APPLICATION OF BINARY LOGISTICS REGRESSION AND RANDOM FOREST TO CIGARETTE CONSUMPTION EXPENDITURE IN GORONTALO REGENCY 2022 - Mohamad Taufik Hamani; Dewi Rahmawaty Isa; Salmun K. Nasib; Hasan S. Panigoro; Isran K. Hasan; Nisky Imansyah Yahya (Department Statistics, Faculty Mathematics and Natural Science, UNIMUS, 2025)
The goal of this research is to predict or identify an object's class using its available attributes through classification. The aim of this research is to use the random forest method to develop a classification model and the binary logistic regression method to discover significant determinants in cigarette consumption expenditure in Gorontalo Regency. The findings indicated that the size of the home, the number of family members, and the head of the household's educational attainment all had a considerable impact. Only the household head's educational attainment, however, consistently influences the model and satisfies the goodness of fit requirements. In contrast, the random forest model outperformed binary logistic regression in the classification analysis when classification characteristics including accuracy, precision, recall, and f1-score were assessed. Consequently, random forest was found to be the most effective classification model in this investigation.