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All Journal Jurnal Informatika Ekonomi Bisnis Jurnal Informatika Ekonomi Bisnis
Yuli Hartati
Universitas Putra Indonesia YPTK Padang
Economics, Econometrics & Finance

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Klasterisasi Bibit Terbaik Menggunakan Algoritma K-Means dalam Meningkatkan Penjualan Yuli Hartati; Sarjon Defit; Gunadi Widi Nurcahyo
Jurnal Informatika Ekonomi Bisnis Vol. 3, No. 1 (2021)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (823.763 KB) | DOI: 10.37034/infeb.v3i1.56

Abstract

Tiara Bersaudara is a shop that sells seeds and agricultural needs. To maintain a stock of seeds that farmers are interested in, sellers must be able to analyze seed sales data. This process is difficult to do because UD has a lot of sales data. The existing problem can be solved by clustering seed sales data. Clustering is grouping data into several clusters based on the level of data similarity. The research objective was to group the best-selling seedlings in UD.Tiara Bersaudara in increasing sales. Seed sales data from January to April 2019 are data that will be processed in this study. The clustering method uses the K-Means algorithm by partitioning the data into clusters based on the closest centroid to the data. Then the test is done by comparing the calculation results with the Rapid Miner studio 9.7 software. Clustering is tested based on lots of data and many clusters. The data tested were 42 seedlings by obtaining 2 clusters, 4 data which were best-selling seeds as cluster one (C1), and 38 data which were unsold seeds as cluster two (C2). Best-selling seeds are the best seeds that can increase sales consisting of Bibit Jagung NK 212, Bibit Jagung NK 7328, bibit Jagung Pioneer 32, Bibit Jagung NK 617232. The results of this study can be used as benchmarks for decision support by UD.Tiara Berasaudara to set up a marketing strategy to increase sales.
Klasterisasi Bibit Terbaik Menggunakan Algoritma K-Means dalam Meningkatkan Penjualan Yuli Hartati; Sarjon Defit; Gunadi Widi Nurcahyo
Jurnal Informatika Ekonomi Bisnis Vol. 3, No. 1 (2021)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (823.763 KB) | DOI: 10.37034/infeb.v3i1.56

Abstract

Tiara Bersaudara is a shop that sells seeds and agricultural needs. To maintain a stock of seeds that farmers are interested in, sellers must be able to analyze seed sales data. This process is difficult to do because UD has a lot of sales data. The existing problem can be solved by clustering seed sales data. Clustering is grouping data into several clusters based on the level of data similarity. The research objective was to group the best-selling seedlings in UD.Tiara Bersaudara in increasing sales. Seed sales data from January to April 2019 are data that will be processed in this study. The clustering method uses the K-Means algorithm by partitioning the data into clusters based on the closest centroid to the data. Then the test is done by comparing the calculation results with the Rapid Miner studio 9.7 software. Clustering is tested based on lots of data and many clusters. The data tested were 42 seedlings by obtaining 2 clusters, 4 data which were best-selling seeds as cluster one (C1), and 38 data which were unsold seeds as cluster two (C2). Best-selling seeds are the best seeds that can increase sales consisting of Bibit Jagung NK 212, Bibit Jagung NK 7328, bibit Jagung Pioneer 32, Bibit Jagung NK 617232. The results of this study can be used as benchmarks for decision support by UD.Tiara Berasaudara to set up a marketing strategy to increase sales.
Co-Authors Gunadi Widi Nurcahyo Sarjon Defit