Model Monitoring IoT Menggunakan Logika Fuzzy Untuk Prediksi Tingkat Kelelahan Mesin Real-Time - Muhammad Furqon Siregar; Siti Sarah Harahap (Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA), 2026)
Modern industrial technology development demands high efficiency levels and zero downtime for production machinery. The phenomenon of machine fatigue, which accumulates due to excessive workload without early detection, frequently triggers sudden catastrophic failures. This study aims to propose an intelligent monitoring model based on Internet of Things (IoT) technology integrated with fuzzy logic to detect machine fatigue in real-time. A DS18B20 temperature sensor and an MPU6050 accelerometer vibration sensor are implemented on the physical layer to perform actual data acquisition aligned with ISO 10816-3 standard. These physical parameters are then transmitted to a cloud server via the MQTT protocol, where the Mamdani fuzzy logic method processes the inputs to generate the machine's status. The research results show that this model successfully detects and classifies machine fatigue into Safe, Warning, and Danger statuses with 100% prediction accuracy matching MATLAB simulations, and demonstrates an average transmission latency of 0.86 seconds (well below 1.2 seconds). This system is proven reliable to be integrated as a key decision part of preventive maintenance strategy in modern manufacturing.