cover
Contact Name
Eko Arip Winanto
Contact Email
ekoaripwinanto@gmail.com
Phone
+6281367704735
Journal Mail Official
mjgcs@mase.or.id
Editorial Address
Contact Jl. R. Wijaya Lorong Akimar No.271, The Hok, Kec. Jambi Sel., Kota Jambi, Jambi 36138
Location
Kota jambi,
Jambi
INDONESIA
Media Journal of General Computer Science (MJGCS)
ISSN : -     EISSN : 30313651     DOI : https://doi.org/10.62205/mjgcs.v1i2.21
Media Journal of General Computer Science (MJGCS), e-ISSN: 3031-3651 is a peer-reviewed journal in Indonesian or English. The purpose of this publication is to disseminate high-quality articles that are devoted to discussing any and all elements of the most recent and noteworthy advancements in the field of computer science. The applications of information technology, applied computing, and computer science are all included in its purview. Skip to main contentSkip to main navigation menuSkip to site footer Open Menu Home / Aims and Scope Aims and Scope Computer Science: Computer Architecture, Parallel and Distributed Computing, Pervasive Computing, Computer Networks, Embedded Systems, Human-Computer Interaction, Virtual/Augmented Reality, Computer Security, Software Engineering (covering Software Lifecycle, Management, Engineering Process, and Engineering Tools and Methods), Programming (encompassing Programming Methodology and Paradigm), and Data Engineering (involving Data and Knowledge Level Modeling, Information Management, Knowledge-Based Management Systems, and Knowledge Discovery in Data). This diverse landscape also includes Network Traffic Modeling, Performance Modeling, Dependable Computing, High-Performance Computing, Human-Machine Interface, Stochastic Systems, Information Theory, Intelligent Systems, IT Governance, Networking Technology, Optical Communication Technology, Next Generation Media, Robotic Instrumentation, Information Search Engine, Multimedia Security, Computer Vision, Information Retrieval, Distributed Computing Systems, Mobile Processing, Next-Generation Networks, Computer Network Security, Natural Language Processing, Business Process, and Cognitive Systems. Information Systems : Data Engineering (comprising Data and Knowledge Level Modeling, Information Management, Knowledge-Based Management Systems, and Knowledge Discovery in Data), Software Engineering (addressing Software Lifecycle, Management, Engineering Process, and Engineering Tools and Methods), Information Retrieval, IT Governance, Networking Technology, Business Process, Intelligent Systems, Multimedia Security, Information Search Engine, Distributed Computing Systems, Mobile Processing, Next-Generation Networks, Computer Network Security, Natural Language Processing, and Cognitive Systems. Signal Processing : Signal Theory, Digital Signal and Data Processing, Stochastic Processes, Detection and Estimation, Spectral Analysis, Filtering, Signal Processing Systems, Environmental Signal Processing, and various applications such as Image Processing, Pattern Recognition, Optical Signal Processing, Multi-dimensional Signal Processing, Communication Signal Processing, Biomedical Signal Processing, Geophysical and Astrophysical Signal Processing, Earth Resources Signal Processing, Acoustic and Vibration Signal Processing, Data Processing, Remote Sensing, Speech Processing, Signal Processing for Audio, Visual, and Performance Arts, Radar Signal Processing, Sonar Signal Processing, Seismic Signal Processing, Medical Imaging Equipment and Techniques, Biomedical Imaging and Image Processing, Video Processing, Industrial Applications, and New Applications. Telecommunication: Modulation and Signal Processing for Telecommunication, Information Theory and Coding, Antenna and Wave Propagation, Wireless and Mobile Communications, Radio Communication, Communication Electronics and Microwave, Radar Imaging, Distributed Platforms, Communication Network and Systems, and Telematics Services and Security Network. Instrumentation & Control: Optimal, Robust, and Adaptive Controls, Nonlinear and Stochastic Controls, Modeling and Identification, Robotics, Image-Based Control, Hybrid and Switching Control, Process Optimization and Scheduling, Control and Intelligent Systems, Artificial Intelligence and Expert Systems, Fuzzy Logic, and Neural Networks, and Complex Adaptive Systems.
Articles 5 Documents
Search results for , issue "Vol. 2 No. 1 (2025): MJGCS" : 5 Documents clear
Optimizing Predictions For Thyroid Disease Sufferers Using Correlation Matrix And Random Forest With Hyperparameter Tuning Angelina; Filosofia, Nadhea; Arga Wijaya, Riyan
Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v2i1.26

Abstract

Thyroid disease is one of the most common endocrine disorders, affecting the body's hormone function and balance. Symptoms can include changes in weight, fatigue, and temperature regulation issues. Although the causes are varied, thyroid disease can generally be treated with medications or medical interventions. The objective of this study is to present and optimize a predictive model for thyroid disease patients by measuring the comparison between correlation analysis of traits and the variables used, as well as evaluating the performance of the Random Forest method in optimizing predictions. One machine learning method that can be used to optimize the prediction of thyroid disease patients is Random Forest. The features used include age, gender, smoking history, radiotherapy history, and pathology characteristics, which are utilized to optimize predictions using this Random Forest algorithm. This study employs hyperparameter tuning, with the best parameters being (n_estimators) 100 and (max_depth) 30, which are then used to predict the occurrence of thyroid disease with an accuracy of 95%.
Optimizing Amazon Reviews Using Principal Component Analysis, Feature Selection On Random Forest Classifier M Nabil Fadhlurrahman; Mutiara Yudina Fitrah
Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v2i1.35

Abstract

Dataset optimization is an important step in machine learning to improve model performance. This review discusses the use of Random Forest, Principal Component Analysis (PCA), and Feature Selection algorithms to optimize datasets. Based on this review, the combination of Random Forest, PCA, and Feature Selection is proven to be effective in improving machine learning model performance. This combination can help reduce overfitting, improve prediction accuracy, and speed up the model training process. In our experiments with the Amazon Reviews dataset, this optimized approach achieved an impressive accuracy of 91%, demonstrating a significant improvement over baseline models.
Detecting the Number of Students Using YOLOv11 to Prevent Proxy Attendance at Universitas Dinamika Bangsa Saputri, Rhadis Steffani; Apriliani, Aulia; Mukminin, Amirul
Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v2i1.38

Abstract

Attendance is an important criterion for passing courses at Universitas Dinamika Bangsa Jambi. According to the academic regulations of Universitas Dinamika Bangsa Jambi, the minimum attendance requirement for course completion is 75%. The attendance process at the university utilizes an academic information system (SIAKAD) where students log in using a username and password, then scan an attendance barcode or input a unique code. Students often engage in proxy attendance practices, where they are marked present in the system despite being absent in reality. This study discusses the prevention of proxy attendance by employing a human detection system based on YOLOv11, capable of counting the number of students present in the classroom at Universitas Dinamika Bangsa Jambi. The research method involves the design, implementation, and evaluation of the system. This study adopts a deep learning approach using supervised learning methods for model training. The model is trained on a labeled dataset from Roboflow and implemented using the YOLOv11 algorithm. Based on the research results, the authors conclude that the human detection system is effective in counting the number of students in the classroom. However, the system still requires further development to detect criteria or features that can distinguish the detected individuals' status, specifically between students and lecturers.
Detecting DoS and SPOOFING Attacks with DNN-based IDS using CICIoT 2024 DataSheet Budi Setiawan; Pratama, Raka Jumersyah; Putra, Hengkky Restu; Sukoco, FA Bambang
Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v2i1.90

Abstract

With the rapid expansion of IoT (Internet of Things) devices, ensuring network security has become a critical challenge. Distributed Denial of Service (DoS) and spoofing attacks are among the most common and damaging threats in IoT ecosystems. Traditional Intrusion Detection Systems (IDS) often face difficulties in detecting these attacks due to the high volume and complexity of IoT network traffic. This study introduces a novel Deep Neural Network (DNN)-based IDS designed to effectively detect DoS and spoofing attacks using the CICIoT 2024 dataset. The CICIoT 2024 dataset provides a comprehensive benchmark with realistic IoT network traffic patterns, including benign and malicious activities. The results highlight the potential of DDN-based IDS to enhance IoT network security, paving the way for more resilient and intelligent defense mechanisms against evolving cyber threats. The detection results are promising, significantly improving attack detection performance, reaching up to 100%.
Development of an Adventure Game Using Construct 3: The Lost: Roux's Escape Ramadani, Dinda Putri; Wibisono, Praditya Oktanza Djaduk; Prayitno; Ismail, Muhammad
Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v2i1.98

Abstract

Adventure games include some of the most popular genres in the gaming industry, with rich narrative arcs and interactive game play. In this research study, the 2D adventure game is designed and developed by using a game engine called Construct 3, which is an easy-to-use and powerful game development application. This study encompasses the design and execution of game mechanics, narrative and graphical elements to provide an immersive experience. We employed an iterative design methodology, which included game prototyping, testing and refinement. The project was assessed in regard to vitality based on gameplay smoothness, user engagement, visuals and received positive feedback from initial testers. Overall, the results point to the successful use of Construct 3 as a development tool, especially for indie developers and educators looking to design gamified learning environments. Overall, this study shows that Construct 3 can help in making game development more accessible to a broader audience, ultimately leading to a more balanced domain of creative outputs in the industry.

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