Klasifikasi Kebutuhan Dokter untuk Kesejahteraan Masyarakat Menggunakan ANFIS

Abstract

In social life, the need for medical workers is different in each region, because the population varies. If doctors cannot treat a large enough number of patients in an area, it can have various impacts on society, such as contracting dangerous diseases if not treated as soon as possible. The effect will be a decline in people's standard of living. By classifying the need for the number of health workers (doctors) relative to the population, the level of welfare in an area can be obtained. To assist in optimizing health workers (doctors) they can use fuzzy logic and ANFIS (Adaptive Neuro Fuzzy Inference System). By using ANFIS, it is hoped that we can find the optimal value for the classification of health workers that will be needed in each region. In the ANFIS test, the RMSE error value was 0.2698 and the first accuracy value was 73%. Then by adding a membership function, an RMSE error value of 0.17698 was obtained, this second accuracy value increased by 10% to 83%. By using the ANFIS method to classify health workers according to different population sizes in each region, you can measure the level of community welfare well.