Imam Frandika, Sofana Bayor Hud, Wiwin Handoko

Penerapan K-Means Clustering Untuk Segmentasi Penjualan Di Minimarket Mardi Dengan Pendakatan Machine Learning

Introduction

Penerapan k-means clustering untuk segmentasi penjualan di minimarket mardi dengan pendakatan machine learning. Penelitian ini menerapkan K-Means Clustering dengan machine learning untuk segmentasi penjualan di Minimarket Mardi. Temukan klaster penjualan, optimalkan strategi pemasaran, stok, & layanan.

Abstract

Perkembangan teknologi informasi mendorong pemanfaatan machine learning dalam analisis data penjualan untuk mendukung pengambilan keputusan bisnis. Minimarket Mardi memiliki beragam produk dengan pola pembelian yang bervariasi, sehingga diperlukan metode analisis yang mampu mengelompokkan data penjualan secara lebih terstruktur. Penelitian ini menerapkan algoritma K-Means Clustering untuk melakukan segmentasi penjualan berdasarkan atribut tertentu, seperti kategori produk, jumlah penjualan, serta frekuensi transaksi. Tahapan penelitian meliputi pengumpulan data, pra-pemrosesan, penentuan jumlah klaster optimal dengan metode Elbow, serta implementasi algoritma K-Means. Hasil analisis menunjukkan terbentuknya beberapa klaster yang merepresentasikan pola penjualan produk di Minimarket Mardi, mulai dari produk dengan tingkat penjualan tinggi, sedang, hingga rendah. Segmentasi ini dapat membantu manajemen minimarket dalam merancang strategi pemasaran yang lebih tepat sasaran, pengelolaan stok yang lebih efisien, serta peningkatan pelayanan kepada konsumen. Dengan demikian, penerapan K-Means Clustering terbukti efektif dalam mendukung pengambilan keputusan berbasis data di sektor ritel.

Review

This paper, titled "Penerapan K-Means Clustering Untuk Segmentasi Penjualan Di Minimarket Mardi Dengan Pendekatan Machine Learning," addresses a pertinent and timely issue in modern retail: the effective utilization of sales data for strategic decision-making. The authors correctly identify the challenge faced by establishments like Minimarket Mardi, where diverse product offerings and varied purchase patterns necessitate advanced analytical methods. By proposing K-Means Clustering, the study aims to bring a structured, machine learning-driven approach to segmenting sales data, thereby enhancing the understanding of product performance and customer behavior. The methodology outlined is straightforward and appropriate for the stated objectives. The research progresses logically from data collection and pre-processing to the critical step of determining the optimal number of clusters using the Elbow method, before implementing the K-Means algorithm. The choice of segmentation attributes, including product category, sales quantity, and transaction frequency, is well-justified and provides a comprehensive basis for understanding sales patterns. The reported outcome of forming distinct clusters representing high, medium, and low sales levels signifies a successful application of the method to reveal actionable insights from the raw sales data. The practical implications of this research are substantial for Minimarket Mardi and potentially other retail businesses. The derived sales segmentation offers a clear foundation for developing more targeted marketing campaigns, enabling more efficient inventory management, and ultimately improving customer service through a data-informed approach. This study effectively demonstrates the efficacy of K-Means Clustering in transforming raw sales data into strategic intelligence, thus reinforcing the value of machine learning in supporting robust, data-driven decision-making within the dynamic retail environment.

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