Segmentasi Pembelian Produk Menggunakan Algoritma K-Means Berdasarkan Clusterisasi pada pemilihan menu yang ada diUMKM Kuliner

Lolanda Hamim Annisa, Dian Rusvinasari

Abstract


Marketing strategy can be seen as one of the bases used in preparing comprehensive SME planning. One of the SMEs that will be highlighted in this research is restaurants. Customer loyalty is an important thing that must be maintained by companies for the sustainability of the company and can improve good relationships between service provider companies and their customers. K-Means Cluster Analysis is a non-hierarchical cluster analysis method that attempts to partition existing objects into one or more clusters or groups of objects based on their characteristics, so that objects that have the same characteristics are grouped in the same cluster and objects that have similar characteristics. different groups are grouped into other clusters. The purpose of this research is to find out how to group menus that have high selling power and also the relationship between one menu variable and another menu when a transaction or purchase occurs by a customer. The results obtained in this research were to create a segmentation of products purchased by customers in the period May-November 2023 in SMEs operating in the culinary sector in the Central Java area. The results showed that the types of products most frequently purchased were Chicken Rice, Tea, Chicken, & White Rice which is at the highest purchase order. Where Chicken Rice was purchased 5409 times, Tea 1867 times, White Rice 1452 times, Chicken 1110 times. In the K-Means Algorithm to determine which products sell frequently and require more inventory and which do not .

Keywords


K-Means, Clustering, UMKM, Kuliner.

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References


S. R. Arifen, V. D. Purwanty, D. A. Suci, R. H. Agustiawan, And A. R. Sudrajat, “Analisis Strategi Pemasaran Untuk Meningkatkan Daya Saing Umkm.”

A. Pramudiansyah And H. Munte, “Segmentasi Pelanggan Menggunakan Algoritma K-Means Berdasarkan Model Recency Frequency Monetary,” Vol. 7, No. 2, 2021, [Online]. Available: Http://Ejournal.Fikom-Unasman.Ac.Id

K. Auliasari Et Al., “Penerapan Algoritma K-Means Untuk Segmentasi Konsumen Menggunakan R,” 2019.

B. Dewayana, A. Purno, W. Wibowo, And I. Artikel, “Menentukan Customer Segment Dan Segment Pasar Umkm (Foodendez) Dengan Metode Decision Tree,” Jurnal Informatika, Vol. 8, No. 2, 2021, [Online]. Available: Http://Ejournal.Bsi.Ac.Id/Ejurnal/Index.Php/Ji

S. Wira Hadi, M. Fahmi Julianto, S. Rahmatullah, W. Gata, And S. Nusa Mandiri, “Bianglala Informatika Analisa Cluster Aplikasi Pada App Store Dengan Menggunakan Metode K-Means,” Vol. 8, No. 2, P. 2020.

B. E. Adiana, I. Soesanti, And A. E. Permanasari, “Analisis Segmentasi Pelanggan Menggunakan Kombinasi Rfm Model Dan Teknik Clustering,” No. 2, 2018, Doi: 10.21460/Jutei.2017.21.76.

N. H. Harani, C. Prianto, And F. A. Nugraha, “Segmentasi Pelanggan Produk Digital Service Indihome Menggunakan Algoritma K-Means Berbasis Python,” Jurnal Manajemen Informatika (Jamika), Vol. 10, No. 2, Pp. 133–146, 2020, Doi: 10.34010/Jamika.V10i2.

B. Eno Ketherin, A. Anjani Arifiyanti, A. Sodik, J. Sistem Informasi, And I. Teknologi Adhi Tama Surabaya, “Analisa Segmentasi Konsumen Menggunakan Algoritma K-Means Clustering.”

I. Y. Kurniawati, “Segmentasi Pelanggan Menggunakan Clustering K-Means.”

A. H. Lubis And P. Ganesha, “Model Segmentasi Pelanggan Dengan Kernel K-Means Clustering Berbasis Customer Relationship Management,” Jurnal & Penelitian Teknik Informatika, Vol. 1, No. 1, 2016.

F. Handayani, “Aplikasi Data Mining Menggunakan Algoritma K-Means Clustering Untuk Mengelompokkan Mahasiswa Berdasarkan Gaya Belajar,” Jurnal Teknologi Dan Informasi, Doi: 10.34010/Jati.V12i1.

K. Prabhakaran, J. Dridi, M. Amayri, And N. Bouguila, “Explainable K-Means Clustering For Occupancy Estimation,” In Procedia Computer Science, Elsevier B.V., 2022, Pp. 326–333. Doi: 10.1016/J.Procs.2022.07.041.

A. Satriawan, R. Andreswari, And O. N. Pratiwi, “Segmentasi Pelanggan Telkomsel Menggunakan Metode Clustering Dengan Rfm Model Dan Algoritma K-Means Telkomsel Customer Segmentation Using Clustering Method With Rfm Model And K-Means Algorithm.”

N. Iqbal And P. Kumar, “From Data Science To Bioscience: Emerging Era Of Bioinformatics Applications, Tools And Challenges,” In Procedia Computer Science, Elsevier B.V., 2022, Pp. 1516–1528. Doi: 10.1016/J.Procs.2023.01.130.

N. Wang, “Design Of An Intelligent Processing System For Business Data Analysis Based On Improved Clustering Algorithm,” Procedia Comput Sci, Vol. 228, Pp. 1215–1224, 2023, Doi: 10.1016/J.Procs.2023.11.105.

K. E. Setiawan, A. Kurniawan, A. Chowanda, And D. Suhartono, “Clustering Models For Hospitals In Jakarta Using Fuzzy C-Means And K-Means,” In Procedia Computer Science, Elsevier B.V., 2022, Pp. 356–363. Doi: 10.1016/J.Procs.2022.12.146.

K. Sadowski, P. Wolski, And I. Czarnowski, “An Application For Collecting And Mining Reports Referring Vulnerabilities And Exposures In Physical Systems: A Comparative Study Of Selected Clustering Methods,” Procedia Comput Sci, Vol. 225, Pp. 2763–2772, 2023, Doi: 10.1016/J.Procs.2023.10.268.

G. Gustientiedina, M. H. Adiya, And Y. Desnelita, “Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan,” Jurnal Nasional Teknologi Dan Sistem Informasi, Vol. 5, No. 1, Pp. 17–24, Apr. 2019, Doi: 10.25077/Teknosi.V5i1.2019.17-24.




DOI: https://doi.org/10.30591/jpit.v9i3.6556

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