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All Journal Media Journal of General Computer Science (MJGCS) Jurnal Edukasi Terkini
Ramadani, Dinda Putri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Journal : Media Journal of General Computer Science (MJGCS)

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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.
Detection Skin Disease using Convolutional Neural Network Model Ramadani, Dinda Putri; Praditya Oktanza Djaduk Wibisono; Prayitno
Media Journal of General Computer Science Vol. 2 No. 2 (2025): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v2i2.51

Abstract

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.
Co-Authors Dinda Khairunnisa, Dinda Handrian, Muhammad Diandy Luthfiardi Muhammad Ismail Praditya Oktanza Djaduk Wibisono Prayitno Rahimah, Aisyah RR. Ella Evrita Hestiandari Saputri, Hernisa Wibisono, Praditya Oktanza Djaduk