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Journal : Journal of Soft Computing Exploration

Performance analysis of amd ryzen 5 4600h mobile processor undervolting using AMD APU tuning utility on cinebench R23 Sulistiyono, Mulia; Ariadi, Muhammad Vicri; Kharisma, Rizqi Sukma; Saputro, Uyock Anggoro
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.369

Abstract

In an effort to optimize laptop performance for gaming and high-demand applications without costly hardware upgrades, this research investigates the impact of CPU undervoltage using the AMD Ryzen Mobile 4600H processor. Undervolting, the process of reducing the CPU's voltage supply, is proposed as a strategy to enhance performance by lowering operational temperatures, potentially allowing for more efficient processing. This study uses the AMD APU Tuning Utility to adjust voltage settings and assesses performance changes using a series of benchmarks. Initial findings indicate that undervoltage can indeed have beneficial effects. The most significant data point from the research is the comparison of Cinebench R23 scores before and after applying undervolting settings. From a baseline score of 6835 points, system performance increased to 7880 points in the optimal undervolting scenario, an improvement of 1045 points. This shows a noticeable enhancement in processing efficiency. However, the study also reveals some complexities in undervolting, such as an initial drop in performance in the first configuration before gains are realized in subsequent adjustments. Efficiency values varied across different settings, starting with a decrease (-0.41) and culminating in a substantial gain (+1.54) by the fourth configuration. These results suggest that while undervolting can improve performance, the outcomes depend significantly on finding the right voltage balance, highlighting the nuanced nature of CPU voltage manipulation for performance optimization.
Digital image based IoT intelligent fire detection with telegram notification Kharisma, Rizqi Sukma; Ibrahim, Malik
Journal of Soft Computing Exploration Vol. 6 No. 2 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i2.580

Abstract

Due to inadequate handling, fire disasters often result in significant losses and even loss of life. A fire detection system is essential, especially in places prone to fire. In this study, a digital image-based IoT system was built using the YOLO (You Only Look Once) algorithm to detect and provide fire warnings quickly and accurately. This research was conducted to develop a fire detection system from existing research on IoT devices by combining it with digital image processing technology with the YOLOv8 algorithm, as well as integrating the IoT system into the Telegram instant messaging application. This study also combines a fire detection system with a fire sensor, MQ-2 temperature sensor, and MQ-2 smoke sensor. The study results show that the YOLOv8 nano model with ESP32-CAM can detect small flames from candles up to a distance of 220 cm. The ESP32 fire sensor can detect small flames up to a distance of 90 cm and large flames up to a distance of 140 cm. VPS can be sent to the Telegram application, just as the LM35 temperature sensor detects temperatures above 50ºC and the MQ-2 smoke sensor detects smoke levels above 450 ppm. All data obtained can be displayed on the VPS dashboard and the Telegram application.