Implementasi Framework Streamlit Sebagai Prediksi Harga Jual Rumah Dengan Linear Regresi

  • Gita Ayu Syafarina Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin
  • Zaenuddin Zaenuddin Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin
Keywords: linear regression, prediction, selling price, artificial intelligence, streamlit

Abstract

This research aims to develop an Artificial Intelligence (AI)-based application using the Streamlit framework to predict house sale prices in Banjarmasin City using Linear Regression methodology. The increase in demand and supply of properties in Banjarmasin City poses a complex challenge in determining house sale prices. The Linear Regression method was chosen as the primary analytical tool to identify factors influencing house sale prices. This application utilizes historical data of house sale prices and variables such as land area, building area, number of rooms, proximity to public facilities, and geographical location as inputs for the Linear Regression model. Furthermore, the Streamlit framework is employed to create an interactive and user-friendly interface for end-users. The outcome of this research is an AI application that assists potential buyers or sellers in Banjarmasin City in determining competitive prices. By inputting information about the property being evaluated, users can obtain a more accurate estimated sale price based on factors identified by the Linear Regression model. In testing the application, actual house sale price data from Banjarmasin City was used to assess the model's accuracy. The testing results indicate that the application is capable of providing reasonably accurate price estimates, achieving an accuracy level of 67.8%. Thus, this AI application holds the potential to be a valuable tool in the property industry in Banjarmasin City, aiding stakeholders in more informed and data-driven decision-making regarding house sale prices. Additionally, this application could serve as a foundation for further developments in AI research and property price analysis.

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Published
2023-12-31
Section
Articles