Bayes interpretation for smoke-free area cities index - Herdi, Tazkiyah; Dores, Ardiansyah (Bulletin of Social Informatics Theory and Application, 2021)
To control tobacco consumption in Indonesia, whose prevalence increased from 32.8% to 33.8%, local governments issued regional regulations on Smoke-Free Area Policy with support from the central government. Until 2019, there were still 166 cities/districts governments that were yet to issue the regulation out of 514 cities/districts. The increase in the number of active smokers and individuals still exposed to cigarette smoke shows that efforts have not been optimized to reduce tobacco consumption. Furthermore, no control effort has been discovered regarding the level of success of the policies that have been applied. Therefore, this research discusses the surveys carried out in cities/Districts that have applied the smoke-free policy, using indicators such as ideal questions relating to the policy. Naïve Bayes Classifier is one of the Decision Support System (DSS) classification methods used to classify the survey results into good, fair or poor categories to determine whether each city/district has implemented the issued regulation. Based on the results from the classification of the three cities/districts using the classifier, Bogor Regency was classified as good while Lombok and Padang Cities were classified as poor.