Background

According to Nurwati N (2008), poverty is a problem that has always been faced by humans. The problem of poverty is as old as human civilization itself, and its implications can involve various aspects of human life. In other words, poverty is a social problem that is global in nature, meaning that poverty has become a concern of the world, and the problem exists in all countries, even though the impact of poverty varies. However, sometimes poverty is not recognized as a problem by the people who are experiencing it. For those who are classified as poor, poverty is a real part of their daily lives because they experience living in poverty. Nevertheless, they may not necessarily be aware of the poverty they are experiencing.

Factors that cause poverty include: low level of education, low degree of health, limited job opportunities, and conditions of isolation (Kartasasmita G 1996). In a report issued by the World Bank, it is known that there are five factors that are considered to influence the occurrence of poverty, namely: education, type of work, gender, access to basic health services, and infrastructure and geographic location.

Therefore, a multivariate analysis using biplot method is needed to identify the characteristics of provinces in Indonesia based on population factors and poverty indicators in Indonesia. Multivariate analysis using biplot can provide ease of understanding through more attractive, informative, communicative, and artistic graphical presentations. Based on the multivariate analysis using biplot, the relationship between population factors and poverty indicators in Indonesia can be identified.

Objectives

The purpose of the research on Multivariate Biplot Analysis on Village Potential Data in Indonesia in 2010 is to achieve the following objectives: (1) Identify the relationship between population variables in Indonesia, such as the population size in each province, poverty line in each province, percentage of the population engaged in farming in each province, the average value of the P1 index, and the average value of the P2 index; (2) Identify the relative position among provinces in Indonesia to observe the similarity among provinces in Indonesia; (3) Obtain the characteristics of each province based on population variables and poverty indicators in Indonesia; and (4) Identify provinces with the lowest poverty rate based on population variables and poverty indicators used in the research.

Benefits

The benefits of conducting research on Multivariate Biplot Analysis of Village Potential Data in Indonesia in 2018 are as follows: (1) Identifying the variables that have a high correlation with the poverty rate in Indonesia; (2) Understanding the characteristics of provinces in Indonesia based on demographic variables and poverty indicators, which can serve as a consideration in formulating public policies; and (3) Evaluating the performance of local governments in developing their regions to alleviate poverty in Indonesia.

Scopes

The scope of the research on Multivariate Biplot Analysis of Village Potential Data in Indonesia in 2010 is as follows: (1) The data used is a combination of secondary data from the poverty data in Indonesia in 2018 and village potential data in 2018 obtained from the official website of the Central Statistics Agency (BPS) of Indonesia. The research aims to analyze the relationship between demographic variables in Indonesia, such as the population of each province, poverty lines in each province, the percentage of farmers in each province, the average P1 index, and the average P2 index, with the poverty indicator as the main focus of analysis. By using multivariate biplot analysis, the study aims to identify the characteristics of each province based on demographic variables and poverty indicators in Indonesia and to evaluate the performance of local governments in poverty reduction efforts.

Kindly proceed to my Github repository to access the relevant materials and information related to this project

Sources

Nurwati N