Statistical integration in excise supervision: Multinomial logistic regression for detecting risk factors of tobacco factory excise violations - Riansyah, Boy; Nisa Nur Aisyah; Anwar Fitrianto; Aam Alamudi (Universitas Islam Negeri Raden Intan Lampung, 2025)
Excise tax serves as a fiscal instrument used by the government to control the consumption of goods with negative externalities for society and the environment, such as tobacco and alcoholic beverages. In Indonesia, tobacco excise remains the largest contributor to national excise revenue, amounting to IDR 216.9 trillion in 2024, or approximately 95.8% of the total. Monitoring violations in the tobacco excise sector is crucial to safeguarding state revenue and ensuring regulatory effectiveness. In the context of classifying violations with more than two categories, multinomial logistic regression is an appropriate statistical method for analyzing the influence of independent variables on the probability of each violation type. This study aims to classify types of excise violations based on internal characteristics of tobacco manufacturers using multinomial logistic regression. The data were obtained from enforcement documentation in 2023 by the Directorate General of Customs and Excise, with four categories of violations serving as the response variable. The issue of class imbalance was addressed by comparing oversampling and weighting techniques. Evaluation results indicate that oversampling produced superior model performance. Partially, variables such as business entity type, asset ownership status, and company age significantly influenced the likelihood of specific violations. Companies with non-permanent asset ownership and complex organizational structures tend to have a higher risk of non-compliance. These findings underscore the importance of implementing risk-based supervision that considers operational profiles as key indicators of potential violations.