Analisis Kebakaran pada Hutan dan Lokasi Lahan di Provinsi Riau Menggunakan Metode C4.5 - Nalda Kresimo Negoro; Mardiana Mardiana; M. Izul Ula; Fajar Dwi Insani (Jurnal Informatika Universitas Pamulang, 2022)
Data mining is a step of the process that is useful and necessary to analyze the data that has been obtained from a particular situation with mathematical calculations. A decision tree (decision tree) is an algorithm that is widely used in data mining classification. One of the algorithms that can help produce decision tree outputs is the C4.5 algorithm. Data mining consists of preprocessing, evaluating patterns, and presenting knowledge from an application. The dataset used in forest fires is taken from the BMKG website and classified according to its attributes so that the data can be processed to produce output for decision-making. The classification process used to process datasets with specific methods can predict events such as weather forecasts and traffic jams to predict natural disasters. Forest fires are very detrimental disasters because they can pollute the environment, interfere with health such as breathing and vision, and weaken the economy of a region. This study aims to apply the C4.5 algorithm, which can analyze and predict forest fires in the Riau province. The implementation of the C.45 algorithm method with an average value attribute has an accuracy value of 92.54% and a decision tree, which is an accuracy value of 99.2% with an equation test and test results with a confusion matrix.