Inayah, Iin (2026) IMPLEMENTASI METODE K-MEANS MENGGUNAKAN MODEL RFM UNTUK MENGANALISIS LOYALITAS PELANGGAN (Studi Kasus: Pada Toko Aisyahmart). S1 / D3 thesis, Universitas Kuningan.

[thumbnail of ABSTRAK] Text (ABSTRAK)
ABSTRAK.pdf

Download (758kB)
[thumbnail of BAB I] Text (BAB I)
BAB I.pdf

Download (382kB)
[thumbnail of BAB II] Text (BAB II)
BAB II.pdf
Restricted to Repository staff only

Download (467kB) | Request a copy
[thumbnail of BAB III] Text (BAB III)
BAB III.pdf
Restricted to Repository staff only

Download (948kB) | Request a copy
[thumbnail of BAB IV] Text (BAB IV)
BAB IV.pdf
Restricted to Repository staff only

Download (872kB) | Request a copy
[thumbnail of BAB V] Text (BAB V)
BAB V.pdf

Download (169kB)
[thumbnail of DAFTAR PUSTAKA] Text (DAFTAR PUSTAKA)
DAFTAR PUSTAKA.pdf

Download (172kB)
[thumbnail of LAMPIRAN] Text (LAMPIRAN)
LAMPIRAN.pdf

Download (6MB)

Abstract

Penelitian ini bertujuan melakukan segmentasi pelanggan pada Toko Aisyahmart menggunakan model Recency, Frequency, Monetary (RFM) dan algoritma K-Means Clustering. Masalah utamanya adalah belum adanya segmentasi berbasis data, sehingga pemasaran kurang tepat sasaran. Penelitian ini menggunakan data transaksi pelanggan selama satu tahun. Model RFM digunakan untuk menganalisis perilaku pelanggan, dilanjutkan dengan klasterisasi menggunakan algoritma K-Means. Penentuan jumlah klaster optimal menggunakan metode Elbow dan evaluasi kualitasnya menggunakan Davies-Bouldin Index (DBI). Hasil penelitian menunjukkan pelanggan berhasil dikelompokkan menjadi 3 segmen, yaitu pelanggan biasa (341 orang), pelanggan loyal (37 orang), dan pelanggan tidak loyal (94 orang). Evaluasi menghasilkan nilai DBI sebesar 0,7024, yang menunjukkan kualitas klasterisasi cukup baik. Hasil segmentasi ini diimplementasikan ke dalam analytical dashboard berbentuk tabel dan visualisasi untuk membantu Toko Aisyahmart memahami karakteristik pelanggan dan menyusun strategi pemasaran yang lebih tepat sasaran.

This study aims to perform customer segmentation at Aisyahmart Store using the Recency, Frequency, Monetary (RFM) model and the K-Means Clustering algorithm. The main issue is the lack of data-driven segmentation, leading to poorly targeted marketing. Utilizing one year of transaction data, the RFM model analyzes customer behavior, followed by clustering via the K-Means algorithm. The optimal number of clusters is determined using the Elbow method, and the quality is evaluated using the Davies-Bouldin Index (DBI). The results show that customers are successfully grouped into 3 segments: 341 regular customers, 37 loyal customers, and 94 disloyal customers. The evaluation yields a DBI score of 0.7024, indicating reasonably good clustering quality. These segmentation results are implemented into an analytical dashboard featuring tables and visualizations, assisting Aisyahmart Store in understanding customer characteristics and formulating more targeted marketing strategies.

Item Type: Thesis (S1 / D3)
Uncontrolled Keywords: Kata Kunci: Segmentasi Pelanggan, RFM, K-Means Clustering, Analytical Dashboard. Keywords: Customer Segmentation, RFM, K-Means Clustering, Analytical Dashboad.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Ilmu Komputer > S1 Sistem Informasi
Depositing User: S.Kom Iin Inayah
Date Deposited: 24 Jul 2026 02:36
Last Modified: 24 Jul 2026 02:36
URI: https://rama.uniku.ac.id/id/eprint/5989

Actions (login required)

View Item
View Item