ANALISIS SUPPORT VECTOR REGRESSION (SVR) DENGAN KERNEL RADIAL BASIS FUNCTION (RBF) UNTUK MEMPREDIKSI LAJU INFLASI DI INDONESIA

Isnaeni R; Sudarmin Sudarmin; Zulkifli Rais

doi:10.35580/variansiunm13

Isnaeni R Department of Statistics, Universitas Negeri Makassar
Sudarmin Sudarmin Department of Statistics, Universitas Negeri Makassar
Zulkifli Rais Universitas Negeri Makassar

DOI: https://doi.org/10.35580/variansiunm13

Keywords: Backpropagation Neural Network; People's Welfare Indicators; Classification

Abstract

Inflation is one indicator that affects the economic growth of a country. As a developing country, Indonesia has an unstable inflation rate every year. Therefore, it is necessary to predict the inflation rate in the future to be useful for formulating future economic policies. SVR is a Support Vector Machine (SVM) development for regression cases. In the SVR method, the RBF kernel is used as an aid in solving non-linear problems, the Min-Max Normalization method for data normalization, distribution of training data and testing data, selecting the best model with Grid Search Optimization, then forecasting using the model obtained with parameter = 0,1, C = 1, and = 3. The forecasting results obtained were evaluated by looking at the RMSE value, the test value obtained was RMSE of 0.0020, which means the model's ability to follow the data pattern well

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