Open AccessComparison Four Kernels of SVR to Predict Consumer Price Index
Author(s) -
Mimin Fatchiyatur Rohmah,
I Ketut Gede Darma Putra,
Rukmi Sari Hartati,
Luki Ardiantoro
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1737/1/012018
Subject(s) - overfitting , consumer price index (south africa) , mean absolute percentage error , econometrics , support vector machine , price index , statistics , mathematics , gaussian function , gaussian , computer science , economics , mean squared error , artificial neural network , artificial intelligence , monetary policy , physics , quantum mechanics , monetary economics
The economy of a region is affected by the stability of food supplies. If the market price of the food supply is stable, the purchasing power level will increase. The price stability of food supplies can be anticipated by using the Support Vector Regression Method, to predict the Consumer Price Index, known as CPI. In the Consumer Price Index assessment, using data based on recording, measurement and calculation of the goods and services average price which consumed by households in a certain period of time. Goods and services that are deemed to represent household expenses are then averaged. The CPI in this study is a type of food supply issued by the Indonesian Central Statistics, and the input variable is taken from the prices of staple commodities in the city of Surabaya, Malang and Kediri based on data from the Siskaperbapo website. To get the supported vector data, the hyperplane maximized by the SVR concept. This concept is able to overcome the overfitting, in order to obtain more accurate prediction results. In predicting the Consumer Price Index, reference data is divided as training data 2016-2019 and testing data 2017-2020. All four kernels were used in the test, namely Spline kernel, Gaussian-RBF kernel, Linear kernel and Polynomial kernel. All four kernels are compared to see their MAPE, this can be shown by the Mean Absolute Percentage Error (MAPE) of less than three, if by using Gaussian RBF kernel. The smallest MAPE value showed by Malang CPI value, which is 1.8242 with C = 50, followed by Kediri with the MAPE value of 2.251 with C = 50 and MAPE value of Surabaya which is 2.5279 with C = 50.