Sebuah Meta-Analisis: Metode Support Vector Machine dan Modifikasinya

Sry Wahyuni, Syaharuddin Syaharuddin, Malik Ibrahim, Habib Ratu Perwira Negara

Abstract


SVM was introduced by Vapnik in 1992, is a machine learning method that works on the basis of Structural Risk Minimization (SRM) with the aim of finding the best hyperplane that separates two classes in the input space. The basic principle of SVM is a linear classifier, which was further developed by including kernel functions (kernel trick) to be able to work on non-linear problems because in general, real-world problems rarely have linear separable properties. The choice of kernel function is a factor that determines the level of accuracy in the introduction of leaf types. In this study, predictions of the level of accuracy will be carried out using the Support Vector Machine (SVM) method. Data were collected from 47 publications from database indexers such as Google Scolar, ScienceDirect, DOAJ, PubMed, WorldCat, Dimensions, Portal Garuda and Mendeley from 2012-2022. The filtered data is the result of research containing correlation test (r), and the amount of data (N) consisting of training data and test data, MSE, MAD, RMSE, MAPE then analyzed using meta-analysis through the value of effect size (ES) and standard error (SE) to see the summary effect size. The simulation results using the JASP software show an accuracy rate of 65% which can be used as a basis for determining the next policy.

Keywords


Meta-Analysis, Support Vector Machine, Modification.

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References


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