Pendukung Keputusan Rawat Inap Dengan Menggunakan Algoritma Naive Bayes - Ridi Satrio Adi (Jurnal ICT : Information Communication & Technology, 2019)
Actual absorption Budget Work Plan is often inappropriate and beyond the ones in the previous plan, the suspect that this occurs because the current formulation does not use methods that can provide assistance in the calculations, so that the calculation of the original approach Realization of the health budget and their families and retired personnel and their families often do not match and exceed what was planned earlier, in guessing that this happens because when the preparation is not using methods that can provide assistance in the calculations, so that the original calculation approach using only the data absorption history of the current year (year N) and the prediction of possible rate in the next year (N + 1) can not fully count on, this can occur in the absence of predictions that allow participants will lead to greater use of health care costs or commonly called cost drivers, and in fact the use of tables morbidity and mortality table is still not able to fully answer to the problem, but just so the constant factor on each participant by age and gender specific.By taking data from database transactions in clinic visits for treatment, which is done by creating an Entity Relationship (ER) and using the menu view in its database software download data into excel the data.Excel data downloaded yet to be fully informed and do not contain meaning and appropriate information is correct so that further processing should be done by using Data Mining and Naive Bayes algorithm, which will determine the participants who could potentially be a cost driver based on the pattern of illness in the next year.In experiments using Naive Bayes algorithm showed that of the 65,535 processed data are 3,915 people who could potentially be a cost driver that is likely to in the Inpatient Hospital, the results obtained with the accuracy of 93.63% and the AUC curve 0.975.