Analysis of House Price Using K-Means and Naïve Bayes Methods

Sugiyarto, Arman Prasojo
Hayati, Nur
Mardiani, Eri

Journal Sensi: Strategic of Education in Information System
Vol 10 No 2 (2024): Journal Sensi

This study aims to compare the performance of the K-Means and Naïve Bayes algorithms in analyzing house prices. The dataset used is a house price dataset obtained from observational results. The study was conducted for approximately 2 months, focusing on the implementation of the K-Means and Naïve Bayes algorithms. The data was processed and analyzed using Orange software, and the results were presented in tables and graphs. The analysis results showed that the K-Means algorithm outperformed the Naïve Bayes algorithm with an accuracy value of 30% for the variable y distance to public facilities and 22% for the variable y land area and 82% with Naïve Bayes calculation. Therefore, it can be concluded that the K-Means method is a more effective method for analyzing house prices.