SEGMENTASI PELANGGAN MENGGUNAKAN METODE K-MEANS CLUSTERING BERDASARKAN MODEL QRF PADA PERUSAHAAN RINTISAN PENYEDIA TENAGA KERJA
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Abstract
The difficulty of getting a job that is in accordance with the interests and specialization of a worker, as well as the difficulty of the company getting a worker who suits the needs of the company causes the mushrooming of consulting firms or labor providers in Indonesia today. With the increasing number of companies providing labor, of course the competitiveness of the business industry in the human resources is increasingly high. So it needs to be analyzed to determine the right business strategy, such as determining the company's promotion goals. One of them is analyzing the segmentation of customers who have worked together. This research successfully modeled customer segmentation based on data mining clustering techniques using the K-Means data mining algorithm. The QRF (Quantity, Recency, Frequency) modeling process is analyzing the customer's behavior from the number of requests in each transaction carried out within a certain timeframe, as well as recency as the identification of the time span of the last transaction, as well as the number of transactions made within a certain time period. Researchers conducted a period of data for one year by analyzing customer activity in start-up providers of labor during 2019, on 86 active customers. Based on the analysis results obtained, customer segmentation in two clusters with QRF (Quantity, Recency, Frequency) modeling using Davies Bouldin Index (DBI) evaluation scored -0,482, while customer segmentation in three clusters using QRF (Quantity, Recency, Frequency) evaluation using Davies Bouldin Index (DBI) evaluation to obtain -0.469, and customer segmentation in four clusters with QRF (Quantity, Recency, Frequency) modeling using Davies BouldinIndex (DBI) evaluation to obtain -0,526.
Keywords— pelanggan, clustering, algoritma k-means, DBI, QRF
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References
[2] A. Wibowo and A. R. Handoko, “Segmentasi Pelanggan Ritel Produk Farmasi Obat Menggunakan Metode Data Mining Klasterisasi Dengan Analisis Recency Frequency Monetary (RFM) Termodifikasi,” J. Teknol. Inf. dan Ilmu Komput., 2020.
[3] E. Turban, J. Aronson, and T. Llang, Decision Support Systems and Intelligent Systems. 2003.
[4] A. Bastian, H. Sujadi, and G. Febrianto, “Penerapan Algoritma K-MEANS CLustering Analysis pada penyakit menular manusia,” J. Chem. Inf. Model., 2013.
[5] C. D. Rumiarti and I. Budi, “Segmentasi Pelanggan Pada Customer Relationship Management Di Perusahaan Ritel: Studi Kasus PT Gramedia Asri Media,” J. Sist. Inf., 2017.