Developing Machine Learning Algorithms for Predicting House Prices in Surabaya Using IBM SPSS Modeler

Using PHP and IBMs SPSS Modeler for data mining and analytics, the au-thors collected and processed housing data from Rumah123 in Surabaya. A total of 10,336 data points were divided into two clusters. Three machine learning (ML) models were developed using SPSS Modeler for 2,460 data points. The results indicate that the Artificial Neural Network (ANN) model provided the most consistent correlation across all scenarios, while the Sup-port Vector Machine (SVM) model performed the worst. The Classification and Regression Tree (CART) model showed good performance in both train-ing and testing for the larger cluster but did not perform as well with the smaller cluster. Overall, ANN and CART models can be used to predict housing prices, with ANN offering higher accuracy.