As a Computer Science student specializing in Artificial Intelligence at BINUS University, I developed the "Corruption Early Prevention: Decision Support System" targeting corruption in Indonesia. This paper, reflecting my academic pursuit, showcases the integration of AI to foster a corruption-free society. Through collaboration and technological innovation, we aim to provide a novel solution for early corruption detection and prevention, demonstrating the practical applications of AI in societal issues.
This study introduces the Corruption Early Prevention (CEP) Decision Support System, designed for the President of Indonesia, to combat corruption through an early warning system engaging government, industry, and society. The objectives are to assess the construct and system design of CEP within the Indonesian context for corruption prevention. Employing Neuro-Research, a blend of qualitative and quantitative methods alongside IT model development, the study validates the theoretical and practical feasibility of CEP for real-time corruption diagnosis.
The methodology, Neuro-Research, combines qualitative (exploratory) and quantitative (explanatory and confirmatory) research phases. Initial exploratory research defined the CEP's theoretical construct, followed by validation through Focus Group Discussions (FGD) using the Delphi technique, and quantitative research to assess the construct's validity and reliability. This multi-stage approach ensured a comprehensive examination of CEP's potential impact on corruption prevention in Indonesia.
Results & Analysis
Findings confirm CEP's theoretical construct is contextually valid, reliable, and feasible for implementation in Indonesia, promising an IT-based, real-time diagnostic system for early corruption prevention. The study emphasizes CEP's innovative design, incorporating public participation and advanced data analysis techniques, to effectively monitor and detect corruption, thereby assisting the government in fostering a transparent, corruption-free society.