Abstract

Human Development Index (HDI) is an index describing the quality of human development in a region as a whole, which is a logical consequence of the development activities in the area. Building a predictive model of HDI level is needed to determine what factors are dominant and the subject of focused development and improvement in the area in order to improve the HDI. This study applies Dynamic Bayesian Networks (DBN) to predict and model the fifteen variables that affect the HDI level. Structural Dynamic Bayesian Networks built with software CaMML then evaluated by measuring the level of accuracy. Scenario experiments in this study were divided into four scenarios, the differences in the proportion of training data, test data and levels of HDI category. Experiments using 75% and 25% training data test data to the prediction of three-level category HDI produces the best accuracy rate, which amounted to 88.461%. Dynamic Bayesian Networks graph structure may indicate a relationship between variables. Dynamic Bayesian Networks graph structure that is built can be used as a predictive model which could provide recommendations what key factors need to be considered to improve the level of HDI category in the districts/cities in West Java province, namely educational factors, demographic factors, and health factors

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