Risiko Kolaborasi R&D Dengan Pendekatan Algoritma C4.5 Dan Random Forest

Abstract

This study investigates the application of the C4.5 and Random Forest algorithms in managing risks within the context of R&D collaboration. In light of the growing complexity of the global landscape, characterized by diverse cultures, objectives, and knowledge-sharing challenges, effective risk management in R&D collaboration is of paramount importance. A quantitative approach, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, is adopted to structure the data collection and analysis process. A dedicated questionnaire was employed to collect data, identifying R&D collaboration risks and factors influencing these collaborations. Additionally, this study evaluates the efficacy of the C4.5 and Random Forest algorithms in addressing these risks. The collected data is subjected to statistical analysis, including both descriptive and classification analyses. This quantitative approach aims to provide objective and quantifiable insights into the practical application of the C4.5 and Random Forest algorithms for risk management in R&D collaboration. The results of the research demonstrate that both the C4.5 and Random Forest algorithms exhibit strong potential in effectively mitigating risks associated with R&D collaboration. These findings are expected to offer practical guidance to organizations engaged in R&D collaboration, equipping them with enhanced risk management strategies and more effective approaches to address the challenges that arise.