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All Journal Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Ilmiah Sistem Informasi
Kurnia, Dennis Ma'rifatul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Transportation Other

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Segmentasi Pelanggan Berdasarkan Volume Pembelian Produk Menggunakan Algoritma K-Means Untuk Efektivitas Pemasaran Kurnia, Dennis Ma'rifatul; Aulia, Nurun Nihayatur Rifqiya; Hidayah, Yulistiya Nur; Saputri, Cindy Avitaselly Bambang
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 2 (2025): OKTOBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i2.9042

Abstract

The rapid development of information technology has changed the way products are sold, especially through online platforms that are increasingly in demand. In increasingly tight business competition, companies need to understand the differences in customer needs and behavior. Inability in this regard can make it difficult to design effective marketing strategies. Therefore, customer segmentation based on transaction data is an important solution to group customers based on similar purchasing patterns. This study aims to examine customer segmentation based on sales transactions to help companies understand customer characteristics and develop more targeted and adaptive marketing strategies. A quantitative approach is used by applying the K-Means Clustering algorithm and PCA dimension reduction to a dataset from Kaggle containing 3,900 entries with 9 attributes. Determination of the optimal number of clusters was carried out using the Elbow and Silhouette Score methods. The segmentation results show five optimal clusters with the highest Silhouette Score of 0.81. Cluster 0 is the most dominant. PCA visualization shows a fairly clear cluster separation although there is little overlap. This study has succeeded in grouping customers based on purchase volume. Limitations of the study include the uneven distribution of clusters and it is recommended to add demographic attributes and evaluate other algorithms such as DBSCAN.
Segmentasi Produk Berdasarkan Penjualan Menggunakan Algoritma K-Means Clustering(Studi Kasus: Shopee) Kurnia, Dennis Ma'rifatul; Hidayah, Yulistiya Nur; Daniati, Erna Daniati
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 9 No. 3 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/2tmxrk65

Abstract

Dalam lingkungan e-commerce seperti Shopee, terdapat ribuan produk dengan variasi harga dan performa penjualan yang berbeda. Untuk membantu penjual dan manajemen marketplace dalam pengambilan keputusan strategis, diperlukan pendekatan analisis data yang mampu mengelompokkan produk berdasarkan karakteristik relevan. Metode yang dinilai paling efektif dalam penelitian ini adalah analisis K-Means Kluster yaitu salah satu teknik clustering non-hierarkis yang bekerja dengan cara membagi sekumpulan objek atau data ke dalam beberapa kelompok (cluster) berdasarkan tingkat kemiripan karakteristik yang dimilikinya. Produk yang memiliki karakteristik yang sama akan tergolong ke dalam cluster yang sama, sementara produk dengan karakteristik yang berbeda akan dikelompokkan ke dalam cluster yang berbeda. Penelitian ini bertujuan untuk melakukan pengelompokan produk berdasarkan daya jual serta melihat keterkaitan antar variabel produk. Hasil segmentasi ini diharapkan memberikan informasi yang berguna untuk rekomendasi strategi penjualan, meningkatkan efisiensi promosi, dan memberikan insight bisnis yang lebih tajam bagi penjual maupun pihak pengelola marketplace dalam mengembangkan strategi pemasaran berbasis data.
Co-Authors Aulia, Nurun Nihayatur Rifqiya Daniati, Erna Daniati Hidayah, Yulistiya Nur Saputri, Cindy Avitaselly Bambang