Laptop Price Prediction with Machine Learning Using Regression Algorithm - Siburian, Astri Dahlia; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf; Indra, Evta (Jurnal Sistem Informasi dan Ilmu Komputer, 2022)
Since the COVID-19 pandemic, many activities are now carried out in a Work From Home (WFH) manner. According to data from the Central Statistics Agency (BPS) of East Java, in 2021, large and medium-sized enterprises (UMB) who choose to work WFH partially are 32.37%, and overall WFH is 2.24% (BPS East Java, 2021 ). With this percentage of 32.37%, many people need a work device (in this case, a laptop) that can boost their productivity during WFH. WFH players must have laptops with specifications that match their needs to encourage productivity. To prevent buying laptops at overpriced prices, a way to predict laptop prices is needed based on the specified specifications. This study presents a Machine Learning model from data acquisition (Data Acquisition), Data Cleaning, and Feature Engineering for the Pre-Processing, Exploratory Data Analysis stages to modeling based on regression algorithms. After the model is made, the highest accuracy result is 92.77%, namely the XGBoost algorithm. With this high accuracy value, the model created can predict laptop prices with a minimum accuracy above 80%.