Smooth Support Vector Machine (SSVM) for classification of Human Development Index

Abstract

Human Development Index (HDI) is measuring achievements of human development based on basic components of quality of life. Human development index is low score if HDI is less than 60, moderate HDI between 60 to less than 70, high HDI between 70 to less than 80, and equal to 80 and more than 80 belong to high HDI. Smooth Support Vector Machine (SSVM) is a classification technique that is new. The algorithm used is Newton Armijo with linear kernel, polynomial kernel, and Radial Basis Function (RBF) kernel. The result of classification of human development index with SSVM method with linear kernel shows the prediction accuracy 84.77%, polynomial kernel 61.65%, and RBF kernel 100%. Radial Base Function Kernel (RBF) is the most accurate kernel in predicting human development index.

A Spatial Error Model in Structural Equation for the Human Development Index Modeling

“…The latent variables must be built by indicators based on strong theoretical concepts. In consequence, in this study, each latent variable comprised multiple indicators drawn from previous studies, namely Darsyah et al (2018), Niranjan (2020), Pramesti & Indrasetianingsih (2018), Rahma (2020), and Wati & Khikmah (2020). The Long and Healthy Life (LHL) is constructed by four indicators.…”