Artificial Data Management in Reaching Conditional Cash Transfer of Program Keluarga Harapan (PKH) Utilizing Simple Addictive Weighting

Abstract

Abstract The main challenge in implementing a poverty reduction program is to identify precisely the targets that will benefit a program. Program Keluarga Harapan (PKH) is a social protection program from the central government through the provision of conditional cash transfers to Low-Income Households (RTSM)/Low-Income Families (CBOs), which are designated as Program Keluarga Harapan beneficiary families. Program Keluarga Harapan is the conditional cash transfer (CCT) program to reduce poverty by making welfare programs dependent upon the receivers’ actions. The accuracy of choosing targets directly impacts the achievement of social protection programs’ objectives in poverty alleviation. This research would like to dig up how Jogjakarta’s government is accurate to reach the beneficial Program Keluarga Harapan recipient. In terms of Jogjakarta’s government using the support system to classify the low-income family as the receiver of conditional cash transfers. Therefore, this research will explain which decision-making model used by the government of Jogjakarta managed by a computerized system, known that computerized data processing is expected to present information rapidly, precise, clear, and directed as the exact target for the program.