AI-Based IPTV Monitoring System for Optimizing Hospitality Entertainment Services: A Case Study of Radisson Blu Uluwatu Bali - Sidin Rahman; Luh Gede Astuti; Cokorda Pramartha; I Ketut Gede Suhartana; Anak Agung Ngurah Istri Eka Karyawati; I Made Widiartha (Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 2026)
This study proposes an intelligent IPTV monitoring system aimed at enhancing service reliability and operational performance in hospitality environments. The system integrates multicast IPTV monitoring and Chromecast tracking with the Random Forest algorithm to identify, classify, and handle service disruptions automatically. Developed using the Design Science Research methodology, the system was evaluated through Quality of Service (QoS) and Mean Opinion Score (MOS) measurements. The findings show that the proposed model achieves an accuracy rate of 94.37% while improving several network performance indicators, including lower delay, reduced packet loss, and higher throughput. In addition, the implementation of real-time notifications helps accelerate response handling and supports more efficient operational processes. By combining AI-based monitoring with multicast and Chromecast technologies, the proposed approach provides a practical and scalable solution for improving digital entertainment services in real hospitality operations.