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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Sunu Ilham Pradika
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.
Optimasi Klasifikasi Multikelas Jenis Serangan Jaringan IOT Sunu Ilham Pradika; Hidayat Ramadhani; 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.v7i03.1342

Abstract

The development of the Internet of Things has encouraged the use of connected devices across various sectors, including healthcare, energy, transportation, and household environments. However, the heterogeneous and dynamic nature of IoT networks, along with their reliance on wireless communication, makes them vulnerable to various cyber threats. This study aims to develop and evaluate an intrusion detection model for multiclass classification in IoT networks using the ToN_IoT dataset. The models used in this study are Random Forest and XGBoost, tested under several scenarios, including baseline models, models with hyperparameter tuning, and the application of SMOTENC to XGBoost to address class imbalance. The research stages include exploratory data analysis, data cleaning, feature selection, data splitting, feature encoding, data balancing, modeling, and evaluation using accuracy, precision, recall, F1-score, training time, and confusion matrix. The results show that all models achieved high performance, with accuracy and F1-score values above 99%. The best performance was obtained by XGBoost with SMOTENC, achieving an accuracy of 99.59% and an F1-score of 99.59%.
Co-Authors Hidayat Ramadhani Hidayat Ramadhani Imelda Imelda Intan Oka Herdanis Jan Everhard Riworuhi Reza Pahlevi