Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Regitaa Ramadhanty
STMIK Kaputama
Automotive Engineering Computer Science & IT Control & Systems Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Application of the Apriori Algorithm for Discovering Patterns of Success Factors in Drug Rehabilitation: A Case Study at MAB Rehabilitation Center Regitaa Ramadhanty; Magdalena Simanjuntak; Suci Ramadani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 6 No. 1 (2026): October 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v6i1.2813

Abstract

Drug abuse is a serious issue that requires effective rehabilitation programs. MAB Rehabilitation Center possesses rehabilitation data that have mainly been used for administrative purposes and have not been analyzed to identify patterns associated with rehabilitation success. This study aims to identify relationship patterns among factors influencing rehabilitation outcomes using the Apriori Algorithm, determine the main factors forming association rules based on support, confidence, and lift ratio, and provide recommendations for rehabilitation strategies. The dataset consists of client rehabilitation records from 2020 to 2025, including variables of drug type, frequency of drug use, dependency level, therapy attendance, family support, and rehabilitation success status. Data were processed using Google Colab with a minimum support threshold of 15% and a minimum confidence threshold of 60%. The analysis generated several association rules related to rehabilitation success. The strongest rule was Moderate Dependency Level → Successful Rehabilitation, with a support value of 22.38%, a confidence value of 63.70%, and a lift ratio of 1.05, indicating the strongest association among the identified patterns. These findings demonstrate that the Apriori Algorithm is effective in discovering meaningful association patterns and can support data-driven decision-making, client assessment, and the development of more targeted rehabilitation strategies at MAB Rehabilitation Center.
Co-Authors Ramadani, Suci Simanjuntak, Magdalena