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Integrated Framework for House Price and Price-Zone Prediction with Natural Language Processing Chatbot

by Rodiah, Diana Tri Susetianingtias, Eka Patriya
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 90
Year of Publication: 2026
Authors: Rodiah, Diana Tri Susetianingtias, Eka Patriya
10.5120/ijca2026926592

Rodiah, Diana Tri Susetianingtias, Eka Patriya . Integrated Framework for House Price and Price-Zone Prediction with Natural Language Processing Chatbot. International Journal of Computer Applications. 187, 90 ( Mar 2026), 36-44. DOI=10.5120/ijca2026926592

@article{ 10.5120/ijca2026926592,
author = { Rodiah, Diana Tri Susetianingtias, Eka Patriya },
title = { Integrated Framework for House Price and Price-Zone Prediction with Natural Language Processing Chatbot },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2026 },
volume = { 187 },
number = { 90 },
month = { Mar },
year = { 2026 },
issn = { 0975-8887 },
pages = { 36-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number90/integrated-framework-for-house-price-and-price-zone-prediction-with-natural-language-processing-chatbot/ },
doi = { 10.5120/ijca2026926592 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-03-20T22:55:35.614789+05:30
%A Rodiah
%A Diana Tri Susetianingtias
%A Eka Patriya
%T Integrated Framework for House Price and Price-Zone Prediction with Natural Language Processing Chatbot
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 90
%P 36-44
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Accurate housing price estimation is essential for supporting real estate decision making and urban economic planning. This study proposes an integrated framework that combines ensemble machine learning models with a Natural Language Processing (NLP) based conversational interface for housing price prediction and price-zone classification in the JABODETABEK region. A dataset of 3,553 property listings was preprocessed through data cleaning, missing value handling, outlier detection using the Interquartile Range (IQR) method, logarithmic transformation, and feature engineering. Comparative experiments were conducted using Linear Regression, Random Forest, Gradient Boosting, and XGBoost for regression tasks, and Random Forest, Decision Tree, K-Nearest Neighbors, and Gradient Boosting for classification tasks. XGBoost achieved the best regression performance with approximately 96% predictive accuracy, while Random Forest demonstrated superior classification performance with an accuracy of 87.46%. The NLP intent classification module, developed using a Bag-of-Words representation and Multinomial Naïve Bayes, achieved 94.82% training accuracy and 90.20% testing accuracy. All components were integrated into a Command Line Interface (CLI)-based chatbot capable of interpreting user queries and generating automated price estimations and price-zone classifications. The results demonstrate that the proposed unified framework provides robust predictive performance while enhancing user accessibility through conversational interaction.

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Index Terms

Computer Science
Information Sciences

Keywords

Chatbot Ensemble Learning House Price Prediction Random Forest NLP-based Query Classification

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