Poverty Classification and Clusterization in Sabu Raijua District, East Nusa Tenggara Province Using Machine Learning
Keywords:
society, poverty, data clusterization, data classification, machine learningAbstract
The scope of this paper is focused on the socio-economy poverty problem in rural area. Household expenditure and income surveys provide data that are used for identifying and measuring the poverty status of households. The contribution of this work is the machine learning approach to assess and monitor the poverty status of households in the rural area of Indonesia. This approach takes into account all the household expenditure and income surveys that took place. This approach is accurate, inexpensive, and makes poverty identification cheaper and much closer to real-time. Data preprocessing and handling imbalanced data are major parts of this work. machine learning classification and clustering models are applied. The final machine learning classification and clustering model could transform efforts to track and target poverty. This work demonstrates how powerful and versatile machine learning can be, and hence, it promotes for adoption across many domains in both the private sector and government.
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