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All Journal Journal of Applied Science, Engineering, Technology, and Education Ceddi Journal of Information System and Technology (JST) Ceddi Journal of Education
Amiruddin, Erwin Gatot
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Other

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Web Based Aviation Communication Tool Information System Amiruddin, Erwin Gatot
Ceddi Journal of Information System and Technology (JST) Vol. 1 No. 1 (2022): April
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v1i1.5

Abstract

In air transportation, communication systems play a vital role as a link between ground personnel, pilots, and other personnel on the ground. However, the process of recording maintenance and repairs of aviation communication equipment still uses conventional, paper-based methods, which are inefficient, error-prone, and require significant space for archiving. This study aims to design and implement an information system for reporting maintenance and repairs of Aviation Communication Facilities at the Makassar Branch of the Public Company (PERUM) LPPNPI, utilizing a web-based platform. Research data was obtained through field observations, data requests, and literature reviews. The system development method used the Waterfall model. The results indicate that the developed information system facilitates technicians in recording maintenance and repair reports more quickly, accurately, and in an organized manner. The system was tested using Black Box and User Acceptance Test (UAT) techniques with 20 respondents. The test results showed a feasibility rate of 85%, indicating that the system can be implemented effectively in the work environment. This innovation enhances the effectiveness of aviation communication facility management and administrative efficiency, while also supporting improved operational safety in air transportation through the availability of more transparent, accurate, and easily accessible data.
Implementation of School Information System at Yaabunaya Fathul Khair Makassar Foundation Kamaruddin; Mustika, Nur; Emasari; Taliang, Askar; Wisda; Amiruddin, Erwin Gatot
Ceddi Journal of Education Vol. 1 No. 2 (2022): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/cje.v1i2.28

Abstract

This research aims to evaluate the implementation of a school information system at the Yaabunaya Fathul Khair Foundation in Makassar and its impact on school management processes. The system is designed to enhance interaction between the school and various stakeholders while ensuring the generation of accurate information. The analysis employs the PIECES method, which examines Performance, Information, Economics, Control, Efficiency, and Service to identify and address issues within the foundation. The research utilizes UML for design, PHP for coding, and MySQL for database management. System testing was conducted using the Black Box method to verify functional performance. The results indicate that the developed system operates effectively and meets its intended functions. User feedback gathered from a questionnaire with 6 respondents answering 10 questions, revealed an average satisfaction score of 86.79%, demonstrating that the application performs well and meets user expectations. This research contributes to the field of educational technology by providing insights into the effective implementation and evaluation of school information systems, highlighting their role in improving school management and stakeholder communication.
Cryptocurrency Risk Management through Decision Engineering: Evaluating XRPUSD and ADAUSD Portfolio Performance Litamahuputty, Jacomina Vonny; Amiruddin, Erwin Gatot; Rahim, Robbi; Rahman, Abdul; Mamadiyarov, Zokir
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3871

Abstract

This research examines the risk profiles of XRPUSD and ADAUSD cryptocurrencies through Value at Risk (VaR) analysis with Monte Carlo simulation, providing quantitative risk assessments for both individual assets and a diversified portfolio. Analyzing historical price data from January 2016 to November 2024, the study identifies distinctive risk characteristics between these cryptocurrencies: ADAUSD exhibited marginally higher historical returns (1.44% monthly) compared to XRPUSD (1.42%), but with notably higher volatility (standard deviation of 5.41% versus 4.65%). The Monte Carlo simulation with 1,000 iterations generated VaR estimates at multiple confidence levels, revealing that XRPUSD consistently demonstrated lower downside risk than ADAUSD across all confidence thresholds. At the 99% confidence level, ADAUSD showed a Mean VaR of -10.97%, indicating potential monthly losses exceeding $10.97 million on a hypothetical $100 million investment, while XRPUSD's lower Mean VaR of -9.52% translated to potential losses of approximately $9.52 million. The most striking finding emerged from the portfolio analysis, which revealed dramatic risk reduction through diversification—the equally-weighted portfolio achieved a Mean VaR of merely -2.22% at the 99% confidence level, representing an approximately 80% reduction in potential losses compared to ADAUSD alone. These results demonstrate that cryptocurrency diversification can substantially mitigate extreme downside risk while maintaining exposure to the digital asset class. The significant risk reduction achieved through a simple two-asset allocation validates the application of modern portfolio theory principles to cryptocurrency investments despite their unique characteristics and underscores the critical importance of diversified approaches rather than concentrated positions for risk-conscious cryptocurrency investors. This research contributes to both theoretical understanding of cryptocurrency risk dynamics and practical portfolio construction approaches, providing quantitative evidence for the value of diversification strategies in navigating the substantial volatility inherent in digital asset markets.
Student Expression Detection Based on Facial Image Using Convolutional Neural Network (CNN) Muh. Riyaldi Pratama; Amiruddin, Erwin Gatot; Kamaruddin, Kamaruddin; Tahir, Tamus Bin; Qadri, Muhammad; Vivek, Kumar
Ceddi Journal of Education Vol. 4 No. 1 (2025): June
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/cje.v4i1.126

Abstract

The widespread adoption of electronic learning (e-learning) in higher education has brought significant changes to how knowledge is delivered. Despite its advantages, many implementations remain focused solely on content dissemination, often neglecting learners’ emotional engagement. Emotional states, particularly in academic contexts, influence concentration, motivation, and comprehension. One of the most effective and intuitive indicators of emotion is facial expression. This research investigates the use of Convolutional Neural Networks (CNN), a deep learning approach, to automatically detect student emotions through facial image analysis. A dataset of facial expressions was constructed and divided into training and testing sets, each containing five distinct emotional categories: anger, happiness, fear, neutrality, and surprise. The CNN model was trained for 100 epochs, resulting in a training accuracy of 89% and a testing accuracy of 88%. These results demonstrate that CNN-based emotion recognition has strong potential to enhance e-learning platforms by providing instructors with real-time emotional insights. By integrating emotional feedback, educators can adapt instructional strategies more effectively to improve student engagement and learning outcomes. This study contributes to the growing field of affective computing and emphasizes the importance of emotional awareness in digital learning environments.
Co-Authors Abdul Rahman Emasari Kamaruddin Kamaruddin Kamaruddin LITAMAHUPUTTY, JACOMINA VONNY Mamadiyarov, Zokir Muh. Riyaldi Pratama Mustika, Nur Qadri, Muhammad Robbi Rahim Tahir, Tamus Bin Taliang, Askar Vivek, Kumar Wisda