Application of Data Mining in Drug Prevention Classification Using the Naïve Bayes Algorithm in BNN Pematangsiantar City - Rosta Dermawan Situmorang; Sumarno Sumarno; Nani Hidayati (JOMLAI: Journal of Machine Learning and Artificial Intelligence, 2022)
The problem of drugs in Indonesia is still something urgent and complex. In the last decade this problem has become widespread. It is proven by the significant increase in the number of drug abusers or addicts, along with the increasing disclosure of drug crime cases, which are increasingly diverse in pattern and the more massive the syndicate network is. Naive Bayes is a simple probabilistic classifier that calculates a set of probabilities by adding up the frequencies and combinations of values from a given dataset. In the classification process to find out the results of prevention activities with urine test activities, which are indicated and not indicated, the authors want to know the overall results with the Naïve Bayes classification technique in order to make it easier to get the overall results of the percentage of patients indicated and not indicated in terms of preventing drug use. Based on the results of the study obtained 2 classifications, namely indicated and not indicated.