CART BAGGING FOR CLASSIFICATION OF CHILD LABOR’S CASE IN CENTRAL SULAWESI PROVINCE

Abstrak / Ringkasan

Classification is statistical method that used to classify the systematical data. In statistics, there are several classification methods, one of the methods is CART (Classification and Regression Trees) that resulted classification trees model. CART has several advantages related with the model and the classification result, although it has a weakness on stability model that resulted. To solve this weakness, bagging (bootstrap aggregating) technique was applied on CART method to increase the stability and classification accuracy, that called CART bagging method. In this research, CART bagging is using to classify child labor’s case in Central Sulawesi province. Beside that, this research also aims to determine the influence factors of child labor’s case and the most dominant factor. The result shows that child get involved in work activity influenced by child’s school participation, child’s age, child’s sex, number of household member, income per capita and head of household’s education level, with child’s school participation become the most dominant factor of child labor’s case in Central Sulawesi Province. Classification accuracy used to see how good the classification model. Bagging technique on CART resulted higher classification accuracy.
Keywords: classification, CART bagging, child labor, classification accuracy.

Topik Terkait

Sitasi