Comparison Of Arrival Classification Of Outpatient Patients Based On Appointment Using Adaboost And Random Undersampling Methods - Widodo; Soeleman, Arief; Alzami, Farrikh; Muslich, Muslich; Krisnawati, Dyah Ika (Universitas Ibn Khladun Bogor, 2023)
Patients who choose to carry out examinations and treatment with an outpatient model without staying at hospitals and health service clinics are increasing for various reasons and the busyness of the patient in question. Clinics and hospitals are still able to survive and operate because they are still needed by patients who require both outpatient and inpatient services. Many clinics and hospitals in various countries still have not implemented an outpatient queue data processing system with an adequate system so there are many patients who have registered to be examined but do not come for various reasons which is a loss for the nurses and doctors on duty at the hospital. that day. This incident is certainly detrimental to clinics and hospitals because data processing is still manual, so it is impossible to predict how many patients will visit the clinic for check-ups. One solution that is still wide open for managing visiting patient data both for outpatient and inpatient treatment is to use big data. The method to be used in data mining is a Decision Tree classification with Adaboost and Random Undersampling. With the Decision Tree classification with Adaboost and Random Undersampling, good predictions will be produced so that they can help in making a decision.