Implementasi Algoritma K-Means untuk Menentukan Persediaan Barang pada Poultry Shop

Firman Nurdiyansyah; Ismail Akbar

doi:10.26905/jtmi.v7i2.6377

Implementasi Algoritma K-Means untuk Menentukan Persediaan Barang pada Poultry Shop

Firman Nurdiyansyah, Ismail Akbar

Abstract

Maintaining inventory so that the goods do not get empty is one of the ways to maintain customer satisfaction. To do this, company management must be able to analyze which items are selling well and which items are not selling well, especially in the sales department. It is not easy to CV. Muria PS because it has a large number of items, so it takes a little computational technique to simplify the problem. The K-Means clustering algorithm was chosen to solve this problem because it can group the products sold and still available into several clusters. Of the three clusters formed, cluster 1 consists of two items, cluster 2 consists of 9 items, and the remaining 25 items are included in cluster 3. From these results, CV management can take advantage of this. Muria PS to increase inventory stock and sales strategy.

Keywords

Data Mining; Clustering; K-Means; Sale; Goods.

Full Text:

PDF

References

P. P. Putra and A. S. Chan, “Pengembangan Aplikasi Perhitungan Prediksi Stock Motor Menggunakan Algoritma C 4.5 Sebagai Bagian dari Sistem Pengambilan Keputusan (Studi Kasus di Saudara Motor),” INOVTEK Polbeng - Seri Inform., vol. 3, no. 1, p. 24, 2018, doi: 10.35314/isi.v3i1.296.

B. M. Metisen and H. L. Sari, “Analisis clustering menggunakan metode K-Means dalam pengelompokkan penjualan produk pada Swalayan Fadhila,” J. Media Infotama, vol. 11, no. 2, pp. 110–118, 2015.

K. Kusrini, “Grouping of Retail Items by Using K-Means Clustering,” Procedia Comput. Sci., vol. 72, pp. 495–502, 2015, doi: 10.1016/j.procs.2015.12.131.

M. Imron, U. Hasanah, and B. Humaidi, “Analysis of Data Mining Using K-Means Clustering Algorithm for Product Grouping,” IJIIS Int. J. Informatics Inf. Syst., vol. 3, no. 1, pp. 12–22, 2020, doi: 10.47738/ijiis.v3i1.3.

P. Kasih, “Pemodelan Data Mining Decision Tree Dengan Classification Error Untuk Seleksi Calon Anggota Tim Paduan Suara,” Innov. Res. Informatics, vol. 1, no. 2, pp. 63–69, 2019, doi: 10.37058/innovatics.v1i2.918.

M. R. L. Iin Parlina, Agus Perdana Windarto, Anjar Wanto, “Memanfaatkan Algoritma K-Means Dalam Menentukan Pegawai Yang Layak Mengikuti Asessment Center,” Memanfaatkan Algoritm. K-Means Dalam Menentukan Pegawai Yang Layak Mengikuti Asessment Cent. Untuk Clust. Progr. Sdp, vol. 3, no. 1, pp. 87–93, 2018.

M. G. H. Omran, A. P. Engelbrecht, and A. Salman, “An overview of clustering methods,” Intell. Data Anal., vol. 11, no. 6, pp. 583–605, 2007, doi: 10.3233/ida-2007-11602.

S. Kapil, M. Chawla, and M. D. Ansari, “On K-means data clustering algorithm with genetic algorithm,” 2016, doi: 10.1109/PDGC.2016.7913145.

A. Agrawal and H. Gupta, “Global K-Means (GKM) Clustering Algorithm: A Survey,” Int. J. Comput. Appl., vol. 79, no. 2, pp. 20–24, 2013, doi: 10.5120/13713-1472.

mohamad jajuli nurul rohmawati, sofi defiyanti, “Implementasi Algoritma K-Means Dalam Pengklasteran Mahasiswa Pelamar Beasiswa,” Jitter 2015, vol. I, no. 2, pp. 62–68, 2015.

DOI: https://doi.org/10.26905/jtmi.v7i2.6377