cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Muhammad Wali
Contact Email
muhammadwali@amikindonesia.ac.id
Phone
+6285277777449
Journal Mail Official
ijsecs@lembagakita.org
Editorial Address
Jl. Teuku Nyak Arief No. 7b 23112, Kota Banda Aceh, Banda Aceh, Provinsi Aceh
Location
,
INDONESIA
International Journal Software Engineering and Computer Science (IJSECS)
Published by Lembaga Komunitas Informasi Teknologi Aceh
ISSN : 27764869     EISSN : 27763242     DOI : https://doi.org/10.35870/ijsecs
Core Subject : Science,
Computer Science & IT
IJSECS is committed to bridge the theory and practice of information technology and computer science. From innovative ideas to specific algorithms and full system implementations, IJSECS publishes original, peer-reviewed, and high quality articles in the areas of information technology and computer science. IJSECS is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information technology and computer science applications..
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 284 Documents
 21   22   23   24   25 
Log-Based Code Maniac E-Learning Web Development Model Utilizing Adaptive Web Development Techniques Hidayatullah, Reko Syarif
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4274

Abstract

Education is a fundamental requirement for human civilization, particularly for children and adolescents. The recent pandemic has compelled the education sector to adopt online learning alternatives. Codemaniac is an e-learning tool developed with gamification techniques to enhance student motivation. However, Codemaniac still lacks adaptive features that optimize user engagement based on individual behaviors. To address this limitation, further development will incorporate adaptive features by utilizing recorded user behavior from log files. This behavioral data will be clustered using the fuzzy c-means algorithm, resulting in three distinct user groups, each receiving a tailored user interface. The system is developed following the SDLC waterfall model, with Python used for clustering implementation. The development process involves three user roles, five additional functional requirements, and one non-functional requirement. System testing employs white-box methods for unit testing and black-box methods for validation.
Implementation of Augmented Reality as an Interactive Medium for Firearms Education Darman, Muhammad Nazar; Asriningtias, Yuli
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4285

Abstract

Augmented Reality (AR) has been utilized as an interactive educational tool to support safer and more effective learning about various firearms. The application was created with Unity3D and C#, and its functionality was evaluated through black box testing. Findings show that the application operates successfully on targeted devices, though some performance issues were observed, including extended loading times and occasional lag during navigation. AR visual and interactive features enable users to explore firearm components and operational procedures without exposure to real-world risks, as the use of physical firearms is not required. The inclusion of offline access further enables users to engage with the learning materials at their convenience. AR demonstrates considerable promise for improving the quality of firearm training and may be further adopted in technical instruction, military education, and the broader development of digital learning environments.
Hybrid Quantum-Classical Optimization for Energy-Efficient Large Language Models Judijanto, Loso; Yuswardi, Yuswardi; Fitriyani, Fitriyani
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.5099

Abstract

The rapid evolution of Large Language Models (LLMs) has transformed natural language processing, enabling sophisticated applications across various sectors. However, the substantial computational demands associated with training and deploying LLMs result in significant energy consumption and carbon emissions. This study introduces an optimized hybrid quantum-classical framework that integrates variational quantum algorithms (VQAs) with accelerated classical learning techniques. By harnessing quantum computing for complex non-linear optimization and employing prompt learning to minimize full model retraining, the proposed approach enhances both computational efficiency and sustainability. Simulation outcomes indicate that the hybrid method can reduce energy usage by up to 30% and shorten computation time by 25% relative to conventional classical approaches, without diminishing model accuracy. These improvements are substantiated through quantitative analysis and visualized energy metrics. The adaptability of the framework supports its application in diverse areas, including sustainable energy management, supply chain optimization, and environmentally conscious transportation systems. Nevertheless, the broader implementation of such hybrid solutions remains constrained by current quantum hardware capabilities and integration challenges with classical infrastructure. The findings underscore the potential of hybrid quantum-classical optimization as a pathway toward sustainable AI development. Future research should prioritize advancements in quantum hardware reliability and interdisciplinary collaboration to accelerate practical adoption, thereby supporting global efforts in energy efficiency and environmental responsibility.
Social Media Sentiment Analysis of Twitter Regarding People's Housing Savings (TAPERA) Using Naïve Bayes Dewy, Avry Liyanah; Kamayani, Mia
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4126

Abstract

The advancement of technology has transformed how people interact and express opinions on social media platforms. This research examines Twitter conversations regarding Indonesia's government-initiated Housing Savings Program (TAPERA) through sentiment analysis. The study employed Naïve Bayes classification methodology, with data acquisition conducted via Google Colab platform utilizing the tweet-harvest library. The collection process yielded 1,800 tweets matching predetermined search parameters. Data underwent rigorous preprocessing, including text cleaning and manual sentiment annotation to establish reliable training datasets. Examination of 720 test tweets revealed 473 (65.69%) expressed negative sentiment while 247 (34.31%) conveyed positive sentiment toward the program. The implemented Naïve Bayes model achieved 84.17% accuracy, with negative class precision at 88.71% and recall at 88.60%, while positive class precision reached 78.54% with 76.08% recall. Results indicate the Naïve Bayes approach effectively categorizes public sentiment regarding the TAPERA program, offering valuable feedback for stakeholders responsible for program assessment and enhancement.
Implementation of Zero-Knowledge Encryption in a Web-Based Password Manager Darmawan, R. Krisviarno; Cahyono, Ariya Dwika
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4207

Abstract

-The secure management of account credentials presents a considerable challenge in the digital era, as many users continue to engage in unsafe practices such as password reuse. Conventional password managers typically store encrypted data on servers, which introduces risks if those servers are compromised. This study develops a web-based password manager that implements Zero-Knowledge Encryption (ZKE), ensuring that all essential cryptographic operations are executed exclusively on the client side (browser). Employing a client-server architecture (React frontend, Python/FastAPI backend), the system derives encryption keys from the user’s master password using Argon2id (4 iterations, 64 MB memory, 1 parallelism), and performs credential data encryption and decryption with AES-GCM entirely on the client side. The server is limited to receiving and storing encrypted data (verifier, salt, data blobs), without ever accessing the master password or plaintext credentials. Network payload analysis conducted with Chrome DevTools confirms that the ZKE implementation effectively prevents the exposure of sensitive data to the server. This approach substantially improves data privacy and security against server-side threats. Nevertheless, the ZKE model lacks an account recovery feature, placing full responsibility on users to protect their master passwords—a trade-off that underscores the need for further investigation into ZKE-compatible recovery mechanisms.
Implementation of the FCFS (First Come, First Served) Method to Resolve Customer Queuing Issues at Lentera Grill Restaurant: (Case Study: Web-Based Reservation Information System at Lentera Grill Restaurant) Prabandaru, Lila; Suharyadi, Suharyadi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4209

Abstract

Lentera Grill Restaurant faces long customer queues due to manual ordering processes, particularly during peak hours. Our research aimed to reduce wait times and improve service accuracy through technology. We designed and built a web-based reservation system using Python, React, and MongoDB that applies the FCFS (First Come First Served) method to process orders in sequence of arrival. Testing involved two phases: functional validation through Blackbox Testing of all user pathways, and performance assessment using JMeter with 20, 50, and 100 simultaneous users. Results showed the system maintained stable throughput with zero error rates across all load scenarios, though we observed latency spikes during heavy traffic that require attention. The FCFS implementation reduced average wait times by 37% compared to the previous manual system and increased customer satisfaction ratings in post-implementation surveys. Restaurants with similar queue management challenges would benefit from adopting such technology-based solutions that balance customer experience with operational efficiency
Apriori Algorithm Analysis of Mattress Material Usage Data for Enhanced Production Optimization Suwaryo, Niko; Santoso, Santoso; Masgo, Masgo; Tugiman, Tugiman; Wijoyo, Sandy Gunarso; Nugraha, Nugraha
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4362

Abstract

Production is a value-adding process that transforms raw materials into finished products to meet manufacturing requirements. Association rule analysis serves as a methodological approach to identify relationships between items, particularly in transactional datasets. This analytical method has proven effective in processing exchange data patterns. Analysis of production material usage patterns revealed that when items A and B are utilized, there exists a 50% probability of concurrent item C usage - a significant pattern emerging from transactional data analysis. The study generated association rules for each operational process. Empirical testing through RapidMiner Studio yielded consistent results, demonstrating linear relationships proportional to the modeled scenarios, thereby validating the model's applicability as a decision-making reference. The evaluation of generated association rules through RapidMiner Studio revealed a Lift Ratio value of 1. These results indicate that combinations meeting or exceeding a Lift Ratio threshold of 1 demonstrate statistical validity and practical utility.
Opportunities and Challenges of Artificial Intelligence in Digital Forensics Syifaurachman, Syifaurachman
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4371

Abstract

Digital forensics research remains constrained, while the rapidly evolving digital landscape renders traditional forensic methodologies increasingly inadequate for modern investigative challenges. This work conducts a systematic literature review and bibliometric analysis of computer forensics, specifically targeting digital forensics applications. The study employed a systematic literature evaluation of the Scopus database using "Computer Forensic" as the search term within article titles, abstracts, and keywords. The initial search retrieved 3,222 publications, subsequently refined to 120 academic articles through PRISMA methodology with inclusion criteria encompassing computer science subject areas, final journal articles, English language publications, and open access availability. Three research questions guide this investigation: examining future digital forensic research directions, analyzing current research methodologies, and identifying practical and theoretical implications. Data collection occurred on May 21, 2025, with analysis performed using VOS Viewer bibliometric software. Results reveal that digital forensics research predominantly originates from industrialized nations, particularly the United States and Europe, accounting for 49 of 120 examined articles (40.83%), while Asian and African contributions remain substantially underrepresented. The analysis identified a four-stage digital forensics implementation framework: identification, collection, analysis, and preservation. Furthermore, the investigation examined artificial intelligence applications in digital forensics, particularly NLP-based approaches and machine learning algorithms including CNN models for forensic processes. While AI has revolutionized digital forensics by enhancing accuracy, efficiency, and investigative effectiveness, the analysis reveals persistent challenges including algorithmic bias, data privacy concerns, and decision-making transparency issues. Future research should incorporate additional databases such as Web of Science to enhance data quality and scope. The integration of AI and machine learning models across digital forensics stages promises to deliver more precise and thorough investigative outcomes.
Validating and Detecting User-Specific Code Clones: An AI Framework Leveraging Metric-Based Feature Vectors Praveen, Asfa
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4431

Abstract

Like other verification aspects, code clone validation remains highly subjective and user-dependent. This research presents an AI-based approach utilizing fragment-specific metric-based feature vectors to identify and validate customized code clones. We derive classification feature vectors through appropriate code metrics, training various machine learning models for identifier classification. The resulting framework enables users to submit code clone pairs for cloud-based validation. Upon submission, the trained AI model analyzes pairs using their metric features, generating user-specific validation scores returned via a RESTful API. We describe the framework architecture encompassing metric extraction, model training, and cloud deployment. Experimental results demonstrate the framework's ability to adapt effectively to individual validation strategies, optimizing accuracy while significantly reducing inspection effort compared to non-customized clone detection systems. A prototype system demonstrates the feasibility of providing automatically computed AI-based validation scores integrated with existing validation tools.
Risk Management Evaluation Based on ISO/IEC 27005 Framework: A Case Study of ABC Company IT Workshop Room Kurniawan, Muhammad Ferdi; Salma, Triana Dewi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4549

Abstract

ABC Company operates as a technology firm based in France, maintaining its research and development operations in Jakarta. The company produces digital security technologies—biometrics, facial recognition systems, and digital identity solutions—alongside telecommunications and payment products including SIM cards, banking cards, and smart cards. Given how much the company relies on technology and secure information handling, it needs strong systems and infrastructure, especially when dealing with sensitive data. Yet no one has conducted a risk management assessment of the IT workshop room. Several problems have emerged with the physical security of this important area, such as people misusing access privileges and assets going missing. This research evaluates how the company manages information security risks by first identifying what's causing these problems through a fishbone diagram that looks at people, technology, and processes. We then assessed risks using the ISO/IEC 27005:2018 standard across 12 assets, examining threats, current controls, weak points, and what treatments are needed. Our analysis shows three assets (A5, A6, A7) carry high risk, three others (A4, A9, A12) have medium risk, and six assets (A1, A2, A3, A8, A10, A11) present low risk. Using these results, we developed specific recommendations for handling risks associated with each asset to improve information security throughout the company.

Page 23 of 29 | Total Record : 284

 21   22   23   24   25 

Filter by Year

2021 2025


Reset
Filter By Issues
All Issue Vol. 5 No. 3 (2025): DECEMBER 2025 Vol. 5 No. 2 (2025): AUGUST 2025 Vol. 5 No. 1 (2025): APRIL 2025 Vol. 4 No. 3 (2024): DECEMBER 2024 Vol. 4 No. 2 (2024): AUGUST 2024 Vol. 4 No. 1 (2024): APRIL 2024 Vol. 3 No. 3 (2023): DECEMBER 2023 Vol. 3 No. 2 (2023): AUGUST 2023 Vol. 3 No. 1 (2023): APRIL 2023 Vol. 2 No. 2 (2022): OCTOBER 2022 Vol. 2 No. 1 (2022): MAY 2022 Vol. 1 No. 2 (2021): AUGUST 2021 Vol. 1 No. 1 (2021): APRIL 2021 More Issue