Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Bimo Aryo Pangestu
Unknown Affiliation
Humanities Education Languange, Linguistic, Communication & Media Mathematics Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Prediksi Unit Price Properti Menggunakan Algoritma Neural Network Berbasis RapidMiner: Penelitian Bimo Aryo Pangestu; Hasbi Firmansyah; Ali Sofyan; Wahyu Asriyani
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 2 (October 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i2.4439

Abstract

This study aims to predict property unit price using the Neural Network algorithm based on RapidMiner. The dataset used consists of property-related attributes, with unit price as the target variable. The research stages include attribute role assignment, data normalization, and data partitioning using the estimation method with a 70:30 split between training and testing data. The Neural Network model is built using the training data and applied to the testing data to generate unit price predictions. Model performance is evaluated using the Performance (Regression) method with the Root Mean Squared Error (RMSE) metric. The experimental results show that the Neural Network algorithm is able to predict property unit price accurately, as indicated by an RMSE value of 0.028. The low RMSE value indicates a small difference between the actual and predicted unit price values, demonstrating that the proposed model has good predictive performance. Therefore, it can be concluded that the Neural Network algorithm based on RapidMiner is effective for predicting property unit priprice and can be used as an alternative approach in property price analysis.
Co-Authors Ali Sofyan Asriyani, Wahyu Hasbi Firmansyah