Pemanfaatan Teknologi dalam Pengelompokkan Produk pada Minimarket
DOI:
https://doi.org/10.35134/jitekin.v11i1.52Keywords:
Retail, Minimarket, Product, Clustering, k-meansAbstract
The retail industry is currently growing rapidly, especially in Indonesia. One form of the retail industry is modern retail which includes supermarkets, minimarkets and others. This study focuses on the grouping of products sold at minimarkets. This research is caused by seeing the phenomenon of the large number of transactions that occur in one day, the result is the number of products sold. This makes it difficult for minimarket managers to determine the next product procurement. Therefore, This study is conducted to group the products sold so that the products that need to be procured are seen next. This study propose a software to perform the grouping using the K-means algorithm. For the data sample, this study obtained sales transaction data for 3 months from the Sastra Mart minimarket. In this study, manual calculations were carried out on 10 samples of beverage data taken randomly from sales transactions which would be divided into 3 clusters. The results of manual calculations, there are 3 drink data entered into the “Sangat Laris” cluster, 2 drink data entered the “Laris” cluster and 5 drink data entered the “Kurang Laris” cluster. The software produced from the research gives the same results as manual calculations in classifying products. This study has also carried out software testing to test all its functionalities, from the test results, everything runs normally and as expected.
References
Abubakar, A., Sagir, J., & Husnan, L. H. (2021). Penghadapi Minimarket / Ritail Modern Di Kabupaten Lombok Tengah. Distribusi, 9(2), 171–192. https://doi.org/10.29303/distribusi.v9i2.156
Alvisan, F. K. (2021). Clustering Minimarket Untuk Menentukan Jumlah Kebutuhan Pembelian Menggunakan Metode K-Means. Jurnal NOE, 4(2), 160–168. https://doi.org/10.29407/noe.v4i2.16784
Ananda, N., & Aras, R. A. (2021). Clustering Pengeluaran Tahunan Berbagai Macam Produk Menggunakan Metode K-Means. Seminar Nasional Sains Dan Teknologi Informasi SENSASI 2021, 143–147.
Elmayati, E. (2017). Data Mining Dengan Metode Clustering Untuk Pengolahan Informasi Persediaan Obat Pada Klinik Srikandi Medika Berbasis Web. Pelita Informatika: Informasi Dan Informatika, 16(4), 357–362. https://ejurnal.stmik-budidarma.ac.id/index.php/pelita/article/view/531/482
Event, & Utnasari, I. (2021). Analisis Clustering dengan K-Means untuk Pengelompokkan Penjualan Produk Pada Hotel Newton. Computer and Science Industrial Engineering, 04(Vol 4 No 4 (2021): Comasie), 1–8. http://ejournal.upbatam.ac.id/index.php/comasiejournal/article/view/3430
Fakhriza, M. H., & Umam, K. (2021). Analisis Produk Terlaris Menggunakan Metode K-Means Clustering pada PT. Sukanda Djaya. JIKA (Jurnal Informatika), 5(1), 8–15. https://doi.org/10.31328/jointecs.v6i3.2693
Fithri, F. A., & Wardhana, S. (2021). Cluster Analysis of Sales Transaction Data Using K-Means Clustering At Toko Usaha Mandiri. Jurnal PILAR Nusa Mandiri, 17(2), 113–118. https://doi.org/10.33480/pilar.v17i2.2273
Fithriyah, M., Yaqin, M. A., & Zaman, S. (2021). K-Means Clustering Untuk Segmentasi Produk Berdasarkan Analisis Recency, Frequency, Monetary (RFM) Pada Data Transaksi Penjualan. ILKOMNIKA: Journal of Computer Science and Applied Informatics, 3(2), 151–164. https://doi.org/10.28926/ilkomnika.v3i2.284
Handayani, S. (2018). Perancangan Sistem Informasi Penjualan Berbasis E-Commerce Studi Kasus Toko Kun Jakarta. ILKOM Jurnal Ilmiah, 10(2), 182–189. https://doi.org/10.33096/ilkom.v10i2.310.182-189
Indaryono, Apdian, D., Awalludin, D., & Nurhasanah, I. A. (2021). Inventory Control Accounting Computerization In J-Mart Karawang Based On Vb.Net 2008. Dirgamaya : Jurnal Manajemen Dan Sistem Informasi, 01, 43–55. https://doi.org/10.35969/dirgamaya.v1i1.26
Mahendra, H. M., & Antoni, D. (2020). Penerapan Data Mining Untuk Clustering Pada Minimarket Di Kota Palembang Mengunakan Algoritma Hard C-Means. Bina Darma Conference on …. http://conference.binadarma.ac.id/index.php/BDCCS/article/view/1589
Mandala, E. P. W., & Putri, D. E. (2020). Peramalan Produksi Serundeng Kentang dengan Fuzzy Tsukamoto. Jurnal Media Informatika Budidarma, 4(3), 769. https://doi.org/10.30865/mib.v4i3.2238
Mandala, E. P. W., Yanto, M., & Putri, D. E. (2018). Aplikasi Pengelompokan Penjualan Dengan Clustering Data Mining Pada Toko Retail Kota Padang. Prosiding SISFOTEK.
Mardhiyah, A., & Safrin, F. A. (2021). Persaingan Usaha Warung Tradisional dengan Toko Modern. Jurnal Bisnis Dan Manajemen, 8(1). https://doi.org/10.26905/jbm.v8i1.5454
Negara, I. S. M., Purwono, & Ashari, I. A. (2021). Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 dengan Algoritma K-means. JOINTECS, 6(3), 153–160. https://doi.org/10.31000/jika.v5i1.3236
Normah, Nurajizah, S., & Salbinda, A. (2021). Penerapan Data Mining Metode K-Means Clustering Untuk Analisa Penjualan Pada Toko Fashion Hijab Banten. Jurnal Teknik Komputer, 7(2), 158–163. https://doi.org/10.31294/jtk.v7i2.10553
Nusti, D. H., Kanedi, I., & Rohmawan, E. P. (2021). Application of K-Means Clustering Algorithm in Grouping Inventory Data at Putra Shop. JURNAL Komitek, 1(1), 29–38. https://doi.org/10.53697/jkomitek.v1i1.104
Putri, D. E. (2020). Pola Frekuensi Penjualan Barang Bali Mart Menggunakan Fp-Growth. JOISIE (Journal Of Information Systems And Informatics Engineering), 4(1), 15. https://doi.org/10.35145/joisie.v4i1.517
Ramadhan, M. Y., Herwanto, D., & Akhriyani, L. (2021). Analisis ukuran kinerja sistem pelayanan pada antrian Alfamidi Jalan HS. Ronggo Waluyo Karawang. Jurnal NOE, 4(01), 35–44. https://ojs.unpkediri.ac.id/index.php/noe/article/view/15909/2013
Sari, I. P. (2021). Implementasi Data Science dalam Ritel Online: Analisis Customer Retention dan Clustering Customer dengan Metode K-Means. J-SAKTI (Jurnal Sains Komputer Dan Informatika), 5(1), 417–425.
Jurnal Teknologi has launched a new template since Vol. 12 (2022) No. 1
