OPTIMASI STRATEGI PENJUALAN MELALUI INTEGRASI METODE K-NEAREST NEIGHBORS (KNN) DAN REGRESI LINIER UNTUK MENGIDENTIFIKASI PRODUK TERLARIS

AGUSTRIA, BELLYAN and Ermatita, Ermatita (2025) OPTIMASI STRATEGI PENJUALAN MELALUI INTEGRASI METODE K-NEAREST NEIGHBORS (KNN) DAN REGRESI LINIER UNTUK MENGIDENTIFIKASI PRODUK TERLARIS. Masters thesis, Sriwijaya University.

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Abstract

In today's competitive digital era, optimizing sales strategies is crucial for sustaining online business growth. This study aims to identify best-selling products by integrating the K-Nearest Neighbors (KNN) algorithm and linear regression. The research utilizes sales data from the fashion brand "Zaskia Sungkar Jakarta" on the Shopee e-commerce platform. The KNN method is applied to classify products into best-selling or non-best-selling categories based on attribute similarities, while linear regression is employed to predict the relationship between sales quantity and variables such as rating, reviews, discounts, and favorites. Evaluation results show that the KNN model achieved a classification accuracy of 80%, while the linear regression model produced an RMSE value of 33.09. The study concludes that the integration of KNN and linear regression effectively supports sales strategy decisions by providing predictive insights on potentially best-selling products. These findings are expected to aid in stock management, promotional planning, and customer loyalty enhancement.

Item Type:	Thesis (Masters)
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Bellyan Agustria
Date Deposited:	25 Jul 2025 14:19
Last Modified:	25 Jul 2025 14:19
URI:	http://repository.unsri.ac.id/id/eprint/180669

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