K-NEAREST NEIGHBOR (KNN) ANALYSIS FOR CLOTHING SALES CLASSIFICATION BASED ON MATERIALS USED

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Published Aug 4, 2023
https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v7i1.3903
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Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER

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Jepri Banjarnahor
Universitas Prima Indonesia
Regina Siregar
Universitas prima indonesia
Christian Frederic Lumbantobing
Universitas Prima Indonesia
Muhammad Alfathan Ridho
Universitas Prima Indonesia
Muhammad Fikri Akbar Zuhdi
Universitas Prima Indonesia

Abstract

Abstract Classification is a method to see the behavior and characteristics of certain groups. The K-nearest neighbor method is a learning algorithm for classifying new data based on the K-Nearest Neighbor majority class. The main purpose of this algorithm is to classify new objects based on attributes and training samples. In today's digital era, competition in the business world is getting tougher and growing rapidly, especially when it comes to online marketing systems. Every market driver must always pay attention to the needs and desires of consumer satisfaction when buying products from online stores. However, the problem that consumers often complain about is the use of clothing size charts in online stores that do not match the consumer's body size. This study aims to reduce the frustration of consumers buying clothes online and in such a way that products do not have to be returned via the internet. Based on these problems, these conditions must be improved by selecting clothes to achieve optimal customer satisfaction. This application was built using the K-Nearest Neighbor (KNN) method and Profile Matching to help you determine what clothes are most suitable for your consumer size.

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How to Cite
Banjarnahor, J., Siregar, R., Lumbantobing, C. F., Ridho, M. A., & Zuhdi, M. F. A. (2023). K-NEAREST NEIGHBOR (KNN) ANALYSIS FOR CLOTHING SALES CLASSIFICATION BASED ON MATERIALS USED. Jurnal Sistem Informasi Dan Ilmu Komputer, 7(1), 42–49. https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v7i1.3903

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