The Process of Grouping Elementary School Students Receiving PIP Assistance uses the K-Means Algorithm

Abstract

As part of receiving support from the Smart Indonesia Program (PIP), this study intends to analyze and apply the K-Means algorithm in the process of grouping elementary school students. PIP is a government initiative that attempts to give money to elementary school pupils from disadvantaged or weaker homes. The effective and fair distribution of aid monies depends on the proper grouping of the students. The K-Means approach was selected because it can cluster data, allowing the grouping of pupils based on pertinent traits. Numerous characteristics that can affect kids' financial needs are included in the data utilized in this study, including family income, parental education level, proximity to the school, and other social and economic issues. This study makes use of empirical data from a PIP-affiliated elementary school in an urban setting. The data includes a large number of pertinent features and thousands of pupils. Based on how similar their characteristics are, pupils are divided into numerous clusters using the K-Means technique. The findings of this study will help us better identify the traits of students who are eligible for PIP support. By doing this, the government can allocate funds more wisely and guarantee that aid is given where it is most needed. The PIP program can benefit children in need more by streamlining the process of grouping the students. In addition, this research has broader implications for social aid and education policy. To guarantee effectiveness and equity in resource allocation, the K-Means algorithm can be used in a variety of additional aid initiatives. Data mining-based strategies, like those employed in this study, are becoming more crucial to boost the effectiveness of aid programs like PIP. The findings of this study can help the government and educational institutions improve the efficacy of aid initiatives designed to boost Indonesian children's education