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Journal : INOVTEK Polbeng - Seri Informatika

Quality of Service Analysis on the Steam Link Platform as an Alternative to Online Gaming Technology Patandung, Gabriel; Dewi, Christine
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/rk0s3v40

Abstract

Cloud gaming is a modern gaming option that has emerged as a result of technological advancements, offering users the convenience of playing games without requiring high-end hardware. This study aims to analyze the Quality of Service (QoS) of the Steam Link platform as an alternative to traditional cloud gaming technology. The evaluation focuses on two types of clients—Android devices and laptops—using quantitative methods, benchmarking, and TIPHON standardization. The tested parameters include throughput, delay, frame rate, and the usage of CPU, GPU, and RAM. Experiments were conducted for 15 minutes across three game genres (FPS, Racing, and Open World), using resolutions of 720p and 1080p, and bandwidth levels of 30, 40, and 50 Mbps. Each scenario was tested three times. The host device used a PC with an Intel Core i5-6400 processor and GTX 1070 GPU, while the clients included a Xiaomi 12 smartphone and an Acer TravelMate i3-1115G4 laptop. Test results showed throughput ranging from 14.542 to 33.920 Mbps, delay between 1.382 and 1.721 ms, and frame rates stable between 30 and 60 FPS. CPU and RAM usage remained under 30%, indicating efficient performance. However, issues such as host stuttering and performance differences between clients were observed. According to TIPHON standards, both throughput and delay were rated as very good. With a stable 50 Mbps network connection, Steam Link proves to be a practical and affordable alternative for cloud gaming.
Classification of Skin Diseases Using YOLOv11 Tappi, Liputra Pronimus; Dewi, Christine
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9zv65764

Abstract

The skin, as the largest organ in the human body, is susceptible to various diseases that can be transmitted through direct contact or environmental exposure. Early detection of conditions such as cancer is crucial for effective treatment. This study implements the YOLOv11 algorithm to classify four types of skin diseases: Actinic Keratosis, Basal Cell Carcinoma, Melanocytic Nevus, and Melanoma. Using a Kaggle dataset of 2,000 images (500 per class), the images were processed by resizing them to 640×640 pixels and applying augmentation techniques (flipping, rotation, lighting adjustments) to enhance model robustness. The data was split into training (85%), validation (10%), and testing (5%). Model training on Google Colab (T4 GPU, 100 epochs) achieved an overall accuracy of 79%. Evaluation metrics showed strong results for Actinic Keratosis (precision=0.92, recall=0.92, F1=0.92) but lower performance for Melanoma (recall=0.59), likely due to class imbalance. Aggregate metrics indicated precision=0.80, recall=0.73, and F1=0.76, demonstrating reliable detection despite uneven performance across disease types. The main limitations include: a limited dataset size affecting model generalization; variability in image quality and lighting; and bias toward certain classes.