PENERAPAN ALGORITMA K-NEAREST NEIGHBORS UNTUK REKOMENDASI PRODUK PADA PENJUALAN HANDPHONE BERBASIS WEB

Abstract

The rapid development of information technology is driving the transformation of 
mobile phone sales towards digital. However, consumers still often experience 
difficulties in determining the right mobile phone for their needs and budget due 
to the wide variety of specifications and price ranges on the market. This condition 
creates the need for a decision support system that can provide objective, relevant 
product recommendations that meet user preferences. This study aims to design 
and implement a web-based mobile phone recommendation system using the K
Nearest Neighbors (KNN) algorithm to help consumers choose the most suitable 
product. The method used is applied research with a system development 
approach that includes needs analysis, design, implementation, and testing. The 
research data are mobile phone specifications obtained from the Jakarta Cell 
Store, while the research subjects are system users. The KNN algorithm is 
implemented using Euclidean distance calculations based on price, RAM, storage 
capacity, and camera attributes to measure the level of similarity between user 
preferences and available products. The results of system testing using the black 
box method show that all functions run as needed, and all main modules operate 
properly. The system is able to produce relevant and accurate mobile phone 
recommendations, so the application of the KNN algorithm is proven effective in 
assisting purchasing decision making and has the potential to increase user 
satisfaction and sales effectiveness.