Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Vol. 7, No. 2, August 2021, pp. 238-248 ISSN: 2338-3070, DOI: 10.26555/jiteki.v7i2.20997 238 Journal homepage : http://journal.uad.ac.id/index.php/JITEKI Email : jiteki@ee.uad.ac.id Analysis of Random Forest, Multiple Regression, and Backpropagation Methods in Predicting Apartment Price Index in Indonesia I NYM Yoga Saputra 1 , Siti Saadah 2 , Prasti Eko Yunanto 3 1,2,3 Informatics Engineering, Telkom University, Jl.Telekomunikasi, Bandung, Indonesia ARTICLE INFO ABSTRACT Article history: Received June 28, 2021 Revised July 13, 2021 Accepted July 18, 2021 This study focuses on predicting the apartment price index in Indonesia using property survey data from Bank Indonesia. In the era of the Covid-19 pandemic, accurately predicting the sale and purchase price of apartments is essential to minimize the impact of losses, thus making apartment prices attractive to predict. The machine learning approach used to predict the apartment price index are the Random Forest method, the Multiple Regression method, and the Backpropagation method. This study aims to determine which method is more effective in predicting small amounts of data accuracy. The data used is apartment price index data from 2012 to 2019 in the JABODEBEK area. The research will produce prediction accuracy that will determine the effectiveness of the application of the method. The Random Forest method with parameters n_estimators=100 and max_features=”log2” produces an R2 accuracy of 0.977. The Multiple Regression method with a correlation between the selling price and rental price variables is 0.746, and the rental inflation variable is 0.042 produces an R2 accuracy of 0.559. The Backpropagation method with a 1000-4000-1 hidden scheme and 20000 iterations produces an R2 accuracy of 0.996. Therefore, the Backpropagation method is more suitable in this study compared to the other two methods. The Backpropagation method is suitable because it gets almost perfect accuracy, so this method will minimize losses in investing in buying and selling apartments in the Covid-19 pandemic era. Keywords: Backpropagation; Multiple Regression Forecasting; Prediction; Predicted Apartment Prices; Random Forest Forecasting This work is licensed under a Creative Commons Attribution-Share Alike 4.0 I NYM Yoga Saputra, Informatics Engineering, Telkom University, Jl. Telekomunikasi, Bandung, Indonesia Email: nyomanyoga@student.telkomuniversity.ac.id 1. INTRODUCTION Occupancy is an essential element in life included in primary needs. One of the housings that are often encountered is an apartment. Apartments are attractive to buy and sell because of their minimalist design, luxury, and high selling value. Sales of apartment properties during the Covid-19 pandemic are getting tighter due to the economic downturn. According to a simulation conducted by research [1] that the economic impact that occurred reduced economic growth in 2020 from 5% to between 4.2% and -3.5%. The Covid-19 pandemic has resulted in a decline in income in all economic sectors, and the residential property sector has already felt the impact from 2020 [2]. This impact creates problems for the community to be more careful in deciding between selling or buying apartments. This makes apartment prices attractive to predict during the Covid-19 pandemic. This test is carried out using apartment price data taken from a Bank Indonesia survey. Based on the research buying attitude [3], it is concluded that the price variable is one of the determinants of buying attitude of 68.6%, and is also supported by the variables of apartment facilities, location and access, environmental quality, physical quality, and promotion. Prediction results will significantly affect the decision to sell or rent apartments to maximize profit by looking at price inflation. Price inflation is a condition where there is an imbalance in the value of the flow of goods. A good prediction will produce a minimum accuracy of 80%, so the prediction model will help make decisions during the urgent Covid-19 pandemic. This study