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.
Articles
25 Documents
<![CDATA[Optimizing Predictions For Thyroid Disease Sufferers Using Correlation Matrix And Random Forest With Hyperparameter Tuning ]]>
<![CDATA[Angelina]]>;
<![CDATA[Filosofia, Nadhea]]>;
<![CDATA[Arga Wijaya, Riyan]]>
<![CDATA[ Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v2i1.26
<![CDATA[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%.]]>
<![CDATA[Optimizing Amazon Reviews Using Principal Component Analysis, Feature Selection On Random Forest Classifier ]]>
<![CDATA[M Nabil Fadhlurrahman]]>;
<![CDATA[Mutiara Yudina Fitrah]]>
<![CDATA[ Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v2i1.35
<![CDATA[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.]]>
<![CDATA[Detecting the Number of Students Using YOLOv11 to Prevent Proxy Attendance at Universitas Dinamika Bangsa ]]>
<![CDATA[Saputri, Rhadis Steffani]]>;
<![CDATA[Apriliani, Aulia]]>;
<![CDATA[Mukminin, Amirul]]>
<![CDATA[ Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v2i1.38
<![CDATA[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.]]>
<![CDATA[Detecting DoS and SPOOFING Attacks with DNN-based IDS using CICIoT 2024 DataSheet ]]>
<![CDATA[Budi Setiawan]]>;
<![CDATA[Pratama, Raka Jumersyah]]>;
<![CDATA[Putra, Hengkky Restu]]>;
<![CDATA[Sukoco, FA Bambang]]>
<![CDATA[ Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v2i1.90
<![CDATA[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%.]]>
<![CDATA[Development of an Adventure Game Using Construct 3: The Lost: Roux's Escape ]]>
<![CDATA[Ramadani, Dinda Putri]]>;
<![CDATA[Wibisono, Praditya Oktanza Djaduk]]>;
<![CDATA[Prayitno]]>;
<![CDATA[Ismail, Muhammad]]>
<![CDATA[ Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v2i1.98
<![CDATA[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.]]>
<![CDATA[Design of the Internal Personnel Administration Information System at the Regional Personnel and Human Resource Development Agency of Jambi City ]]>
<![CDATA[Feri Pebrianto]]>;
<![CDATA[Renaldi Yulvianda]]>;
<![CDATA[Mochammad Arief Hermawan Sutoyo]]>
<![CDATA[ Media Journal of General Computer Science Vol. 2 No. 2 (2025): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v2i2.34
<![CDATA[The city of Jambi is an administrative entity responsible for assisting the mayor in matters related to staffing, education, and training. Currently, the administrative process, particularly in task letter numbering, still encounters issues with number duplication. Therefore, an internal administrative information system using PHP programming language and MySQL DBMS is needed. The system development method used is the waterfall model with a unified model language approach, utilizing activity diagrams, use case diagrams, and class diagrams. This information system produces outputs that display the accumulated days of official trips per employee and generates task letters as well as travel orders automatically according to the official document format, thereby facilitating the printing and reporting process at the Regional Personnel and Human Resources Development Agency of Jambi City.]]>
<![CDATA[Detection of Chili Plant Pests and Diseases using Yolov5 ]]>
<![CDATA[khairunisah, sherli]]>;
<![CDATA[Siti Lutfiah]]>;
<![CDATA[Fitria Musdalifah]]>
<![CDATA[ Media Journal of General Computer Science Vol. 2 No. 2 (2025): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v2i2.40
<![CDATA[The rapid advancement of technology has led to the development of various innovative techniques that assist humans in numerous domains, including object detection, which serves to identify individual elements within an image. Object detection is widely utilized due to its ability to accurately recognize each component in an image, making it valuable in addressing real-world challenges. One such challenge is the decline in agricultural income resulting from diseases affecting chili plants. The cultivation of chili plants faces several obstacles, including weather-related factors that contribute to the spread of pests and diseases, ultimately reducing chili production. By implementing object detection technology, farmers can easily identify plant diseases through image analysis, enabling timely and effective treatment. This study employs the YOLOv5 algorithm to evaluate the model's performance in detecting diseases in chili plants. The images used were captured using a smartphone camera with a resolution of 3472×3472 pixels. A total of 430 images were utilized, divided into three subsets: training data, validation data, and test data. To obtain the optimal model, the study conducted three experiments using different data distribution ratios: Experiment 1 with a 70:20:10 split, Experiment 2 with a 75:15:10 split, and Experiment 3 with an 80:10:10 split. Among these, the third experiment yielded the best performance, achieving an average test accuracy of 0.947. The corresponding precision, recall, and mean Average Precision (mAP) scores were 0.946, 0.936, and 0.959, respectively]]>
<![CDATA[Detection Skin Disease using Convolutional Neural Network Model ]]>
<![CDATA[Ramadani, Dinda Putri]]>;
<![CDATA[Praditya Oktanza Djaduk Wibisono]]>;
<![CDATA[Prayitno]]>
<![CDATA[ Media Journal of General Computer Science Vol. 2 No. 2 (2025): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v2i2.51
<![CDATA[Skin diseases are one of the most important global health problems; thus, early and correct diagnosis is very critical for effective treatment. The following research introduces a Convolutional Neural Network model developed in TensorFlow for classifying skin diseases based on the Skin Cancer MNIST: HAM10000 dataset, a rich collection of dermatoscopic images of pigmented lesions. The goal is to improve diagnostic accuracy and efficiency through automated image classification. The dataset undergoes preprocessing in order to improve the model's generalization ability. Design a CNN model and train it on a large number of images to distinguish different lesion types. Measure its performance based on various metrics, including accuracy, precision, recall, and F1-score. Preliminary results achieved very high accuracy in the classification task, which is an indicative capability for the support model. Future research will be targeted at real-time applications, including the addition of more data to increase coverage. The present study emphasizes the potential role of deep learning in medical diagnostics and provides a useful tool for the automatic recognition of skin diseases, thereby contributing to improved health outcomes.]]>
<![CDATA[Design of a Decision Support System for Scholarship Award Recommendations Using the TOPSIS Method (Case Study: MTS Al-Hidayah, Jambi City) ]]>
<![CDATA[Ramdani Saputra]]>;
<![CDATA[Jasmir]]>;
<![CDATA[Dodi Sandra]]>
<![CDATA[ Media Journal of General Computer Science Vol. 2 No. 2 (2025): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v2i2.104
<![CDATA[Processing scholarship data at MTS Al-Hidayah Kota Jambi takes a relatively long time in determining who is eligible to receive the scholarship and in recording student data there are often errors in inputting student identities so that a scholarship decision support system is needed using PHP programming languages and MySQL DBMS. The author develops the system using the waterfall method and uses the unified model language system model approach using use case diagrams, activity diagrams, class diagrams and flowchart documents. The results of the system can display student assessment data and the results of the calculation of scholarship awarding with the TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution)]]>
<![CDATA[Optimization of Heart Failure Risk Prediction Using Random Forest Classifier Algorithm ]]>
<![CDATA[M Nabil Fadhlurrahman]]>;
<![CDATA[Winanto, Eko Arip]]>
<![CDATA[ Media Journal of General Computer Science Vol. 2 No. 2 (2025): MJGCS ]]>
Publisher : <![CDATA[MASE - Media Applied and Engineering ]]>
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DOI: 10.62205/mjgcs.v2i2.105
<![CDATA[This study discusses the optimization of heart failure prediction using the Random Forest Classifier algorithm with a focus on feature selection marked by a threshold and the number of features used. The results of the analysis show that the right threshold has a significant effect on model performance. At a threshold of 0.02, the model achieves the best performance with the highest accuracy, precision, and F1-score values. However, increasing the threshold above 0.08 causes a gradual decrease in model performance. In addition, the number of features used also affects the prediction results, where the right combination of features can increase the effectiveness of the classification. Therefore, this study emphasizes the importance of optimizing thresholds and feature selection in building more accurate and efficient prediction models.]]>
