Media Journal of General Computer Science (MJGCS)
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.
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<![CDATA[MATLAB Modeling of Component in Electrical Discharge Machining (EDM) Pulses ]]>
<![CDATA[Betantya Nugroho]]>;
<![CDATA[Yahya, Azli]]>;
<![CDATA[Mat Sidek, Abd. Rahim]]>;
<![CDATA[Andromeda, Trias]]>
<![CDATA[ Media Journal of General Computer Science Vol. 1 No. 1 (2024): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v1i1.6
<![CDATA[Electrical Discharge Machining (EDM), is a machining technique for working with conductive materials. During the EDM process, electrical discharge energy is transformed into thermal energy, leading to the erosion of the workpiece. The energy utilized by the EDM process is represented by the time-dependent current, which determines the energy density employed for workpiece erosion. Ideally, during a discharge event, the current pulse should exhibit a square wave shape. However, in practice, EDM circuits often incorporate parasitic components that lead to non-square waveforms or transient currents. In this paper, we describe the simulation of parasitic components using MATLAB, revealing that these components alter the signal waveform and affect the achievement of a square pulse wave in MRR. The presence of parasitic components results in transient current patterns during the discharge phase and, consequently, a reduction in MRR. The implementation of a square wave current, however, enhances the MRR value and increases the efficiency of the EDM process]]>
<![CDATA[Android Security: Malware Detection with Convolutional Neural Network and Feature Analysis ]]>
<![CDATA[Sharipuddin]]>;
<![CDATA[Putra, Rafi Septiandi]]>;
<![CDATA[Aulia, M. Farhan]]>;
<![CDATA[Maulana, Sayid Achmad]]>;
<![CDATA[Jusia, Pareza Alam]]>
<![CDATA[ Media Journal of General Computer Science Vol. 1 No. 1 (2024): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v1i1.7
<![CDATA[Android is a mobile operating system based on a modified version of the Linux kernel and other open-source tools. Due to its system efficiency and the multitude of features it offers to users, the Android operating system has taken a leading position in the technology market and often attracts the attention of cybercriminals. As malware continues to evolve, traditional methods for detecting Android malware, such as signature-based approaches, may not be sufficient to detect the latest malware threats. Therefore, this research proposes a deep learning algorithm, specifically Convolutional Neural Network (CNN) and Component Analysis (PCA), for feature extraction to enhance the accuracy of Android malware detection. The dataset used in this study is the CICAndMal2017 dataset. Testing results are evaluated using three parameters: accuracy, precision, and recall. Experimental results indicate that our deep learning approach outperforms many other methods with an accuracy of 91%.]]>
<![CDATA[Improvement Attack Detection on Internet of Thinks Using Principal Component Analysis and Random Forest ]]>
<![CDATA[Adrian Pirtama]]>;
<![CDATA[Yuda Prasetia]]>;
<![CDATA[Redho Irnindo Saputra]]>;
<![CDATA[Eko Arip Winanto]]>
<![CDATA[ Media Journal of General Computer Science Vol. 1 No. 1 (2024): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v1i1.8
<![CDATA[Network security has become crucial in facing increasingly complex and sophisticated attack threats. Network intrusion detection aids in identifying suspicious activities indicating unauthorized intrusions. This research aims to enhance the performance of advanced attack detection. The Random Forest method is an algorithm that leverages an ensemble of decision trees. This ensemble comprises several independent decision trees used to classify data. One characteristic of the Random Forest method is its ability to address overfitting issues and provide good predictive quality. One approach to improving RF's performance is through Principal Component Analysis (PCA). PCA is a statistical technique used to reduce feature dimensionality. PCA eliminates feature correlations and identifies essential features that can enhance the detection of attacks and normal traffic. This research will be tested with the CIC IoT 2023 dataset, encompassing various attack types. The model testing consists of four feature dimensions, namely 5, 8, 10, and 47. The detection results are promising, significantly improving attack detection performance, reaching up to 99.2%.]]>
<![CDATA[Analysis Of User Satisfaction On Edmodo And E-Learning In Higher Education Student Using Kano Method ]]>
<![CDATA[Pranata, Erika]]>;
<![CDATA[Sutoyo, Mochammad Arief Hermawan]]>;
<![CDATA[Veronica, Sheila]]>;
<![CDATA[Natalie, Sherly]]>;
<![CDATA[Wijaya, Tiara Vinsky]]>;
<![CDATA[Mahendra, Vimaladevie]]>
<![CDATA[ Media Journal of General Computer Science Vol. 1 No. 1 (2024): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v1i1.9
<![CDATA[The Edmodo application and E-learning are learning facilities at University Y in Jambi to anticipate face-to-face learning that has the potential to increase the spread of the virus through direct/face-to-face interactions between lecturer and student. The purpose is to present the value from user satisfaction, determine the effect of application quality and provide suggestions regarding the Edmodo application and university Y E-learning to increase user satisfaction. The method used by the author is the Kano method. Kano's method is a method that identifies user needs and expectations through preference classification techniques. The purpose of the Kano method is to classify the attributes of a product or service by looking at the level of satisfaction and fulfillment of customer needs for the product or services offered. The data was taken by distributing questionnaires made with google forms which have been distributed online and got 58 respondents who are users of the Edmodo application and university Y E-learning. The results of calculations between these two e-learning applications are in the Indifferent phase which means these customers are indifferent to the feature, and do not care about the presence of the feature so that it does not make a significant difference in product satisfaction reaction. In the Edmodo application, students were most satisfied in the easy-to-learn application category and most dissatisfied in the satisfactory display category. As for the university Y E-learning, students were most satisfied in the trustworthy privacy category and the least satisfied in the recommended category.]]>
<![CDATA[Development of Platformer Game “Adventure Drake” Using Finite State Machine Method ]]>
<![CDATA[Roby Setiawan]]>;
<![CDATA[Agus Nugroho]]>;
<![CDATA[Redhy Putra, Bima]]>
<![CDATA[ Media Journal of General Computer Science Vol. 1 No. 1 (2024): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v1i1.10
<![CDATA[Video game is one of the most popular hobbies out there, someone who have been played games always ended with fun experience and adrenaline rush. The people who enjoy this type of hobby so diverse starring with kids, teenager, and adult. With increasing technology playing game is not exclusive on desktop pc or game console, but now can be play on smartphone with android and IOS system operations, so they can play game wherever they want, and whenever they want. For that reason, the author with interested in designing game with the platformer genre using 2D technology and using finite state machine for controlling character animation Therefore author make research platformer game adventure drake using finite state machine, the itself game can be played by anytime at anywhere. The main reason to building this game therefore adding onto the collection Indonesia game’s and analyses the game with implementing finite state machine method. The result from building this game is great, the game success implementing both 2D technology and finite state machine.]]>
