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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Reza Pahlevi
Universitas Budi Luhur
Computer Science & IT Engineering

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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 Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8063

Abstract

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.
Peningkatan Akurasi Deteksi DDoS SDN Menggunakan Algoritma Random Forest Berbasis Gain Ratio Reza Pahlevi; Latifah Nur Zam Zam; Imelda Imelda
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i03.1288

Abstract

The development of Software-Defined Networking technology offers high flexibility in network management but also introduces security vulnerabilities to Distributed Denial of Service attacks. These attacks can paralyze the SDN controller through packet flooding, leading to a drastic decline in network performance. This research aims to enhance the accuracy of DDoS attack detection in an SDN architecture based on the Ryu Controller with the OpenFlow v1.3 protocol by implementing the Random Forest algorithm optimized with Gain Ratio feature selection. The novelty of this study lies in the use of a primary dataset collected through a self-developed SDN testbed using Mininet with a Modified Tree topology to generate more realistic attack scenarios. The Gain Ratio method is employed to reduce data dimensionality by selecting the most relevant features from OpenFlow network traffic, thereby accelerating processing time and minimizing detection error rates. The experimental results demonstrate that the combination of Random Forest and Gain Ratio achieved a detection accuracy of 99.84% in a controlled testbed environment. This study indicates that the integration of Random Forest and Gain Ratio provides higher accuracy compared to the standard algorithm. The use of Gain Ratio feature selection is proven to optimize both the accuracy and efficiency of the Random Forest algorithm in DDoS detection
Co-Authors Hidayat Ramadhani Imelda Imelda Intan Oka Herdanis Jan Everhard Riworuhi Latifah Nur Zam Zam Sunu Ilham Pradika