<![CDATA[The rapid advancement of technology and communication over time has significantly increased the utilization and application of technology in various fields and aspects of life, such as the use of smartphones. Mobile phone classification is often performed using linear regression methods to predict prices based on various features. However, the accuracy of mobile phone price classification using linear regression, as well as the impact of parameters such as maximal_depth and criteria types like gain_ratio, accuracy, and gini_index on the classification results, remains unknown. The objectives of this research are to determine the accuracy of mobile phone price classification using the linear regression algorithm and to understand the influence of the maximal_depth parameter and criterion types (gain_ratio, accuracy, and gini_index) on the classification results. The classification method that will be applied in this research is linear regression. The results of this research are expected to provide a clear picture of the accuracy of mobile phone price prediction using the linear regression algorithm, as well as how the parameters maximal_depth and criterion types contribute to the model's performance. Consequently, this research will contribute to the selection and application of more appropriate classification methods for predicting mobile phone prices Keywords: data mining, rapidminer, classification, cellphone, linear regression algorithm]]>
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