Model for Developing Technology-Based Extreme Poverty Data Collection in Semarang City

Abstract

This study aims to examine the technology-based extreme poverty data collection model to improve the accuracy and effectiveness of the Extreme Poverty Eradication Acceleration Program (P3KE) in Semarang City. Along with the complexity and dynamics of the socio-economic development, accurate and integrated extreme poverty data collection is key to determining targeted policies in Semarang City. However, challenges such as data inaccuracy, information fragmentation between regional apparatuses in Semarang City and other institutions, limited technological infrastructure, and low digital literacy among the community are still significant obstacles. This study uses a qualitative approach with case study methods and in-depth interviews to explore the effectiveness of using technology in extreme poverty data collection. The results of the study show that the integration and synchronization of data systems between regional apparatuses in Semarang City, the use of Big Data technology, and mobile applications for field data collection can significantly improve the quality and speed of data collection. In addition, regular data updates, community participation in the data collection process, and the application of a multidimensional approach to poverty are important factors that support the increase in the accuracy and effectiveness of the P3KE program. This study recommends the development of an integrated and synchronized database system in real time, optimization of the use of digital technology, and increasing human resource capacity through digital literacy training. In addition, collaboration with the private sector and non-governmental institutions is also identified as a strategy that can accelerate the eradication of extreme poverty. With the implementation of policies based on technology and innovation, it is hoped that the P3KE Program can run more effectively in reducing the level of extreme poverty in Semarang City.