Human Capital Decision Intelligence (HCDI) architecture in microbiology laboratory based on machine learning and operations research models - Trihandaru, Suryasatriya; Susetyo, Yosia Adi; Parhusip, Hanna Arini; Susanto, Bambang (International Journal of Advances in Intelligent Informatics, 2025)
The Human Capital Decision Intelligence (HDCI) system integrates human-computer interaction in a microbiology laboratory that uses machine learning and operational research to classify new tasks and then recommend assignments to each person. The models evaluated in building this system are Support Vector Machine, Gaussian Naive Bayes, Multinomial Logistic Regression, and Artificial Neural Network. The results of the research show that the ANN model is the most consistent and reliable across various training ratios, as indicated by the model's goodness parameters. The selected ANN model is combined with a linear programming approach to optimize workload distribution. The integrated system successfully manages new job scenarios and recommends staff based on competencies and availability. It also ensures assignments do not exceed maximum workload limits and finds alternatives when key staff are unavailable. The implementation of the HDCI system has a positive impact on various factors, including the fair distribution of tasks, enhanced staff performance monitoring, and significantly improved operational efficiency and human resource management in the microbiology laboratory. The system is designed to be easy to use and support collaboration between laboratory staff and computational models. The system is not only advanced in supporting personnel management decision-making, but it can also demonstrate how artificial intelligence and operations research systems can be combined to address the needs of the microbiology laboratory environment.