DEVELOPMENT OF AN INTEGRATED MONITORING SYSTEM BASED ON FACE RECOGNITION AND IOT TECHNOLOGY FOR KPI ASSESSMENT IN THE MANUFACTURING INDUSTRY

• Main Author: Ricky
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/87284
• Institution: Institut Teknologi Bandung

The manufacturing industry plays a critical role in supporting the global economy. However, in addressing the challenges of the competitive digitalization era, companies require effective approaches to maintain operational efficiency. This study aims to develop an integrated monitoring system based on face recognition and Internet of Things (IoT) technologies to evaluate Key Performance Indicators (KPIs), particularly in assessing worker efficiency. The system is designed to record worker activities in real-time and monitor productive activities on machines automatically, offering higher accuracy and efficiency compared to manual methods. Using this monitoring data, evaluations can be conducted with precise information, enabling companies to identify areas for improvement and boost productivity, thus enhancing global competitiveness. The worker monitoring system utilizes face recognition technology with an accuracy of 99.63% and a sensitivity range of 93.11 ms to 472.35 ms. For machine monitoring, the implemented sensors demonstrated an average recording time of 28.45 ms. The system is integrated with a KPI evaluation system on a local network, featuring an interactive dashboard. It achieved an average data transmission and reading time of 113.58 ms, ensuring ease of use and high responsiveness in the evaluation process. The system's implementation on the production floor successfully monitored and evaluated worker performance in alignment with operational schedules. The result indicated that the monitoring system can accurately detect worker activities free from manipulation and integrate them with work schedules to assess compliance levels. Consequently, the system significantly contributes to supporting the implementation of Industry 4.0 concepts in the manufacturing sector.