IMPLEMENTASI METODE K-MEANS CLUSTERING UNTUK SEGMENTASI PELANGGAN PADA SISTEM MANAJEMEN LAUNDRY DENGAN MODUL BUSINESS INTELLIGENCE (Studi Kasus: Faeyza Laundry)

PUTRI, MAD'SYADINA (2026) IMPLEMENTASI METODE K-MEANS CLUSTERING UNTUK SEGMENTASI PELANGGAN PADA SISTEM MANAJEMEN LAUNDRY DENGAN MODUL BUSINESS INTELLIGENCE (Studi Kasus: Faeyza Laundry). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Faeyza Laundry has customer and transaction data stored in its laundry management system; however, the data has not yet been optimally utilized to help the business owner understand customer characteristics. The available transaction data is primarily used for operational record-keeping purposes, creating a need for data processing that can generate strategic information to support decision�making. The K-Means Clustering method is implemented to group customers based on similarities in transaction behavior and present the results through a business intelligence dashboard. The data used in this study consists of secondary data in the form of customer and transaction data from Faeyza Laundry for the period from November 2024 to April 2026. The processed data is not derived from respondent samples but from all available customer transaction data in the system within the research period; therefore, no sampling technique is applied. The variables used in the segmentation process consist of Frequency, Monetary, and Average Weight. Frequency represents the number of customer transactions, Monetary represents the total value of customer transactions, while Average Weight represents the average weight of laundry per customer. The data analysis method used is K-Means Clustering, with data processing stages including transaction data aggregation, data normalization, distance calculation to centroids, cluster formation, and interpretation of segmentation results. The data processing results produce three customer segments, namely Loyal, Regular, and Low Activity. The Loyal segment is characterized by the highest Average Weight and a relatively high Monetary value, although its Frequency is lower than that of the Regular segment. The Regular segment has the highest Frequency and Monetary values, with a lower Average Weight compared to the Loyal segment. Meanwhile, the Low Activity segment has relatively the lowest Frequency, Monetary, and Average Weight values. The segmentation results presented through the business intelligence dashboard can help the owner of Faeyza Laundry understand customer characteristics and support data-driven decision-making. Keywords: K-Means Clustering, Customer Segmentation, Laundry Management System, Business Intelligence, Dashboard Faeyza Laundry memiliki data pelanggan dan transaksi yang tersimpan dalam sistem manajemen laundry, namun data tersebut belum dimanfaatkan secara optimal untuk membantu pemilik usaha memahami karakteristik pelanggan. Data transaksi yang tersedia masih lebih banyak digunakan untuk kebutuhan pencatatan operasional, sehingga diperlukan pengolahan data yang mampu menghasilkan informasi strategis bagi pengambilan keputusan. Implementasi metode K-Means Clustering dilakukan untuk mengelompokkan pelanggan berdasarkan kemiripan perilaku transaksi dan menyajikan hasilnya melalui dashboard business intelligence. Data yang digunakan merupakan data sekunder berupa data pelanggan dan transaksi Faeyza Laundry pada periode November 2024 sampai April 2026. Data yang diolah bukan berasal dari sampel responden, melainkan dari seluruh data transaksi pelanggan yang tersedia pada sistem sesuai periode penelitian, sehingga tidak menggunakan teknik pengambilan sampel. Variabel yang digunakan dalam proses segmentasi terdiri dari Frequency, Monetary, dan Average Weight. Frequency menunjukkan jumlah transaksi pelanggan, Monetary menunjukkan total nilai transaksi pelanggan, sedangkan Average Weight menunjukkan rata-rata berat cucian pelanggan. Metode analisis data yang digunakan adalah K-Means Clustering dengan tahapan pengolahan data meliputi agregasi data transaksi, normalisasi data, perhitungan jarak terhadap centroid, pembentukan cluster, dan interpretasi hasil segmentasi. Hasil pengolahan data menghasilkan tiga segmen pelanggan, yaitu Loyal, Reguler, dan Aktivitas Rendah. Segmen Loyal memiliki karakteristik Average Weight tertinggi dengan nilai Monetary yang relatif tinggi, meskipun Frequency lebih rendah dibandingkan segmen Reguler. Segmen Reguler memiliki Frequency dan Monetary tertinggi dengan Average Weight yang lebih rendah dibandingkan segmen Loyal. Sementara itu, segmen Aktivitas Rendah memiliki nilai Frequency, Monetary, dan Average Weight yang relatif paling rendah. Hasil segmentasi yang ditampilkan melalui dashboard business intelligence dapat membantu pemilik Faeyza Laundry memahami karakteristik pelanggan dan mendukung pengambilan keputusan berbasis data. Kata Kunci: K-Means Clustering, Segmentasi Pelanggan, Sistem Manajemen Laundry, Business Intelligence, Dashboard

Item Type: Thesis (S1)
NIM/NIDN Creators: 41822010089
Uncontrolled Keywords: K-Means Clustering, Segmentasi Pelanggan, Sistem Manajemen Laundry, Business Intelligence, Dashboard
Subjects: 100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 153 Conscious Mental Process and Intelligence/Intelegensia, Kecerdasan Proses Intelektual dan Mental
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
600 Technology/Teknologi > 650 Management, Public Relations, Business and Auxiliary Service/Manajemen, Hubungan Masyarakat, Bisnis dan Ilmu yang Berkaitan > 658 General Management/Manajemen Umum
Divisions: Fakultas Ilmu Komputer > Sistem Informasi
Depositing User: khalimah
Date Deposited: 11 Sep 2026 04:58
Last Modified: 11 Sep 2026 04:58
URI: http://repository.mercubuana.ac.id/id/eprint/103787

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