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All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics JSR : Jaringan Sistem Informasi Robotik Jurnal Digit : Digital of Information Technology Media Jurnal Informatika Jurnal Masyarakat Indonesia Journal of Community Empowerment and Innovation Journal of Artificial Intelligence and Digital Business Surya Informatika Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Febima, Mesi
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Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Journal : Journal of Information Systems and Informatics

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Predictive Analytics on Shopee for Optimizing Product Demand Prediction through K-Means Clustering and KNN Algorithm Fusion Febima, Mesi; Magdalena, Lena
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.720

Abstract

This study focuses on predictive analysis in the context of the Shopee market, aiming to optimize product demand forecasting through the combination of K-Means clustering and KNN algorithms. With the exponential growth of e-commerce platforms like Shopee, accurately predicting product demand is becoming increasingly important for inventory management and marketing strategies. In this research, we propose a novel approach that combines the strengths of K-Means clustering and the KNN algorithm to improve demand prediction accuracy. By leveraging K-Means clustering to group similar products into two clusters, namely “Low Interest” with 64 data points and “High Interest” with 25 data points, we then apply the KNN algorithm to predict demand within each cluster. The KNN algorithm produces two classifications: Low Sales and High Sales. Based on tests using the KNN algorithm with k values of 3, 5, and 7, it was demonstrated that the product “Soraya Bedsheet Cotton Gold Motif Dallas Ask Grey Tua” can be predicted to fall under “High Sales.” The sales prediction accuracy rate for Shopee marketplace products is 96%. The implications of these findings indicate that the combination of K-Means and KNN algorithms can improve the accuracy of product demand predictions and optimize inventory and marketing strategies.
Co-Authors Alhafidz, Dennis Cesar Ananda, Fikri Anggara Putra, Dwi Pasha Christina, Stefanny Chritviona Parera, Shalom Fahrudin, Rifqi Jamilah, Laeli Juliana, Jidan Lena Magdalena, Lena Loka, Diah Pita Marsani Asfi Mita, Shella Muhammad Hatta Muhammad Hatta Nabillah, Sofy Nakeisha Wahyudi, Nayla Nur'Faradila, Dellatia Ayu Nurpatimah, Suci Ramadhan, Abdan Syakur Ramdhani, Fathul Sahputra, Illal Dwi Trivena, Stephani Unang Solihin Winurcahyono, Alexander