Penerapan Algoritma K-Medoids Untuk Menentukan Segmentasi Pelanggan

Anggi Ayu Dwi Sulistyawati, Mujiono Sadikin

Abstract


Abstrak

Strategi pemasaran berorientasi pelanggan memiliki peranan penting dalam mengelola hubungan baik dengan pelanggan. Agar strategi pemasaran tepat sasaran, segmentasi pelanggan dapat digunakan untuk mengelompokkan pelanggan berdasarkan karakteristik yang sama. Dalam penyusunan strategi pemasaran dapat memanfaatkan TI di bidang komputasi, salah satunya adalah data mining. Pemanfaatan teknologi komputasi untuk pengolahan data yang belum maksimal mengakibatkan penumpukan data yang miskin informasi. Pada penelitian ini dilakukan penerapan teknik clustering dengan menggunakan algoritma K-Medoids pada dataset transaksi penjualan untuk menentukan segmentasi pelanggan. Penyusunan strategi pemasaran ditentukan berdasarkan tipe dan karakteristik pelanggan pada setiap cluster atau segmen pelanggan yang terbentuk. Uji validitas cluster menggunakan Silhouette Index dan Davies Boulbin Index dilakukan untuk menentukan jumlah cluster yang paling optimal. Hasil penelitian ini menunjukan bahwa jumlah cluster optimal adalah 3 (tiga) cluster dengan nilai maksimum Silhouette Index adalah 0,375 dan nilai minimum Davies Doulbin Index adalah 1,030. Segmen pelanggan hasil penelitian adalah lost customer, core customer, dan new customer

Kata kunci: algoritma k-medoids, clustering, data mining, segmentasi pelanggan, strategi pemasaran

 

Abstract

Customer-oriented marketing strategies play an important role in managing good relationships with customers. To keep marketing strategies on target, customer segmentation can be used to group customers based on the same characteristics. In the preparation of marketing strategies can utilize IT in the field of computing, one of which is data mining. The utilization of computing technology for data processing that has not been maximized resulted in a poor accumulation of information data. In this study, the application of clustering techniques using the K-Medoids algorithm on sales transaction dataset to determine customer segmentation. The preparation of a marketing strategy is determined based on the characteristics and types of customers in each cluster or segment of customers formed. cluster validity tests using the Silhouette Index and Davies-Boulbin Index are performed to determine the most optimal number of clusters. The results of this study showed that the optimal number of clusters is 3 (three) clusters with a maximum silhouette index value of 0.375 and the minimum value of the davies-bouldin index is 1.030. The customer segments of the research results are lost customers, core customers, and new customers. 

Keywords: k-medoids algorithm, clustering, data mining, customer segmentation, marketing strategy

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References


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DOI: https://doi.org/10.32520/stmsi.v10i3.1332

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