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

doi:10.59934/jaiea.v6i1.2813

Authors

Regitaa Ramadhanty STMIK Kaputama
Magdalena Simanjuntak STMIK Kaputama
Suci Ramadani STMIK Kaputama

DOI:

https://doi.org/10.59934/jaiea.v6i1.2813

Keywords:

Apriori, Data mining, Rehabilitation Success Factors

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.

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