Purnama Jaya, Adee (2019) ANALISIS SEGMENTASI AGEN PENJUALAN MENNGUNAKAN MODEL RFM DAN ALGORITMA K-MEANS (Studi Kasus : PT. Kereta Api Indonesia). S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
Abstrak Agen penjualan memberikan kontribusi yang cukup besar dalam pemasaran. Setiap agen yang berbeda memiliki nilai yang berbeda pula. Pengetahuan tentang karakteristik setiap agen yang diperlukan untuk mendukung keputusan yang diperlukan strategi bisnis perusahaan juga mengatur hubungan yang baik antara perusahaan dan agennya. Segmentasi agen penjualan dapat Segasi agen penjualan, perusahaan diharapkan dapat meminta kebijakan yang tepat. Informasi tentang segmentasi agen penjualan dapat diperoleh dengan menggunakan teknik Penambangan Data dan metode RFM (Kekinian, Frekuensi, Moneter). Teknik Data Mining K-means Clustering digunakan untuk segmentasi agen penjualan. RFM adalah model yang digunakan untuk membedakan agen berdasarkan 3 variabel, yaitu kebaruan, frekuensi, dan moneter. Dari hasil penerapan model kmeans dan agen RFM penjualan dibagi menjadi 3 segmen dengan karakteristik yang berbeda. Segmen ketiga tersebut yaitu 54,17% agen baru atau agen yang jarang terjadi transaksi dan tidak lama terjadi transaksi lagi, 35,42% agen penjualan terbaik dan merupakan agen yang ”bernilai” untuk perusahaan, serta 10,42% agen penjualan yang semakin meningkat. Kata kunci: Data Mining, k-means, Clustering, RFM. Abstract Sales agents make a significant contribution in marketing. Each different agent has different values. Knowledge of the characteristics of each agent needed to support the decisions needed by the company's business strategy also regulates good relations between the company and its agents. Sales agent segmentation can be Segasi sales agents, companies are expected to be able to request the right policies. Information about sales agent segmentation can be obtained using Data Mining techniques and RFM methods (Current, Frequency, Monetary). The Kmeans Clustering Data Mining technique is used for sales agent segmentation. RFM is a model used to differentiate agents based on 3 variables, namely novelty, frequency, and monetary. The results of the application of the k-means model and the RFM sales agent are divided into 3 segments with different characteristics. The third segment is 54.17% of new agents or agents that rarely occur and there is no longer a long transaction, 35.42% of the best sales agents and agents who are "valued" for the company, and 10.42% sales agents are increasing. Keywords: Data Mining, k-means, Clustering, RFM.
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIKOM/INF 19 001 |
| NIM/NIDN Creators: | 41513110088 |
| Uncontrolled Keywords: | Kata kunci: Data Mining, k-means, Clustering, RFM. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 15 Jul 2022 01:53 |
| Last Modified: | 15 Jul 2022 02:00 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/65249 |
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