<![CDATA[Determining the price of a mobile phone that will be released to the market cannot be based on assumptions alone. This problem can be overcome by utilizing machine learning. In this study, what is predicted is not the exact price, but rather the price range of a cellphone based on the specifications that are its attributes. In machine learning, the Deep Learning ANN model will be used to predict the price range of a mobile phone. To understand the relationship between features and labels, the Univariate feature selection method SelectKBest is used which will calculate the correlation value between features and labels. In this study, the best performance was obtained from the ANN model with feature selection and hyperparameter tuning, the evaluation of performance metrics obtained the highest accuracy of 97.5%. Experiments were conducted by building several models to compare until there was one model that performed well in processing training and validation data. Model evaluation is presented using confusion metrics with various types of performance metrics: accuracy, precision, recall and f1-score. This study also aims to evaluate the effectiveness of the SelectKBest feature selection method in improving model accuracy and testing various hyperparameter configurations to obtain the best performance.]]>
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