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Hermika, Eva
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Library & Information Science Other

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Journal : Informatika

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Application Of Data Mining In Selecting Superior Products Using The K-Means And K-Medoids Algorithm Methods Hermika, Eva; Harahap, Syaiful Zuhri; Ritonga, Irmayanti
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.5968

Abstract

As a supermarket, we are committed to always improving everything, including selecting the greatest goods. To evaluate which items are more superior or popular and which are less popular, you will want a sizable amount of information sources. To select products and identify those that belong in the superior product cluster, researchers employed the clustering method. The clustering strategy uses two forms of cluster analysis, k-means and k-medoids, which have related techniques. The research results show that the k-means algorithm's Davies Bouldin value is -0.430, whereas the k-medoids algorithm's Davies Bouldin value is -1.392. This suggests that the Davies Bouldin value of the k-medoids approach is the lowest, showing that the grouping findings of the k-means method are  a better method to apply to the issue of choosing better products.
Co-Authors Ritonga, Irmayanti Syaiful Zuhri Harahap