Application of fuzzy genetic system to predict the number of outpatient visits


  • Sarif Surorejo STMIK YMI Tegal, Indonesia
  • Septian Dwi Cahyo STMIK YMI Tegal, Indonesia
  • Nurul Fadilah STMIK YMI Tegal, Indonesia
  • Gunawan Gunawan STMIK YMI Tegal, Indonesia



Fuzzy System, Genetic Algorithm, Outpatient, Patient Visits, Predictions


Improving the management and use of resources in outpatient care is a challenge faced by health facilities in today's digital era. The inability to accurately predict patient flow can result in inadequacies in staff scheduling and effective space management. Therefore, this study aims to develop a predictive model of outpatient visits using the fuzzy system genetic method. The research methods used include the design of a combination of experimental methods, quantitative analysis, and model validation. Outpatient visit data is taken from a hospital and processed using the Fuzzy Genetics System which optimizes fuzzy rules with genetic algorithms. The results of the model implementation show accurate and adaptive predictions to variations and uncertainties in patient visiting patterns. Based on the results of the study, it can be concluded that the use of fuzzy system genetic methods in predicting outpatient visits can improve the operational efficiency of health facilities. The developed prediction model is able to provide predictions that are more accurate, adaptive, and responsive to the real needs of health facilities. With the adoption of this method, health facilities can optimize management and resources in outpatient health services. This research contributes significantly to the development of predictive models that are more efficient and applicable in the dynamic context of healthcare.


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How to Cite

Surorejo, S., Cahyo, S. D., Fadilah, N., & Gunawan, G. (2024). Application of fuzzy genetic system to predict the number of outpatient visits. Jurnal Mandiri IT, 13(1), 47–55.

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