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Systematic Literature Review: Application of Interactive Educational Games ‘Climate Change and Mitigation Effort’
Full text linkThe purpose of this Systematic Literature Review (SLR) is to explore the role of interactive educational games in increasing public awareness about climate change and the mitigation strategies that can be adopted. The review examines how educational games can enhance understanding of climate change by integrating narratives, simulations, and gamification. A systematic approach was used to collect and analyze 28 relevant academic papers, focusing on interactive games used to teach climate change. The methodology involved identifying studies from various databases, applying specific inclusion and exclusion criteria, and synthesizing findings from studies that explore the effectiveness of games in environmental education.The review found that interactive educational games, especially those utilizing augmented reality (AR), simulation, and narrative-based approaches, are effective tools for raising awareness about climate change. These games engage players by simulating real-world environmental challenges and offering mitigation solutions. However, the effectiveness varies depending on the audience\u27s age, background, and technical skills. Challenges such as limited access to technology and differing levels of engagement across age groups were identified, but these can be addressed by using more accessible mobile platforms and gamified learning experiences. This SLR contributes to the understanding of how interactive games can be a valuable tool in climate change education. It highlights the potential of combining emerging technologies like AR and machine learning with traditional educational methods to create engaging and effective learning experiences. The paper provides insights into the current state of research on game-based climate change education.
Introduction to the Solar System Based on Augmented Reality: An Interactive Learning Tool for Middle School Students
Full text linkPenelitian ini bertujuan untuk mengevaluasi pengaruh teknologi Augmented Reality (AR) terhadap tingkat pemahaman siswa dalam pembelajaran tata surya di sekolah menengah. Perancangan penelitian ini menggunakan metode kuantitatif dengan pendekatan survei pada 30 siswa menengah di Indonesia. Data dikumpulkan melalui instrumen yang telah diuji validitas dan reliabilitasnya (Cronbach\u27s Alpha = 0,719). Analisis regresi sederhana digunakan untuk menentukan hubungan antara penggunaan AR dan tingkat pemahaman siswa. Hasil menunjukkan bahwa teknologi AR memiliki pengaruh signifikan terhadap pemahaman siswa dengan nilai signifikansi 0,001. Persamaan regresi di mana setiap peningkatan satu unit penggunaan teknologi AR meningkatkan pemahaman siswa sebesar 0,300 unit. Keaslian penelitian ini terletak pada penerapan teknologi AR untuk materi tata surya, dengan hasil yang mendukung potensi AR sebagai alat pembelajaran interaktif modern, meskipun pengaruhnya dipengaruhi oleh faktor lain
Performance Analysis of Power Link Budget and Rise Time Budget to Support Fiber Optic Connectivity Telkom University
Full text linkFaculty of Applied Sciences, Telkom University has an Optical Communication System Laboratory designed as a supporting facility for the Optical Communication System course. One of the main obstacles is that this laboratory does not yet have a miniature Fiber to the X (FTTX) network like the optical cable-based internet network topology owned by internet service providers, resulting in a digital divide and limited practical experience for students. To overcome this problem, the development of an optical cable layout on a special FTTX network was applied to the Optical Communication System laboratory, so that participants understand the concept of Optical Distribution Cabinet (ODC) to Optical Distribution Point (ODP). The results of this study indicate that the fiber optic cable layout has succeeded in connecting the Optical Line Termination (OLT) at the Faculty of Applied Sciences to the Optical Distribution Point (ODP) 800 meters away (Hotel Lingian), as well as connecting to several laboratories in the Faculty of Applied Sciences environment that are connected to the Optical Distribution Center (ODC). The results of the Power Link Budget measurements for Downlink and Uplink and Bit Error Rate have values of -23.920 dBm, -24.631 dBm, respectively. While the Rise Time Budget value in uplink, downlink conditions with a value of 0.334 ns and 0.426 ns and the results of the Bit Error Rate (BER) are 16.37 × 10^(-13) and 15.25 × 10^(-12). The measurement value shows that the design has met the standards of the existing value parameters.
 
Comparison Of Blurred Image Restoration Methods Using CNN, Non-Local Means (NLM), and Lucy-Richardson
Full text linkPurpose: Blurred images caused by camera motion, poor lighting, or inaccurate focus are common challenges in digital imaging. These issues not only affect visual aesthetics but also risk the loss of critical information, particularly in forensic analysis, medical diagnostics, and historical documentation. This study aims to compare the effectiveness of three image restoration methods—Convolutional Neural Network (CNN), Non-Local Means (NLM), and Lucy-Richardson—through a systematic literature review approach.
Design/methodology/approach: This research adopts a Systematic Literature Review (SLR) methodology, analyzing peer-reviewed articles from IEEE Xplore and other reputable sources. Each method is evaluated based on key restoration criteria, including detail preservation, noise handling, and computational complexity.
Findings/result: CNNs demonstrate superior performance in restoring semantic and complex structural details due to their deep learning capabilities, although they require large datasets and longer training times. NLM is effective in reducing noise and preserving texture details but is computationally intensive. The Lucy-Richardson algorithm, as a classical deconvolution method, is relatively simple and does not require training data, yet it heavily depends on accurate point spread function (PSF) estimation and is susceptible to noise amplification. The analysis indicates that a hybrid approach combining these methods can significantly enhance image restoration quality.
Originality/value/state of the art: This study offers a comprehensive comparative analysis of three widely used restoration techniques and highlights the potential of hybrid systems. By integrating the strengths of CNN, NLM, and Lucy-Richardson, a more adaptive and optimal restoration strategy can be developed to address diverse types of image degradation
Enhancing The Accuracy of Small Object Detection In Traffic Safety Attributes Using Yolov11 And Esrgan: Peningkatan Akurasi Deteksi Objek Kecil pada Atribut Keselamatan Berkendara Menggunakan Yolov11 dan ESRGAN
No full textThis study aims to detect motorcycle rider attributes, specifically helmets and side mirrors, using a deep learning approach combining YOLOv11 and ESRGAN models. The proposed model addresses challenges in attribute detection under real-world conditions, such as low-resolution images, varying angles, and uneven lighting. The dataset comprises images of motorcycle riders captured by surveillance cameras (CCTV), which underwent preprocessing, augmentation, and resolution enhancement using ESRGAN to improve input quality.
The results demonstrate that ESRGAN significantly enhances the performance of YOLOv11, particularly for high-resolution images. The YOLOv11 + ESRGAN model with 300 epochs achieved the best performance, with precision of 75.8%, recall of 69.1%, and an F1-score of 0.7 during testing. During validation, the model reached a precision of 0.826 and recall of 0.797, indicating good generalization capabilities. Compared to the YOLOv11 model without ESRGAN, this combination significantly improved accuracy, especially in detecting small attributes such as side mirrors.
This study suggests further exploration with larger and more diverse datasets and fine-tuning to enhance detection accuracy. Additionally, integrating the model into real-world systems based on edge computing can accelerate real-time inference and reduce reliance on cloud-based servers. With broader implementation, this model has the potential to improve the efficiency and safety of AI-powered traffic monitoring systems
Optimization of stock price prediction of PT Aneka Tambang Tbk (ANTM) using Long Short-Term Memory
Full text linkPurpose: Develop a machine learning model to predict stock market activity by finding the Root Mean Squared Error (RMSE) value.Design/methodology/approach: LSTM (Long Short-Term Memory) is one of the machine learning techniques that can be used to anticipate traffic in realtime. Using this method can be used to analyze stock market activity that has time series data.Findings/result: This research obtained a Root Mean Squared Error (RMSE) value of 43.32.Originality/value/state of the art: By using the same machine learning method as the previous research, namely LSTM. The research provides more efficient results
Analysis of Information System Security Using OWASP ZAP on a Web-Based Electronic Archiving System
Full text linkPurpose: Web-based information systems have become an essential bridge for facilitating accessibility and the use of information. However, with the convenience of access and usage, serious threats related to data security in web systems have also emerged. These threats may arise due to vulnerabilities in the web system, which can be exploited by irresponsible parties to carry out cyberattacks aimed at stealing, damaging, or altering the available information. Therefore, this research is conducted as a preventive measure against these threats through preventive actions by analyzing security vulnerabilities on websites using penetration testing, one of which utilizes the Open Web Application Security Project (OWASP).Design/methodology/approach: Security analysis of information systems using OWASP ZAP with a penetration testing method.Findings/result: The testing results and analys conducted on the target website of the web-based electronic archiving system, http://silancarbedas.bandungkab.go.id/, revealed 13 security vulnerabilities categorized under several OWASP ZAP 10:2021 frameworks. Based on these findings, several suggestions or recommendations have been provided to address the website vulnerabilities, which can be used by the website developers to enhance the site\u27s securityOriginality/value/state of the art: Vulnerability testing on the web-based electronic archiving information system at http://silancarbedas.bandungkab.go.id/ has not been conducted previously
Pemodelan Spasial Kelembaban Tanah Berbasis Indeks Spektral dengan Integrasi Citra Satelit Multi Sensor
Full text linkTujuan: Keakuratan estimasi kelembaban tanah merupakan faktor penting dalam memonitor dan manajemen sumber daya air serta mitigasi dampak lingkungan. Pengukuran kelembaban tanah secara in-situ terbatas oleh biaya dan cakupan spasial yang rendah. Karenanya, integrasi data iklim dan citra satelit menjadi alternatif yang menarik untuk meningkatkan keakuratan estimasi kelembaban tanah. Penelitian ini mengembangkan model spasial kelembaban tanah dengan menggabungkan data iklim (suhu permukaan tanah dan curah hujan) dan indeks spektral dari citra satelit multi-sensor, termasuk Landsat 8 dan Sentinel-2, serta menggunakan algoritma Random Forest untuk klasifikasi kelembaban tanah. Hasil penelitian menunjukkan bahwa pendekatan ini menghasilkan peta kelembaban tanah dengan akurasi Overall Accuracy (OA) sebesar 0.8 dan kappa 0.75 untuk Random Forest, dan akurasi OA sebesar 0.93 dan kappa 0.92 untuk Gradient Boosting. Penelitian ini menyimpulkan bahwa integrasi data iklim dan citra satelit multi-sensor secara signifikan meningkatkan akurasi prediksi kelembaban tanah, memberikan manfaat signifikan bagi perencanaan dan pengelolaan lahan
Development of a Penetration Testing Framework for Identifying Security Vulnerability Solutions in WiFi Networks: Pengembangan Framewok Penetration Testing untuk Proses Pencarian Solusi Kerentanan Keamanan pada Jaringan Wifi
Full text linkThe rapid increase in internet users has driven the development of WiFi networks, which play a crucial role in providing secure internet access, especially within Industry 4.0 and Industry 5.0 environments that rely on efficient data exchange. Penetration testing (pentest) is a vital approach for auditing and evaluating the security level of WiFi networks. Several frameworks such as PTES, PETA, and ISSAF are often used as references, although only a few are explicitly designed for WiFi networks. This study proposes a modification of the PTES framework to better align with the security characteristics of WiFi networks by providing relevant solution recommendations. The integration of the Boyer-Moore algorithm is employed as an efficient method to identify solutions for detected vulnerabilities. The implementation of this framework is demonstrated through testing the suggestion process, which produces solution recommendations based on vulnerabilities found during the pentest. The Boyer-Moore algorithm exhibits high efficiency in generating recommendations with a response time of 0.0000087 seconds
Analysis of the Effectiveness of Data Warehousing in Management Information Systems Using the Neural Networks Method
Full text linkPurpose: The purpose of this research is to investigate the effectiveness of data warehousing and the application of Neural Networks methods in analyzing bicycle travel app user data, with a focus on enhancing the annual membership of app users in North America.Design/methodology/approach: This study utilizes a dataset that includes membership and usage data from relevant bicycle travel apps. It involves comparing the performance of different Neural Networks architectures, such as Feedforward Neural Networks, Convolutional Neural Networks (CNN), and other suitable models, to evaluate their effectiveness in predicting user membership.Findings/result: The analysis results demonstrate that the implementation of Neural Networks can improve prediction accuracy, with the most effective model achieving 76.03% accuracy. The research also highlights the importance of preprocessing steps, such as data normalization and transformation, in contributing significantly to model performance. However, challenges such as overfitting were identified, suggesting the need for further testing with model and parameter variations.Originality/value/state of the art: This research provides valuable insights for application developers and policy makers, helping them create data-driven strategies to improve the bicycle travel management information system. It also supports efforts to sustainably grow user membership. The study contributes to the field by exploring the practical application of Neural Networks for data analysis in the context of bicycle travel management, filling a gap in current research on effective predictive models for user membership growth