Application of the K-Nearest Neighbor Machine Learning Algorithm to Preduct Sales of Best-Selling Products
DOI:
https://doi.org/10.47709/brilliance.v4i1.4063Keywords:
K-Nearest Neighbor ALgorithm, Machine Learning, SalesAbstract
The development of increasingly intense competition in the business world, accompanied by advances in information technology, has brought retail companies into a situation of tighter and more open competition. PT LG Innotek Indonesia is the only company that produces tuners in Indonesia. Looking at consumer demand, PT LG Innotek must improve product quality, and add products that consumers like and frequently purchase. For this reason, PT LG Innotek Indonesia needs an analysis that can help the company identify products that tend to sell well. This analysis can be carried out through the application of machine learning algorithms, especially the K-Nearest Neighbor method. The aim of this research is to find out how the KNN algorithm performs in predicting products that are selling well and not selling well at PT LG Innotek Indonesia. Based on the analysis results, prediction results were obtained with an accuracy level of 94.74% and an error rate of 5.26%. With this high level of accuracy and low error rate, it can be concluded that the K-Nearest Neighbor method is effectively used to predict sales of PT LG Innotek Indonesia's best-selling products.
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