Customer Relationship Management in Increasing Customer Loyalty using the K-Means Algorithm
DOI:
https://doi.org/10.35134/jcsitech.v9i1.60Keywords:
Customer Relationship Management, K-means Algorithm, Loyalty, Web, ManualAbstract
Advances in technology allow humans to more quickly and precisely in carrying out various activities in everyday life. One of the companies engaged in the field of vehicle washing services is Doorsmeer Keluarga Nasution. Doorsmeer. The Nasution Family Doorsmeer, located on Jl. Medan Padang Aek Godang Panyabungan, Mandailing Natal Regency, provides services in the form of car and motorcycle washing. Bookkeeping at the Nasution Family Doorsmeer is still done manually, allowing for loss of important transaction data, as well as a lack of business promotion in the community resulting in slow business development. The purpose of this study is generally to apply Web-based Customer Relationship Management with the K-means algorithm method for marketing/transactions at the Nasution Family Doorsmeer. This research is a combined type, using descriptive qualitative and quantitative methods. From the research results it is known that with the existence of Web-based Customer Relationship Management it can simplify and assist in managing good marketing strategies so that they can increase sales revenue, and by providing the best service will encourage customer loyalty and make it easier to determine loyal customers with the K-means algorithm in order to continue to provide convenience to customers.
References
Wildyaksanjani, JP, & Sugiana, D. (2018). PT Angkasa Pura II (Persero) Customer Relationship Management (CRM) Strategy. Journal of Communication Studies, 6(1), 10. https://doi.org/10.24198/jkk.v6i1.8754
Bastian, A., Sujadi, H., & Febrianto, G. (2018). Application of the K-Means Clustering Analysis Algorithm to Human Transmitted Diseases (Case Study in Majalengka Regency). 1, 26–32. https://doi.org/10.21609/jsi.v14i1.566
Fauzi, A., & Harli, E. (2017). Service Quality Improvement Through CRM with the RAD Method. RESTI Journal (Systems Engineering and Information Technology), 1(1), 76–81. https://doi.org/10.29207/resti.v1i1.16
Fahurian, F. (2018). Tender Data Collection Information System at the Highways Service of Lampung Province. 7. https://doi.org/10.31227/osf.io/768en
Ahmad, RF, & Hasti, N. (2018). Web-Based Sandal Sales Information System. Journal of Technology and Information, 8(1), 67–72. https://doi.org/10.34010/jati.v8i1.911
Ngelyarata, D., & Soediantono, D. (2022). Customer relationship management (CRM) and recommendation for implementation in the defense industry: a literature review. Journal of Industrial Engineering & Management Research, 3(3), 17-34. https://doi.org/10.7777/jiemar.v3i3.279
Damayanti, D. (2020). Design and Build a Technology and Business Alignment Measurement System for the Auditing Process. Compact Techno Journal, 14(2), 92-97. https://doi.org/10.33365/jtk.v14i2.727
Alfiah, A., & Damayanti, D. (2020). E-Marketplace Application for Sales of Catfish Harvests (Case Study: Pringsewu Regency, Pagelaran District). Journal of Technology and Information Systems, 1(1), 111-117. https://doi.org/10.33365/jtsi.v1i1.241
Rusliyawati, R., Damayanti, D., & Prawira, FN (2020, December). Implementation of SAW Method for Determining SCRM Model as Business Strategy in Higher Education. In Proceeding International Conference on Information Technology and Business (pp. 249-257).
Hana, P., Rusliyawati, R., & Damayanti, D. (2019). The Influence of Media Richness and Frequent Updates on the Loyalty of Higher Education Academics. Compact Techno Journal, 13(2), 7-10. https://doi.org/10.33365/jtk.v13i2.328
Aris, A., Anggara, R., & Zamzami, ZA (2016). Web-Based New Student Admissions Information System Design at PKBM Bhakti Sejahtera. Cices, 2(1), 87–98. https://doi.org/10.33050/cices.v2i1.215
Putra, RR, & Wadisman, C. (2018). Implementation of Potential Customer Selection Data Mining Using the K Means Algorithm. INTECOMS: Journal of Information Technology and Computer Science, 1(1), 72–77. https://doi.org/10.31539/intecoms.v1i1.141
Sucipto, A. (2019). Clustering Prospective New Students Using the K-Means Algorithm. Science Tech: Journal of Science and Technology, 5(2), 50-56. https://doi.org/10.30738/jst.v5i2.5829
Sulistiyawati, A., & Supriyanto, E. (2021). Implementation of the K-means Clustring Algorithm in Determining Superior Class Students. Compact Techno Journal, 15(2), 25-36. https://doi.org/10.33365/jtk.v15i2.1162
Rohmatullah, A., Rahmalia, D., & Pradana, MS (2019). Agricultural Data Clusterization in Lamongan District Using K-Means and Fuzzy C Means Algorithms. Scientific Journal of Technoscience, 5(2), 86-93. https://doi.org/10.26877/jitek.v5i2.4254
Putra, RR, & Wadisman, C. (2018). Implementation of Potential Customer Selection Data Mining Using the K Means Algorithm. INTECOMS: Journal of Information Technology and Computer Science, 1(1), 72–77. https://doi.org/10.31539/intecoms.v1i1.141
Utomo, DP, & Mesran, M. (2020). Comparative Analysis of Data Mining Classification Methods and Attribute Reduction in Heart Disease Data Sets. Journal of Media Informatics Budidarma, 4(2), 437. https://doi.org/10.30865/mib.v4i2.2080
Simargolang, MY, & Nasution, N. (2018). WEB-Based Laundry Service Application (Case Study: Pelangi Laundry Kisaran). Journal of Information Technology, 2(1), 9. https://doi.org/10.36294/jurti.v2i1.402
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