Analisis Data Mining Dalam Pemilihan Smartphone dan Klasifikasi di Berbagai Perangkat Menggunakan Random Forest - Ananda Aulia; Sri Wahyuni (Bulletin of Information Technology (BIT), 2024)
Abstract− Smartphone technology continues to develop rapidly, driving the need for effective analysis methods to assist users in selecting devices that suit their needs. This research aims to implement data mining using the Random Forest method in the process of selecting smartphones and classifying devices based on their technical specifications. The Random Forest method was chosen because of its reliable ability to handle data with a large number of attributes, produce an accurate classification model, and minimize the risk of overfitting. The dataset used includes technical specifications of various smartphones, such as camera resolution, chipset, RAM capacity, screen resolution, and support for 4K video recording. The research process involved data collection, pre-processing to handle missing values ​​and data transformation, as well as model training using the Random Forest algorithm.  The research results show that the Random Forest method is able to classify devices with high accuracy, helping users determine smartphones that meet their criteria, such as support for 4K video recording and overall performance. Additionally, this research provides insight into the importance of certain attributes in smartphone selection. Thus, implementing data mining using Random Forest can be an effective solution in supporting data-based decision making in the field of consumer technology. Keywords: Data Mining, Random Forest, Smartphone, Classification, Technical Specifications