Designing a Product Classification Dashboard for Marketing Strategy Using K-Nearest Neighbor - Kadek Intan Cahya Putria; Anak Agung Ngurah Hary Susila; Ni Putu Sutramiani (Jurnal Riset Multidisiplin Edukasi, 2025)
The development of information technology has driven the use of sales data to support data-driven business decision-making. This study aims to design a dashboard to classify Orlenalycious Padangsambian's products using the K-Nearest Neighbor (K-NN) algorithm to determine more accurate marketing strategies. The methods used include collecting sales data from the Moka POS system, data preprocessing, classification using the K-NN algorithm with K=5, and visualizing the classification results in a Streamlit-based dashboard. The classification results divide the products into three categories: Highly Popular, Popular, and Fairly Popular. The proposed marketing strategy refers to the 4P Marketing Mix, where highly popular products are promoted intensively, popular products are pushed through advertising, and fairly popular products are evaluated or promoted through bundling. The resulting dashboard displays informative visualizations such as pie charts and bar charts to facilitate the analysis of sales trends and product performance. This study provides a solution for Orlenalycious to design more efficient and effective data-driven marketing strategies, as well as offering an easier way to monitor and evaluate product performance in real-time.