONGGIGILAN GRIFFO Liando, Olivie E. S Medi Hermanto Tinambunan Metsi Daud1 Muyu, Ronaldo Ricky Ratu Ridwan, Ridwan Rifa Tingon Rolly Robert Oroh Shirly Susanne Lumeno Tendly Maki