Analytics-Based on Classification and Clustering Methods for Local Community Empowerment in Indonesia

Abstract

West Papua is reportedly the second-most populous province in Indonesia. The United Nations International Children’s Emergency Fund (UNICEF) highlights Papua’s performance in selecting the Sustainable Development Goals (SDG) indicators compared to other provinces in the country. The data shows that food, nutrition, health, education, housing, water, sanitation, and protection are defined as multidimensional child poverty. Population statistics and poverty figures show that inter-provincial equity in Indonesia needs to be re-measured. In 2008, the Regional Governments of Papua and West Papua Provinces implemented a Community Empowerment Program called “PNPM RESPEK”, which provided direct community assistance for IDR 100 million per village. To determine the people’s level of understanding and perception towards this program, PNPM RESPEK, in collaboration with the Central Statistics Agency, conducted an integrated PNPM RESPEK Evaluation Survey in July 2009. Based on the survey results, this paper identifies a model (pattern) of understanding the people of Papua and West Papua towards the program and finds the best method to build this model through classification techniques. Then the data model was also tested using unsupervised learning, the clustering method. The experimental results show that the J48 decision tree produces the highest accuracy compared to the others. As for clustering, the clustering hierarchy provides the best accuracy. Decision Tree J48 has the best accuracy with an accuracy of 97.31%. In this case, 97.31% of the people of Papua and West Papua who receive direct community assistance meet the level of understanding and perception of the PNPM RESPEK Program.