Particle Swarm Optimization – Based on Decision Tree of C4.5 Algorithm for Upper Respiratory Tract Infections (URTI) Prediction. (March 2019) - Tarigan, Dwi Meylitasari Br.; Palupi Rini, Dian; Sukemi,
Abstract: Data mining is related to searching data to find patterns or knowledge from the whole data. It turns out that a large data set can produce a data whose results can provide new knowledge information. Data mining is an important step in the process of finding knowledge. In this study will be discussed about data mining design using C4.5 algorithm to predict acute or non-acute URTI in children by selecting the candidate criteria used in this study so that it can contribute to the medical team in the health environment to know and follow up patients who affected by URTI. The C4.5 algorithm is used to obtain information by selecting or separating characteristics. Giving attribute weight to the C4.5 algorithm using Particle Swarm Optimization can improve the accuracy of the C4.5 Algorithm performance and can also be influenced by the selection of the right attributes, the more attributes used will result in a long time and costs that will reduce the accuracy and performance slower.