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All Journal Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Mandiri IT Jurnal Komputer, Informasi dan Teknologi
H.A Danang Rimbawa
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Computer Science & IT Electrical & Electronics Engineering Library & Information Science Mathematics

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EVALUASI PENENTUAN PERSONIL IT INFRASTRUKTUR PADA TNI AL MENGGUNAKAN KOMBINASI METODE AHP DAN VIKOR Abdillah, Abdillah Imam Julianto; H.A Danang Rimbawa; Yudistira Asnar
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 17 No 1 (2023): Mei 2023
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v17i2.3094

Abstract

In the current development of technology, information technology is not only used in the economic field, but is divided into various fields, including the military world of the Navy. In order to promote the capability of human resources in the field of IT infrastructure in the Navy, it is necessary to select the right personnel in the maintenance of IT infrastructure assets, so as to obtain competent personnel output in answering future challenges. To facilitate the determination of the right IT infrastructure personnel, the author implements a system by combining the AHP and Vikor methods based on Python. The AHP method is used to determine the weight of criteria in the selection of IT personnel, and the Vikor method is used to rank the appropriate IT personnel. With this system, it is hoped that it can be a supporting application to help select IT personnel according to tactical, technical, strategic criteria.
Quantum Entropy-Based Encryption for Securing Communication Devices in TNI AU Space Units Kencana, Lisdi Inu; H.A Danang Rimbawa; Bisyron Wahyudi
Jurnal Komputer, Informasi dan Teknologi Vol. 4 No. 2 (2024): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v4i2.2091

Abstract

The rising threat of cyber-attacks demands advanced encryption technologies to ensure secure communication. This study evaluates the performance and security of the Quantum Shieldz Cipher integrated with Quantum Entropy-Based Encryption (QEBE) to address the limitations of conventional encryption methods. The main objective is to test the system's ability to generate unpredictable encryption keys, detect interception attempts, and resist quantum-based cyber threats. Experiments were conducted under various operational scenarios, including standard conditions, high interference, and high bandwidth environments, with a focus on its implementation for strategic communication in the Indonesian Air Force (TNI AU).The results show that QEBE effectively generates highly secure encryption keys using the Quantum Random Number Generator (QRNG), significantly reducing the risk of brute-force attacks. The system successfully detects interception by identifying changes in qubit states during data transmission. The implementation within TNI AU demonstrates its effectiveness in securing critical communication systems that require robust protection. However, the system relies heavily on stable network infrastructure with high bandwidth to maintain optimal performance. Compared to conventional methods, QEBE provides superior security and resistance to quantum-based attacks, albeit with a slight trade-off in processing speed. In conclusion, the Quantum Shieldz Cipher integrated with QEBE shows significant potential for enhancing secure communication systems, particularly in critical operations within TNI AU. This technology is a promising solution to safeguard against evolving cyber threats and quantum-based attacks.
Machine learning-based approach for evaluating physical fitness through motion detection Rais, M. Fazil; Chadafa Zulti Noorta; M. Ilham AlFatrah; H.A Danang Rimbawa; Fatmawati, Uvi Desi
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.406

Abstract

Physical fitness assessment is crucial for evaluating an individual's physical performance and endurance. However, traditional methods often rely on manual observation, which can lead to subjectivity and inconsistent results. This study proposes a machine learning-based approach for physical fitness evaluation through motion detection using pose estimation and exercise classification models. A quantitative method was employed to train and evaluate models for four exercise types: push-ups, sit-ups, pull-ups, and chinning. Each model was trained separately and assessed using accuracy, precision, recall, and F1-score metrics, achieving accuracies of 97.50% for push-ups, 97.67% for sit-ups, 97.00% for pull-ups, and 98.50% for chinning. The maximum error margin compared to manual counting was 2.48%. System-generated outputs were validated against manual observations using standard evaluation matrices. These findings indicate that machine learning can offer a reliable, consistent, and automated solution for physical fitness assessment, with the potential to enhance training programs, support remote fitness monitoring, and reduce human error in performance evaluation.
Co-Authors Abdillah, Abdillah Imam Julianto Bisyron Wahyudi Chadafa Zulti Noorta Kencana, Lisdi Inu M. Ilham AlFatrah Rais, M. Fazil Uvi Desi Fatmawati Yudistira Asnar