Application of the KNN Algorithm to Assess Customer Satisfaction at A2 Collection Sei Silau Timur
DOI:
https://doi.org/10.35870/ijmsit.v6i1.6813Keywords:
Utilization of the K-Nearest Neighbor (KNN) Algorithm, Data Mining Concept, Customer Satisfaction Classification, A2 Collection StoreAbstract
The development of information technology and data mining in recent years has changed the way retail businesses, including small and medium-scale fashion businesses, collect, analyze, and utilize customer data to improve services and marketing strategies. In addition, through the main discussion carried out in this study, it aims to be able to analyze the factors that influence the level of customer satisfaction at Amel Fashion Prapat Janji based on the attributes of product quality, price, comfort of use, and service. In addition, this study develops and applies the K-Nearest Neighbor (K-NN) algorithm to classify the level of customer satisfaction more objectively, measurably and data-based. And in addition, for the Research Method section used in this study, a qualitative approach was chosen because the focus of this study is to explore the meaning, perception, and direct experience of business actors in the marketing and distribution process. So based on that, this study shows the results that the application of the K-Nearest Neighbor (KNN) algorithm in the customer satisfaction classification system at A2 Collection Sei Silau Timur is able to provide an effective solution in managing and analyzing customer evaluation data. This website-based system has succeeded in changing the assessment process that was previously carried out manually to be more structured, systematic, and easily accessible. Based on the system's calculations, the resulting distance values, such as 2.354, categorized as "Satisfied" and 2.325, categorized as "Dissatisfied," indicate that the proximity of attribute values significantly influences the classification results. Although the difference in distance values is relatively small, the system is still able to determine the class based on the dominance of the nearest neighbor data.
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