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All Journal Jurnal Media Infotama Jurnal Media Computer Science
Dian Novianto
ISB Atma Luhur
Computer Science & IT

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Optimalisasi Koneksi Local Area Network (LAN) Menggunakan Metode Fasttrack Pada Routerboard Mikrotik Dian Novianto; Yohanes Setiawan Japriadi; Lukas Tommy
Jurnal Media Infotama Vol 19 No 1 (2023): April
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v19i1.3548

Abstract

Internet network connection is very important to support the performance of an organization. Latency on the network can make the throughput on the network smaller, this can disrupt business processes that require the internet in an organization. To improve internet performance, we need a method that can reduce latency and increase throughput, one of which is fasttrack. Fasttrack is an option available on router devices from Mikrotik which can be used to speed up internet connections on a local network by doing several bypasses on running connections. In this case, the researcher tested the effectiveness of using a fasttrack connection on a RB951Ui-2ND type mikrotik routerboard as the main device in optimizing internet network connections, by making several settings to speed up internet connections. A model is needed for network system development to direct network development, here researchers use a model, namely PPDIOO whose contents are: Preparation, Planning, Design, Implementation, Operations, and Optimization. Supporting tools are also needed to help develop the system, namely the Unified Modeling Language (UML). The test results show that using Mikrotik fasttrack can reduce latency so that internet connection performance increases by up to 59% compared to using standard configurations. In addition, using a fasttrack connection can also reduce CPU resource usage on the used mikrotik routerboard.
Implementation Of Glcm And KNN Algorithms For Classification Of Real Faces And Screen Displays Dian Novianto; Yohanes Setiawan Japriadi; Lukas Tommy; Sujono Sujono
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.10271

Abstract

The significance of image processing technology has grown tremendously in recent years, with its applications now encompassing various fields, including biometric sistems. A salient concern in the realm of biometric sistems pertains to the potential for face forgery through images exhibited on mobile device screens. The objective of this research is to develop a method for detecting skin texture that can differentiate between real human skin and skin displayed on a mobile device's interface. The proposed approach utilizes the Gray Level Co-occurrence Matrix (GLCM) to analyze the skin texture, while the K-Nearest Neighbor (KNN) algorithm is employed to differentiate between human skin and the screen. The GLCM operates through the extraction of texture features, including contrast, energy, homogeneity, and correlation, from grayscale images. These features are then trained and tested using the KNN algorithm to classify the image into the real skin or screen category. Then LBP (Local Binary Pattern) was added to see the micro texture which can help GLCM detect texture more accurately. The dataset under consideration in this study is comprised of a series of images depicting human skin, along with images of human skin displayed on a mobile phone screen. These images have undergone a series of processing steps, including preprocessing and feature normalization. The evaluation results demonstrate a classification accuracy rate of 90%, suggesting that the method is effective in identifying distinctive texture patterns between human skin and screen. This research also contributes to the development of biometric sistems, such as face-based attendance, which are designed to detect fraud, such as the use of fake faces from the screen.
Co-Authors Lukas Tommy Sujono Yohanes Setiawan Japriadi