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.
翻译:寄宿房是最重要的要求,特别是对于远离城市、其原籍地或住所的大学生来说,寄宿房是最重要的要求。然而,我们现在看到的问题是印度尼西亚学习场所分布不均,75%的最好的顶级教育机构来自爪哇岛。因此,寻找寄宿房租金的学生需要更加努力地比较所希望的方方面面。他们需要逐个地调查他们想要的寄宿房,尽管他们可以在线调查,但仍然需要更加努力地关注所需要的设施。因此,我们随后创建了一个移动应用程序,通过比较几个变量,即城市、地区、寄宿房类型和设施,根据学生的需求预测价格。因此,学生可以很容易地估计理想价格。这项研究的结果证明,我们已经成功地根据我们确定的变量预测了出租房的价格,并且用深神经网络回归模型来模拟变量。