Prediction Of The Accuracy Of Student Boarding House Prices Around Widyatama University Using Neural Network Backpropagation Algorithm

Abstract

Boarding room rental services are a business that has been around for a long time. One of the target consumers of this business is from students who plan to stay temporarily during the lecture period so that they are close to the campus location where the student is studying. A strategic location such as in the area of ​​one of the campuses in Bandung, namely the Widyatama University Campus, provides added value for boarding owners and also consumers who will rent, but there are several problems in this boarding rental business, namely the lack of knowledge of information on boarding rental prices around the area. campus due to the lack of effort made by boarding house owners in surveying boarding rental price data. In addition, boarding house owners find it difficult to determine the range of rental prices in accordance with boarding facilities. This problem is also experienced by prospective boarding house tenants in estimating whether the rental price offered is ideal and comparable to the facilities that will be obtained. This research conducts trials and predicts the range of rental prices in accordance with the parameters of the facilities that have been defined based on a survey of boarding price data via the internet and predicts how accurate the model used is. The method used is an artificial neural network with a backpropagation algorithm. The results of this study indicate that the 14-12-10-2-1 model with an epoch of 100 produces an accuracy rate of 87.18% and an MSE of 0.0033.