Analisis Prediksi Hasil Pemilu Legislatif DPR RI DKI Jakarta Tahun 2024 Menggunakan Metode Random Forest dan Gradient Boosting - Effendy, Rangga Febrian; Susanto, Agung Budi; Anggai, Sajarwo (Jurnal Ilmu Komputer, 2024)
In general elections, it is closely related to predictions, predictions play an important role in obtaining results in future legislative elections. Predicting general election results can be done through a series of processes to find patterns and knowledge from a set of data using data mining techniques. To get accurate prediction results in the future, a method is needed that can be used as predictive modeling. This research aims to find out the results of model testing and predictions for the 2024 DPR RI DKI Jakarta legislative election using random forest and gradient boosting methods and to find out patterns and knowledge from the prediction results themselves. Based on the model testing results, the gradient boosting method has an accuracy value of 95.8%, precision 72.2% and recall 61.9%. Meanwhile, random forest has an accuracy value of 95.4%, precision 63.6% and recall 33.3%. The pattern and knowledge from the prediction results is that the elected legislative candidates on average are in serial numbers 1 and 2, have valid votes starting from 63,529, are male and have a doctoral degree.