Implementasi K-Means untuk segmentasi pelanggan dalam pengembangan aplikasi untuk toko ABC
Abstract
Abstrak
Pemerintah Indonesia mendorong akselerasi adopsi teknologi digital oleh UMKM agar tetap produktif. Toko ABC adalah salah satu UMKM yang bergerak dalam bidang distribusi suku cadang kendaraan bermotor. Tim PkM mengidentifikasi tiga masalah utama di toko ABC pada proses pembelian dan persediaan, dan lima masalah utama pada proses penjualan. Salah satu masalah utama pada proses penjualan adalah data pelanggan dan riwayat transaksi penjualan belum dimanfaatkan secara maksimal untuk melakukan segmentasi pelanggan. Tujuan dari kegiatan PkM ini adalah mengembangkan aplikasi melalui peningkatan fitur yang berfokus pada manajemen usaha inti toko. Aplikasi ini juga menerapkan K-Means untuk melakukan segmentasi pelanggan menggunakan model RFM. Analisis dan perancangan sistem menggunakan pendekatan design thinking yang terdiri dari 6 (enam) tahapan, dilanjutkan dengan konstruksi sistem, pengujian fungsionalitas, implementasi dan evaluasi aplikasi. Berdasarkan pengujian black-box dan pengujian kepuasan dengan nilai rata-rata sebesar 3.2 (interval Puas), maka PkM ini berhasil menyelesaikan masalah yang dihadapi oleh mitra.
Kata kunci: aplikasi; segmentasi; pelanggan; K-Means; RFM
Abstract
The Indonesian government is promoting the acceleration of digital technology adoption by SMEs to maintain productivity. Store ABC is one of the SMEs engaged in the distribution of motor vehicle spare parts. The Community Service team identified three main issues at Store ABC in the purchasing and inventory processes, and five main issues in the sales process. One of the main issues in the sales process is that customer data and sales transaction history have not been maximally utilized for customer segmentation. The goal of this PkM activity is to develop an application through feature enhancement focused on the core business management of the store. This application also applies K-Means to perform customer segmentation using the RFM model. The system analysis and design use a design thinking approach consisting of six stages, followed by system construction, functionality testing, implementation, and application evaluation. Based on black-box testing and satisfaction testing with an average score of 3.2 (in the Satisfied interval), this PkM successfully addressed the issues faced by the partner.
Keywords: application; segmentation; customer; K-Means; RFM
Keywords
Full Text:
PDFReferences
Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University - Computer and Information Sciences, 34(5), 1785–1792. https://doi.org/10.1016/j.jksuci.2019.12.011
Cahrianto, C., Sunarmo, S., Widuhung, S. D., Arsyad, A. T., Halim, A., & Lakhsamana, N. D. (2024). SOSIALISASI DAN PENDAMPINGAN STRATEGI PEMASARAN PADA UMKM DAPUR MOMY KEAN DI BEKASI. Kumawula: Jurnal Pengabdian Kepada Masyarakat, 7(2), 294–301. https://doi.org/10.24198/kumawula.v7i2.45933
Dedi, Dzulhaq, M. I., Sari, K. W., Ramdhan, S., Tullah, R., & Sutarman. (2019). Customer Segmentation Based on RFM Value Using K-Means Algorithm. 2019 Fourth International Conference on Informatics and Computing (ICIC), 1–7. https://doi.org/10.1109/ICIC47613.2019.8985726
DOLL, W. J., DENG, X., RAGHUNATHAN, T. S., TORKZADEH, G., & XIA, W. (2004). The Meaning and Measurement of User Satisfaction: A Multigroup Invariance Analysis of the End-User Computing Satisfaction Instrument. Journal of Management Information Systems, 21(1), 227–262. https://doi.org/10.1080/07421222.2004.11045789
Duan, H., Li, Q., He, L., Zhang, J., An, H., Ali, R., & Vazifedoust, M. (2024). Climate Classification for Major Cities in China Using Cluster Analysis. Atmosphere, 15(7), 741. https://doi.org/10.3390/atmos15070741
Istianah, E., & Yustanti, W. (2022). Analisis Kepuasan Pengguna pada Aplikasi Jenius dengan Menggunakan Metode EUCS (End-User Computing Satisfaction) berdasarkan Perspektif Pengguna. Journal of Emerging Information System and Business Intelligence (JEISBI), 3(4), 36–44. https://doi.org/10.26740/jeisbi.v3i4.47882
Kominfo, B. H. K. (2023, September 21). Dukung UMKM Go Online, Menteri Budi Arie: Kominfo Siapkan Pendampingan. Pemerintah Kabupatane MIMIKA Dinas Komunikasi dan Informatika. https://diskominfo.mimikakab.go.id/seputarit/dukung-umkm-go-online-menteri-budi-arie-kominfo-siapkan-pendampingan
Lewrick, M., Link, P., & Leifer, L. (2020). The Design Thinking Toolbox: A Guide to Mastering the Most Popular and Valuable Innovation Methods (L. Leifer, M. Lewrick, & P. Link, Ed.). Wiley. https://www.google.co.id/books/edition/The_Design_Thinking_Toolbox/yGrTDwAAQBAJ?hl=id&gbpv=0
Limanseto, H. (2022, Oktober 28). Berperan Dalam Peningkatan Pertumbuhan Ekonomi Digital, Pemerintah Dorong Akselerasi Adopsi Teknologi Digital oleh UMKM. Kementerian Koordinator Bidang Perekonomian. https://www.ekon.go.id/publikasi/detail/4662/berperan-dalam-peningkatan-pertumbuhan-ekonomi-digital-pemerintah-dorong-akselerasi-adopsi-teknologi-digital-oleh-umkm
Mariani, C., Navrotska, Y., & Mancini, M. (2023). Unsupervised machine learning for project stakeholder classification: Benefits and limitations. Project Leadership and Society, 4, 100093. https://doi.org/10.1016/j.plas.2023.100093
Meiyana, N. S., Susanto, T., Rokhmah, D., Yunanto, R. A., & Rahmawati, I. (2023). Analysis of hospital management information system satisfaction using the end-user computing satisfaction method: A cross-sectional study. Jurnal Keperawatan Padjadjaran, 11(1). https://doi.org/10.24198/jkp.v11i1.2099
Philbin, S., Viswanathan, R., & Telukdarie, A. (2022). Understanding how digital transformation can enable SMEs to achieve sustainable development: A systematic literature review. Small Business International Review, 6(1), e473. https://doi.org/10.26784/sbir.v6i1.473
Pradana, M. (2021). Maximizing Strategy Improvement in Mall Customer Segmentation using K-means Clustering. Journal of Applied Data Sciences, 2(1). https://doi.org/10.47738/jads.v2i1.18
Sembiring Brahmana, R. W., Mohammed, F. A., & Chairuang, K. (2020). Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 11(1), 32. https://doi.org/10.24843/LKJITI.2020.v11.i01.p04
Solichin, A., & Wibowo, G. (2022). Customer Segmentation Based on Recency Frequency Monetary (RFM) and User Event Tracking (UET) Using K-Means Algorithm. 2022 IEEE 8th Information Technology International Seminar (ITIS), 257–262. https://doi.org/10.1109/ITIS57155.2022.10009981
Weygandt, J. J., Kimmel, P. D., & Mitchell, J. E. (2024). Accounting Principles (J. J. Weygandt, P. D. Kimmel, & J. E. Mitchell, Ed.). Wiley. https://www.google.co.id/books/edition/Accounting_Principles/46r4EAAAQBAJ?hl=id&gbpv=0
Widagdho, A., Retnowati, M. S., Masyail, M. S., & Pratama, A. A. (2024). PENGUATAN KOMPETITIVITAS UMKM KULINER DI DESA BANGUNREJO, PONOROGO MELALUI PENDAMPINGAN IMPLEMENTASI MEDIA PEMASARAN DIGITAL. Kumawula: Jurnal Pengabdian Kepada Masyarakat, 7(2). https://doi.org/10.24198/kumawula.v7i2.54186
Wulansari, R., Sulistiani, S., & Fauzi, R. D. (2024). The influence of service quality and price on consumer satisfaction. Journal of Economics and Business Letters, 4(1), 23–32. https://doi.org/10.55942/jebl.v4i1.271
DOI: https://doi.org/10.31764/jpmb.v9i3.30276
Refbacks
- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
______________________________________________________
Jurnal Selaparang
p-ISSN 2614-5251 || e-ISSN 2614-526X
EDITORIAL OFFICE: