| dc.description.abstract | Since the emerging of big data era, the information and data are
grown rapidly. It requires us to have ability to extract the knowledge and
information that consisted in this explosion of the data. One of way that
can be used for this purpose is by using machine learning method. One of
purpose of machine learning implementation is to conduct classification
analysis and to identify variable importance that contribute in the research.
It’s conducted the comparative study between two machine learning
classification methods named classification tree and random forest method.
This study is implemented on Indonesian Socioeconomic Survey
(SUSENAS) 2020 in Aceh Province. The purpose of the study is to
identify the optimum method between both and to identify the
characteristics of food insecure household. The optimum method obtained
by comparing the AUC value. The results obtained is random forest
outperformed classification tree with the AUC value of random forest
method is 0,718 and classification tree method is 0,668. The rank of
variable importance of the optimum method is the type of cooking fuel
used in the household, the area of house floor, education level of head of
household, number of savers in a household, and the type of house floor. | en_US |
