PERBANDINGAN ALGORITMA FUZZY C-MEANS DAN K-MEANS DENGAN MODEL RFM UNTUK MENENTUKAN CUSTOMER SEGMENTATION (STUDI KASUS:CAHAYA33 DIGITAL PRINTING)

SETIAWAN, SYAHRUL (2024) PERBANDINGAN ALGORITMA FUZZY C-MEANS DAN K-MEANS DENGAN MODEL RFM UNTUK MENENTUKAN CUSTOMER SEGMENTATION (STUDI KASUS:CAHAYA33 DIGITAL PRINTING). S1 thesis, Universitas Mercu Buana-Menteng.

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Abstract

Data penjualan merupakan aset penting bagi perusahaan dalam memahami perilaku pelanggan dan merumuskan strategi pemasaran yang efektif. Dalam konteks ini, penelitian ini berfokus pada analisis segmentasi pelanggan menggunakan dua algoritma klasterisasi yang populer, yaitu K-Means dan Fuzzy C-Means, dengan pendekatan model RFM (Recency, Frequency, Monetary). Masalah yang dihadapi adalah menentukan algoritma mana yang lebih optimal dalam mengelompokkan pelanggan berdasarkan karakteristik pembelian mereka. Penelitian ini bertujuan untuk membandingkan kinerja kedua algoritma dalam segmentasi pelanggan di Cahaya33 Digital Printing, dengan menggunakan data penjualan yang dikumpulkan dari bulan Juni 2020 hingga Desember 2021. Proses penelitian dimulai dengan pengumpulan dan pra-pemrosesan data untuk memastikan kualitas dan relevansi informasi yang digunakan. Selanjutnya, kedua algoritma diterapkan untuk mengidentifikasi cluster pelanggan berdasarkan nilai RFM, dengan evaluasi menggunakan metrik validitas seperti Davies-Bouldin Index dan Silhouette Score. Hasil penelitian ini diharapkan dapat memberikan wawasan mengenai perbandingan efektivitas K-Means dan Fuzzy C-Means dalam hal akurasi dan keandalan segmentasi. Temuan ini akan menunjukkan algoritma mana yang lebih baik dalam mengelompokkan pelanggan, serta memberikan rekomendasi bagi perusahaan dalam mengoptimalkan strategi pemasaran berdasarkan hasil segmentasi yang diperoleh. Sales data is an important asset for companies in understanding customer behavior and formulating effective marketing strategies. In this context, this research focuses on customer segmentation analysis using two popular clustering algorithms, namely K-Means and Fuzzy C-Means, with an RFM (Recency, Frequency, Monetary) model approach. The problem addressed is determining which algorithm is more optimal in grouping customers based on their purchasing characteristics. This study aims to compare the performance of both algorithms in customer segmentation at Cahaya33 Digital Printing, using sales data collected from June 2020 to December 2021. The research process begins with the collection and preprocessing of data to ensure the quality and relevance of the information used. Next, both algorithms are applied to identify customer clusters based on RFM values, with evaluation using validity metrics such as the Davies-Bouldin Index and Silhouette Score. The results of this study are expected to provide insights into the comparative effectiveness of K-Means and Fuzzy C-Means in terms of segmentation accuracy and reliability. These findings will indicate which algorithm is better at clustering customers and provide recommendations for the company in optimizing marketing strategies based on the segmentation results obtained.

Item Type: Thesis (S1)
NIM/NIDN Creators: 41818310009
Uncontrolled Keywords: Segmentasi Pelanggan, K-Means, Fuzzy C-Means, RFM, Metrik Validitas. Customer Segmentation, K-Means, Fuzzy C-Means, RFM, Validity Metrics.
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem
Divisions: Fakultas Ilmu Komputer > Sistem Informasi
Depositing User: NAYLA AURA RAYANI
Date Deposited: 06 Sep 2024 03:50
Last Modified: 06 Sep 2024 03:50
URI: http://repository.mercubuana.ac.id/id/eprint/91214

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