Article Per Year (5 Year)
p-Index From 2020 - 2025
0.778
P-Index
This Author published in this journals
All Journal Jurnal ULTIMA InfoSys Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Sihombing, Rudolf Paris Parlindungan
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Perbandingan Kompresi NTFS Terhadap Kompresi Lain dari Tingkat Kompresi dan Kecepatan Baca dan Tulis Azzahra, Fadel; Sihombing, Rudolf Paris Parlindungan; Simanjuntak, Mirza Uliartha; Kardian, Aqwam Rosadi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 1 (2023): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i1.555

Abstract

This study aims to compare the read and write speed levels between NTFS compression and compression in software, both with the Lempel-Ziv algorithm and other algorithms. Results were retrieved by comparing the compression and decompression rates using the NTFS file system and the 7-Zip application which was performed using files with different file types and sizes. It is concluded that NTFS compression with process automation capability is quite good in terms of read and write speed, although in terms of compression level it is still not as good as compression in software. This research does not only compare one file compression algorithm to another, but also how the algorithm is implemented in different ways/media, in this case on the NTFS file system and on third-party software (7-Zip).
CodeGuardians: A Gamified Learning for Enhancing Secure Coding Practices with AI-Driven Feedback Hadiprakoso, Raden Budiarto; Sihombing, Rudolf Paris Parlindungan
ULTIMA InfoSys Vol 15 No 2 (2024): Ultima Infosys: Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v15i2.3858

Abstract

This paper introduces CodeGuardians, a gamified platform designed to improve secure coding practices using AI-driven, real-time feedback. The platform focuses on key secure coding concepts, such as input validation, authentication, session management, and cryptography. Developed using the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) instructional model, CodeGuardians enhances engagement and knowledge retention by incorporating interactive challenges. The AI component, powered by OpenAI, provides adaptive feedback on user-submitted code, helping users to learn secure coding practices more effectively. To assess its impact, a one-group pretest-posttest design was conducted. The results of a paired sample t-test showed a significant improvement in secure coding knowledge (t = 19.50, p = 0.048), confirming the platform’s effectiveness. In addition, the system’s usability was rated highly, with a score of 0.93 on the Computer System Usability Questionnaire (CSUQ), classifying it as "Very Good." The practical implications of this research suggest that CodeGuardians could be implemented in both educational and professional settings to enhance secure coding skills and reduce software vulnerabilities. From a theoretical standpoint, this study advances cybersecurity education by integrating AI-driven feedback into gamified learning environments. The research supports the theory that gamification improves engagement and learning retention, while also highlighting the value of adaptive technologies in addressing real-world security challenges. Future work will examine the long-term retention of knowledge and scalability across diverse learning environments.
CodeGuardians: A Gamified Learning for Enhancing Secure Coding Practices with AI-Driven Feedback Hadiprakoso, Raden Budiarto; Sihombing, Rudolf Paris Parlindungan
ULTIMA InfoSys Vol 15 No 2 (2024): Ultima Infosys: Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v15i2.3858

Abstract

This paper introduces CodeGuardians, a gamified platform designed to improve secure coding practices using AI-driven, real-time feedback. The platform focuses on key secure coding concepts, such as input validation, authentication, session management, and cryptography. Developed using the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) instructional model, CodeGuardians enhances engagement and knowledge retention by incorporating interactive challenges. The AI component, powered by OpenAI, provides adaptive feedback on user-submitted code, helping users to learn secure coding practices more effectively. To assess its impact, a one-group pretest-posttest design was conducted. The results of a paired sample t-test showed a significant improvement in secure coding knowledge (t = 19.50, p = 0.048), confirming the platform's effectiveness. In addition, the system's usability was rated highly, with a score of 0.93 on the Computer System Usability Questionnaire (CSUQ), classifying it as "Very Good." The practical implications of this research suggest that CodeGuardians could be implemented in both educational and professional settings to enhance secure coding skills and reduce software vulnerabilities. From a theoretical standpoint, this study advances cybersecurity education by integrating AI-driven feedback into gamified learning environments. The research supports the theory that gamification improves engagement and learning retention, while also highlighting the value of adaptive technologies in addressing real-world security challenges. Future work will examine the long-term retention of knowledge and scalability across diverse learning environments.
Co-Authors Azzahra, Fadel Kardian, Aqwam Rosadi Raden Budiarto Hadiprakoso Simanjuntak, Mirza Uliartha