Implementation of RFM Method and K-Means Algorithm for Customer Segmentation in E-Commerce with Streamlit
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Implementation of RFM Method and K-Means Algorithm for Customer Segmentation in E-Commerce with Streamlit |
| 2. | Creator | Author's name, affiliation, country | Farrikh Alzami; Universitas Dian Nuswantoro; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Fikri Diva Sambasri; Universitas Dian Nuswantoro |
| 2. | Creator | Author's name, affiliation, country | Mira Nabila; Universitas Dian Nuswantoro |
| 2. | Creator | Author's name, affiliation, country | Rama Aria Megantara; Universitas Dian Nuswantoro |
| 2. | Creator | Author's name, affiliation, country | Ahmad Akrom; Universitas Dian Nuswantoro |
| 2. | Creator | Author's name, affiliation, country | Ricardus Anggi Pramunendar; Universitas Dian Nuswantoro |
| 2. | Creator | Author's name, affiliation, country | Dwi Puji Prabowo; Universitas Dian Nuswantoro |
| 2. | Creator | Author's name, affiliation, country | Puri Sulistiyawati; Universitas Dian Nuswantoro |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | E-Commerce; Customer Segmentation; K-Means; RFM; Streamlit |
| 4. | Description | Abstract | E-commerce is selling and buying goods through an online or online system. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One thing that needs to be considered in the business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers to maintain good relationships with customers and increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature and display it in the form of a dashboard using the Streamlit framework. Several stages of research must be carried out. Firstly, taking data from the open public data site (Kaggle), then merging the data to select some data that needs to be used, understanding data by displaying it in graphic form, and conducting data selection to select features/attributes. The step follows the proposed method, performs data preprocessing, creates a model to get the cluster, and finally displays it as a dashboard using Streamlit. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470. |
| 5. | Publisher | Organizing agency, location | Prodi Teknik Informatika FIK Universitas Muslim Indonesia |
| 6. | Contributor | Sponsor(s) | Lembaga Penelitian dan Pengabdian Universitas Dian Nuswantoro |
| 7. | Date | (YYYY-MM-DD) | 2023-04-07 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1524 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.33096/ilkom.v15i1.1524.32-44 |
| 11. | Source | Title; vol., no. (year) | ILKOM Jurnal Ilmiah; Vol 15, No 1 (2023) |
| 12. | Language | English=en | en |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2023 Farrikh Alzami, Fikri Diva Sambasri, Mira Nabila, Rama Aria Megantara, Ahmad Akrom, Ricardus Anggi Pramunendar, Dwi Puji Prabowo, Puri Sulistiyawati This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |