Comparison of Methods for Applying the Data Mining Clustering Concept to Banten Provincial Government Poverty Data: Systematic Review - Yuningsih, Irma; Dwi Aryani, Mina Winawati; Imelda, Imelda (Jurnal Mamangan, 2024)
An in-depth understanding of the phenomenon of poverty in Banten Province is the key to formulating effective strategies in overcoming this problem. In general, poverty in this area is caused by several interrelated and complex factors. This research provides a comparative analysis of the application of clustering data mining methods in calculating population poverty data in certain regions or provinces, especially Banten. This research aims to assess the consistency and effectiveness of various clustering methods used in previous research. Using a qualitative approach with a literature review, secondary data from relevant research was collected through systematic searching, reading, and note-taking. The data collected is then carried out through a process of data collection, filtering, presentation and drawing conclusions. The results show that most of the literature reviews show similar results regarding the effectiveness of various clustering methods in analyzing poverty data in Banten. This shows that there is a consensus among previous studies regarding the efficacy of clustering data mining techniques in overcoming problems related to poverty in the region. These findings contribute to a deeper understanding of the methodology used in poverty analysis and provide insights for policy makers and researchers to develop more effective strategies in poverty alleviation efforts. The conclusion of this research is that clustering data mining in analyzing poverty data in Banten Province has shown quite high consistency and effectiveness. Various literature studies that have been reviewed show that various clustering methods that have been applied previously provide similar results in identifying poverty patterns, understanding socio-economic structures, and providing valuable insights for developing more targeted policies.