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All Journal International Journal of Electrical and Computer Engineering Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknologi Elekterika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Embedded Systems, Security and Intelligent Systems Journal of Informatics and Computer Engineering Research

Irfan Syamsuddin

Politeknik Negeri Ujung Pandang

Author-ID : 4066853

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 6 Documents Claim Missing Document

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Articles

Decision Making Analysis of Video Streaming Algorithm for Private Cloud Computing Infrastructure Irfan Syamsuddin; Rini Nur; Hafsah Nirwana; Ibrahim Abduh; David Al-Dabass
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 6: December 2017
Publisher : Institute of Advanced Engineering and Science

The issue on how to effectively deliver video streaming contents over cloud computing infrastructures is tackled in this study. Basically, quality of service of video streaming is strongly influenced by bandwidth, jitter and data loss problems. A number of intelligent video streaming algorithms are proposed by using different techniques to deal with such issues. This study aims to propose and demonstrate a novel decision making analysis which combines ISO 9126 (international standard for software engineering) and Analytic Hierarchy Process to help experts selecting the best video streaming algorithm for the case of private cloud computing infrastructure. The given case study concluded that Scalable Streaming algorithm is the best algorithm to be implemented for delivering high quality of service of video streaming over the private cloud computing infrastructure.

A Novel Framework to Select Intelligent Video Streaming Scheme for Learning Software as a Service Irfan Syamsuddin
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 1: EECSI 2014
Publisher : IAES Indonesia Section

Cloud computing offers many benefits for government, business and educational institutions as exemplified in many cases. Options to deliver video streaming contents for educational purposes over cloud computing infrastructures are highlighted in this study. In such case, parameters that affect video quality directly or indirectly must be taken into account such as bandwidth, jitter and loss of data. Currently, several intelligent schemes to improve video streaming services have been proposed by researchers through different approaches. This study aims to propose a novel framework to select appropriate intelligent video streaming schemes for efficiently delivering educational video contents for Learning Software as a Service (LSaaS).

Pemodelan dan Simulasi Photovoltaic Menggunakan Pendekatan Model Tiga Diode Usman; Ahmad Rosyid Idris; Sofyan; Irfan Syamsuddin
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 4: November 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Photovoltaic (PV) performance measurement requires a conditioned environment, which are conditions of solar radiation and temperature standard, thus, a special equipment is required. A simple way to do this is by modeling the PV. The method used to describe the characteristics of PV is equalizing the maximum power of model with the maximum power from datasheet, which is implemented in MATLAB. The simulation results show that the difference between ????????????,???? and ????????????,???? is 0.000314 W and the average absolute error of current, compared to measurement, is 2.159%. The I-V curves obtained in this model are also very identical to the I-V curves using two diode approach or one diode model. The simulation results of this model are also compared to the same model, using the MRFO, SFO, COA, WOA, SA, and GA algorithms, which shows high similarities. I-V curves are strongly influenced by the constants ????, ????????, and ????????. The constant ???????? affects the curvature of the I-V curve, to be precise the curvature at ????????, ????????. Whereas ???????? affects the curvature of the I-V curve before ????????, ???????? and ???????? after ????????, ????????.

User Experience Analytics pada Sistem Informasi Politeknik Negeri Ujung Pandang HM, Nurfaida; Tungadi, Eddy; Syamsuddin, Irfan
Jurnal Teknologi Elekterika Vol. 19 No. 1 (2022): Mei
Publisher : Jurusan Teknik Elektro Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/elekterika.v6i1.3503

The Ujung Pandang State Polytechnic (PNUP) manages several departments, consisting of the Department of Electrical Engineering, the Department of Machinery, the Department of Civil, the Department of Chemistry, the Department of Accounting, and the Department of Commerce Administration. In managing how many significant programs, PNUP requires a quality information system to support academic activities by evaluating the system's quality that has been implemented. The PNUP Information System with the URL address https://.www.poliupg.ac.id/ has never been evaluated, where there are several complaints from students and admins regarding obstacles to using the information system. Complaints about the information system's use prompted researchers to research the quality of the PNUP information system. Therefore, the User Experience was analysed from the usability aspect, which was tested with several tools, namely Woorank, SEOquake, PageSpeed and using the SUS method. Testing with the usability aspect approach was carried out on the information system at PNUP. The test results using several tools, namely Woorank, got a score of 78. It was considered suitable for facilitating searches on search engines. Page Speed scored 67 for desktop use and 28 for mobile use. It was considered very bad in loading website pages which were considered to disturb user satisfaction and using the SUS method, with 180 respondents getting a score of 58.65, which is considered usable and has the potential to reduce visitors to the PNUP information system. It can be judged that the PNUP information system has not met the usability aspect.

IMPLEMENTASI DEEP LEARNING UNTUK PENDETEKSIAN PENGGUNA MASKER PADA CCTV: STUDI KASUS PUSKESMAS SUDIANG RAYA Trisnaningrun, Ainun; Tungadi, Eddy; Syamsuddin, Irfan
Journal of Informatics and Computer Engineering Research Vol. 1 No. 2 (2024)
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jicer.v1i2.5184

The use of mask is one of the things that needs attention when you want to leave the house to implement health protocols to avoid diseases that are currently troubling people in the world or commonly known as Covid-19. Currently, people are reluctant to come to the hospital for fear of being exposed to the Covid-19 virus, so people who need treatment prefer to visit the puskesmas near their home. However, there are still many people who do not use masks on the grounds that the intended location is close to home. To overcome this can be done by detecting visitors' faces using the camera. So a system is proposed, namely the detection of mask users with the Convolutional Neural Network (CNN) method. One of the widely applied CNN methods for processing image data is YOLO. YOLO (You Only Look Once) is a deep learning-based model developed to detect an object in real-time. YOLO works by looking at the image as a whole, then using a neural network and automatically detecting existing objects. So that in this study the YOLO model, namely YOLOv4, was used as an object detection model in a mask detection system with CCTV video media whose data is sent in real-time.

COMPASS: Comparative Evaluation of Machine Learning Algorithms for DDoS Detection Using ANOVA F-Value on AISED Dataset Hartinah; Syamsuddin, Irfan; Syarwani, Andi
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8276

This study presents COMPASS, a comparative evaluation of ten Machine Learning algorithms for DDoS attack detection using the AISED Dataset on Cloud DDoS Attacks. Feature selection was performed using SelectKBest with ANOVA F-Value, evaluating model performance across varying feature dimensions (K = 10, 15, 20, 25). Experimental results demonstrate that ensemble-based methods, particularly Random Forest, Gradient Boosting, and AdaBoost, achieve near-theoretical maximum AUC scores (>0.998) while maintaining fast training times (<0.1 seconds). K-Nearest Neighbors (KNN) also exhibits robust performance (AUC > 0.98) with minimal computational cost. In contrast, Support Vector Machine (SVM) and Quadratic Discriminant Analysis (QDA) show relatively lower accuracy (AUC > 0.85) and suffer from high computational complexity, with SVM requiring up to 572 seconds to train at K=25. These findings highlight the critical trade-off between classification accuracy and computational efficiency in selecting optimal models for real-time DDoS detection systems. As future work, we propose deploying a lightweight version of COMPASS on edge computing devices and integrating it into federated learning frameworks to enable collaborative, privacy preserving model training.

Co-Authors Ahmad Rosyid Idris David Al-Dabass Hafsah Nirwana Hartinah HM, Nurfaida Ibrahim Abduh Rini Nur Sofyan Syarwani, Andi Trisnaningrun, Ainun TUNGADI, EDDY Usman Usman

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