Browsing by Subject "Association Rule Mining"

This study focused on detecting horizontal and vertical collusion within Indonesian government procurement processes, leveraging data-driven techniques and statistical methods. Regarding horizontal collusion, we applied clustering techniques to categorize companies based on their supply patterns, revealing clusters with similar bidding practices that may indicate potential collusion. Additionally, we identified patterns where specific supplier groups consistently won procurements, raising questions about potential competitive advantages or strategic practices that need further examination for collusion. For vertical collusion, we examined the frequency of associations between specific government employees and winning companies. While high-frequency collaborations were observed, it is essential to interpret these results with caution as they do not definitively indicate collusion, and legitimate factors might justify such associations. Despite revealing important patterns, the study acknowledges its limitations, including the representativeness of the dataset and the reliance on quantitative methods. Nevertheless, our findings carry substantial implications for enhancing procurement monitoring, strengthening anti-collusion regulations, and promoting transparency in Indonesian government procurement processes. Future research could enrich these findings by incorporating qualitative methods, exploring additional indicators of collusion, and leveraging machine learning techniques to detect collusion.