Boarding House Renting Price Prediction Using Deep Neural Network Regression on Mobile Apps

TL;DR

This paper presents a deep neural network regression model integrated into a mobile app to predict boarding house rental prices based on various factors, aiding students in Indonesia.

Contribution

It introduces a novel mobile application utilizing deep learning for accurate boarding house price prediction based on multiple user-selected variables.

Findings

High accuracy in price prediction demonstrated

Effective comparison of boarding options for students

Model successfully incorporates multiple variables

Abstract

Boarding house is the most important requirement, especially for college students who live far away from the city, place of his origin or house. However, the problem we see now is the uneven distribution of study places in Indonesia which 75% of the best top educational institutions come from the island of Java. So, students who are looking for boarding houses rent requires more effort in comparing the various aspects desired. They need to survey one by one to the boarding house they want, even though they can survey online, it still requires more effort to pay attention to the desired facilities one by one. Therefore, we then created an Mobile Application that can predict prices based on student needs by comparing several variables, namely city, area, type of boarding house, and facilities. So, students can easily estimate the ideal price. The results of this study prove that we have succeeded in predicting prices for boarding houses rent well based on the variables we have determined, and modeling that variables using Deep Neural Network Regression.