MODEL KLASIFIKASI REGRESI LOGISTIK BINER UNTUK LAPORAN MASYARAKAT DI OMBUDSMAN REPUBLIK INDONESIA - Daswati, Oktaviyani; Indahwati, Indahwati; Erfiani, Erfiani; Fitrianto, Anwar; Aliu, Muftih Alwi (Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, 2024)
A classification model is needed to predict data into the right class according to the pattern of previous data. Binary Logistic Regression can be used in building classification models, even though the independent variables are categorical scale data. Through binary logistic regression, it can also be seen which category of independent variables influences the response variable. Public complaint reports at the Ombudsman of the Republic of Indonesia are classified into reports that found maladministration and not. The Binary Logistic Regression model with several categorical independent variables related to the public complaint reports data applied resulted in a classification model with an overall classification accuracy of 66.08% and a sensitivity of 75.31% in estimating the presence of maladministration findings in the submitted public complaint reports. Based on the 95% confidence level of the model, it is known that the factors that influence the occurrence of maladministration are the Group of Reportees, the Substance of the Report, the Method of Submission, the Request for Confidentiality, and the Location of the Inspection Office. This model can be used as a reference to reduce the incidence of maladministration cases in public service providers by focusing socialization and education on categories that have a real influence on each of these factors