<![CDATA[Low levels of product literacy among consumers often cause a mismatch between the specifications of the purchased mobile phone and their actual needs. This problem is common, especially in stores that provide a wide selection of products with technical information that is poorly understood by prospective buyers. Lack of understanding of features such as processor type, camera quality, RAM capacity, and internal memory makes the purchasing process less than optimal. This study aims to improve consumer literacy by developing a mobile phone purchase recommendation system based on the C4.5 algorithm. The method used is a data mining approach with stages of collecting historical consumer purchase data, preprocessing data to eliminate noise, labeling purchase decisions, forming a decision tree model using the C4.5 algorithm, and evaluating model performance using a confusion matrix. The dataset used includes 19 types of mobile phones from a local store with attributes such as processor, main camera, front camera, RAM, internal memory, and price. The method used in this study uses the C4.5 algorithm. The results of the study show that the C4.5 algorithm is able to classify data with satisfactory accuracy and produce a relevant recommendation system. In addition to providing product suggestions, this system also includes a logical explanation of each decision taken, thereby increasing consumer understanding. Thus, this system not only helps the decision-making process, but also acts as an educational medium in increasing consumer literacy regarding mobile phone product specifications that suit their needs and budget.]]>
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