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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence
Nani Hidayati
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Computer Science & IT Engineering

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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 Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (485.41 KB) | DOI: 10.55123/jomlai.v1i4.1667

Abstract

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.
Application of Associations Using the Apriori Algorithm to Analyze Consumer Purchase Patterns at Grocery Stores Oka Ristawaty Sirait; Sumarno Sumarno; Nani Hidayati
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (578.322 KB) | DOI: 10.55123/jomlai.v1i4.1679

Abstract

The grocery store sells various types of ingredients for everyday life. Every day many customers shop at the grocery store. Every item sold at the Grocery Store will generate sales data, but this data cannot be utilized optimally. So we need a data analysis to help the Grocery Store gain knowledge of sales patterns in a certain period. The algorithm used as the primary process of analyzing the sale of ingredients in grocery stores is an a priori algorithm using the application of a minimum support value of 50% and a minimum confidence value of 70%, which meets the minimum support value and minimum confidence value, and sales transactions to find association rules. The Apriori algorithm test results will show results that have met the needs and determine the pattern of purchasing materials at the Grocery Store based on the items that customers most frequently purchase.
Co-Authors Oka Ristawaty Sirait Rosta Dermawan Situmorang Sumarno Sumarno