Prediksi Harga Laptop Berdasarkan Spesifikasi Menggunakan Algoritma K-Nearest Neighbour

Abstract

The development of information technology is driving the increasing need for laptops as the primary computing device for various groups. Laptop prices vary greatly, influenced by specifications such as processor type, RAM capacity, storage, and additional features. This research aims to develop a laptop price prediction system based on specifications using the K-Nearest Neighbor (K-NN) algorithm, integrated into a web-based application with a Laravel API backend and MySQL database. The K-NN algorithm works by finding data with the most similar characteristics to determine the price estimate. The laptop price dataset used underwent preprocessing, including converting categorical data to numerical data so it could be processed by the model. Testing was conducted with varying K values of 3, 5, 7, and 9, using the metrics of accuracy, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The test results showed that K = 5 provided the best performance with an accuracy of 0.89, MAE of 0.15, and RMSE of 0.18. Too small a K value makes the model sensitive, while too large a K value makes the model too general, making K = 5 the optimal point. This model is capable of producing predictions with a low error rate and high accuracy, proving the effectiveness of K-NN in predicting laptop prices based on specifications such as processor brand, processor type, RAM, storage capacity, and screen size. The success of this research also proves that the integration of Flutter, Laravel API, and MySQL can support the development of a responsive, interactive, and beneficial laptop price prediction system for both potential buyers and sellers