Naufal, Fadlan (2024) ANALISIS VISUALISASI DAN PENGELOMPOKAN BARANG KURANG DIMINATI DALAM E-COMMERCE MENGGUNAKAN ALGORITMA K-MEANS. S1 thesis, Universitas Mercu Buana-Menteng.
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Abstract
Penelitian ini bertujuan untuk menganalisis dan mengelompokkan produk yang kurang diminati dalam e-commerce di Indonesia menggunakan algoritma K-Means. Dengan perkembangan pesat e-commerce di Indonesia, persaingan antar penjual semakin ketat, sehingga penting untuk mengetahui performa penjualan produk yang kita tawarkan. Mengetahui produk mana yang penjualannya kurang baik memungkinkan kita untuk memperbaikinya dari segi pemasaran atau kualitas produk itu sendiri. Dalam penelitian ini, data penjualan produk dianalisis menggunakan algoritma K-Means clustering. Algoritma ini efektif dalam mengelompokkan data berdasarkan karakteristik tertentu, sehingga memudahkan identifikasi produk dengan penjualan rendah. Hasil analisis menunjukkan kelompok produk yang kurang diminati oleh konsumen, memberikan informasi berharga bagi pengelola e-commerce untuk merancang strategi pemasaran dan peningkatan produk yang lebih efektif. Melalui pendekatan ini, diharapkan penjual dapat meningkatkan daya saing mereka dengan memahami dan mengatasi kekurangan dalam penjualan produk, serta mengoptimalkan strategi bisnis berdasarkan data yang dihasilkan dari analisis K-Means clustering. This study aims to analyze and categorize products that are less popular in e-commerce in Indonesia using the K-Means algorithm. With the rapid development of e-commerce in Indonesia, competition among sellers is becoming increasingly fierce, making it important to understand the sales performance of the products we offer. Knowing which products have poor sales allows us to improve them in terms of marketing or product quality itself. In this study, sales data of products were collected from e-commerce platforms and analyzed using the K-Means clustering algorithm. This algorithm is effective in grouping data based on specific characteristics, thus facilitating the identification of products with low sales. The results of the analysis indicate groups of products that are less popular among consumers, providing valuable information for e-commerce managers to design more effective marketing strategies and product improvements. Through this approach, it is hoped that sellers can enhance their competitiveness by understanding and addressing deficiencies in product sales, and optimizing business strategies based on data generated from K-Means clustering analysis.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41520120023 |
| Uncontrolled Keywords: | Analisis visualisasi, Algoritma K-Means, Clustering, Penjualan produk. Visualization analysis, K-Means algorithm, Clustering, Product sales |
| 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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | OKTAFIYANI AZ ZAHRO |
| Date Deposited: | 07 Mar 2025 04:47 |
| Last Modified: | 07 Mar 2025 04:47 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/94713 |
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