PENGEMBANGAN SISTEM AUTOMATIC WORKLOAD THROTTLING BERBASIS PYTHON UNTUK MITIGASI THERMAL THROTTLING CPU PADA PERANGKAT KOMPUTER - Intan Oka Herdanis; Reza Pahlevi; Sunu Ilham Pradika; Hidayat Ramadhani; Jan Everhard Riwurohi (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
The increasing demand for modern computing drives processors to operate under heavy loads; however, this condition potentially elevates CPU operating temperatures and triggers thermal throttling. This study aims to develop the Python-based Yield Thermal Intelligent Handling & Automation (PYTHIA) system as a software-based CPU workload management mechanism to adaptively mitigate thermal throttling. The system utilizes real-time temperature monitoring from the Libre Hardware Monitor (LHM) Web Server and implements a duty-cycle worker control logic to adjust CPU workload based on predefined temperature thresholds. Testing was conducted on two processors with distinct characteristics, the AMD Ryzen 7 7730U and the Intel Core i7-10750H, through three experimental phases: pre-throttling, cooldown, and automatic throttling. The results indicate that PYTHIA successfully monitors processor temperature in real-time and reduces workload as temperatures rise. On the AMD Ryzen 7 7730U, the system effectively maintained stability following the cooldown and automatic throttling phases. Meanwhile, on the Intel Core i7-10750H, the system responded to temperature increases, although temperature fluctuations remained significant, occasionally approaching the 90–95°C range. It should be noted that the high temperature fluctuations on the Intel  Core i7-10750H processor indicate that the system still requires refinement in its adaptive control mechanism to achieve optimal thermal stability. Overall, PYTHIA is proven to assist in reducing thermal throttling risks through adaptive workload control, though the control mechanism requires further optimization for smoother duty-cycle transitions and improved temperature stability.