Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

drug consumption; classification; machine learning; confusion matrix; AUC curve

Caseand s of drug abuse are on the rise, with many users entering the addiction phase, often resulting in overdose and death.
Drugs are chemical compounds that are capable of affecting biological functions, can induce feelings of happiness and reduce
pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and
non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug
users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consists of
1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine
learning methods, specifically Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support
Vector Machine (SVM), and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under
Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and
an f1-score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% accuracy, 87%
precision, 82% recall, 84% f1-score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and non-user

Nursela Salsabilla Basuni, Amril Mutoi Siregar

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

December 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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