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All Journal Jurnal Ilmu Komputer Syntax Jurnal Informatika Publication Library and Information Science Cyberspace: Jurnal Pendidikan Teknologi Informasi Jiko (Jurnal Informatika dan komputer) Technologia: Jurnal Ilmiah JSR : Jaringan Sistem Informasi Robotik Jurnal Teknik Informatika C.I.T. Medicom Jurnal Indonesia : Manajemen Informatika dan Komunikasi Insearch: Information System Research Journal Alfuad: Jurnal Sosial Keagamaan Istinarah: Riset Keagamaan, Sosial dan Budaya Jurnal Ilmu Perpustakaan dan Informasi Islam Alfuad: Jurnal Sosial Keagamaan
Dodi Nofri Yoliadi
Universitas Islam Negeri Mahmud Yunus Batusangkar
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : Insearch: Information System Research Journal

 1 
Data Mining Dalam Analisis Tingkat Penjualan Barang Elektronik Menggunakan Algoritma K-Means Dodi Nofri Yoliadi
Insearch: Information System Research Journal Vol 3, No 01 (2023): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v3i01.5829

Abstract

In this research, data collection and processing of sales of electronic goods were carried out with CV. Berkah Elektronik. The data obtained is then aggregated with the K-Means algorithm to gain knowledge about which electronic products are selling well on the market and which are not. In this study, the clustering method was used with Tanagra 1.4.48 software, and the K-Means algorithm was used as an algorithm to draw conclusions about which items were selling well and which were not inputted, namely product prices. goods and sale of goods. And from the results of testing and manual testing with the Tanagra application, the same clusters are produced, namely, products that do not sell well (Cluster_KMeans_1) and products that sell well (Cluster_KMeans_2).
Co-Authors Corry Mislonika Serjuasa Rumba Thoriq Buchory