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TechComp Innovations: Journal of Computer Science and Technology
Published by Yayasan Mabadi Iqtishad Al Islami
ISSN : 30628903     EISSN : 3062889X     DOI : https://doi.org/10.70063
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management
TechComp Innovations: Journal of Computer Science and Technology is a premier scholarly publication dedicated to advancing knowledge and understanding in the rapidly evolving field of computer science and technology. The journal serves as a platform for researchers, academics, engineers, and practitioners to disseminate cutting-edge research findings, innovative technologies, and practical applications in various areas of computer science and technology. With a focus on fostering interdisciplinary collaboration and promoting excellence in research and development, TechComp Innovations covers a wide spectrum of topics, including but not limited to artificial intelligence, machine learning, computer vision, cybersecurity, data science, cloud computing, software engineering, and Internet of Things (IoT). Each issue of TechComp Innovations features high-quality, peer-reviewed articles that present original research contributions, theoretical insights, experimental studies, and practical implementations. The journal strives to publish research that pushes the boundaries of knowledge in computer science and technology, addresses significant challenges, and contributes to the advancement of the field. By providing a platform for scholarly discourse and knowledge exchange, TechComp Innovations aims to foster innovation, inspire collaboration, and drive technological progress in both academia and industry. TechComp Innovations welcomes submissions from researchers and practitioners worldwide, offering a forum for the dissemination of new ideas, methodologies, and technologies. Through its publication, the journal aims to facilitate dialogue, stimulate critical thinking, and promote the adoption of cutting-edge solutions to address real-world problems. Ultimately, TechComp Innovations endeavors to be a leading resource for researchers, professionals, and students interested in the latest developments and trends in computer science and technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Interaksi Komputer-Manusia
Articles 25 Documents
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Machine Learning-Enabled Digital Twin Framework for Predictive Intelligence in Smart Mechanical Systems Juvinal Ximenes Guterres; Bhadrappa Haralayya; Varinder Singh Rana
TechComp Innovations: Journal of Computer Science and Technology Vol. 3 No. 1 (2026): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v3i1.182

Abstract

This study investigates the integration of digital twin technology and machine learning for predictive analysis in smart mechanical systems. The research emphasizes the role of intelligent computational frameworks in improving industrial monitoring, predictive maintenance, and operational efficiency within Industry 4.0 environments. A qualitative content analysis approach was employed by reviewing scientific literature, industrial reports, and previous studies related to digital twins, artificial intelligence, and predictive analytics. The findings indicate that digital twin architectures supported by machine learning algorithms can significantly enhance real-time monitoring, fault prediction accuracy, and maintenance optimization. The integration of IoT devices, cloud computing, and intelligent analytics also improves industrial sustainability, reduces operational downtime, and supports data-driven decision-making processes. Furthermore, the study identifies several technological challenges, including cybersecurity risks, data integration complexity, and computational limitations. Overall, the proposed intelligent digital twin framework provides a promising approach for future industrial innovation and sustainable smart mechanical system management
AI-Driven Cybersecurity Threat Detection Framework for Next-Generation Network Environments Ravi Kumar Saidala; Amirkhan Pashayev; Tofig Hasanov
TechComp Innovations: Journal of Computer Science and Technology Vol. 3 No. 1 (2026): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v3i1.184

Abstract

This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems
Machine Learning Approaches for Detecting Political Disinformation in Social Media Ecosystems Ahmad Nur Ihsan Purwanto; Nur Hazwani Dzulkefly; Umna Iftikhar
TechComp Innovations: Journal of Computer Science and Technology Vol. 3 No. 1 (2026): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v3i1.190

Abstract

Political disinformation has become one of the most critical challenges in contemporary digital democracies due to the rapid expansion of social media ecosystems. This study investigates the effectiveness of machine learning approaches in detecting political disinformation across online platforms such as Twitter, Facebook, and political discussion forums. Using a qualitative research design with a content analysis approach, the study examines linguistic manipulation, emotional narratives, sentiment polarity, and behavioral communication patterns embedded in misleading political content. The findings indicate that deep learning models, particularly Long Short-Term Memory (LSTM) architectures, demonstrate superior performance in identifying contextual and semantic inconsistencies compared to traditional machine learning algorithms. The study also reveals that algorithmic amplification, echo chambers, and coordinated bot activities significantly contribute to the rapid spread of political misinformation. Furthermore, the research highlights the importance of ethical artificial intelligence governance, transparency, and digital literacy in strengthening democratic resilience and protecting information integrity within digital communication environments
TraceRoot: A Blockchain-Based Traceability Framework for Enhancing Transparency and Trust in Rice and Essential Goods Supply Chain Md. Safaet Hossain; Mohammad Shakibul Hasan Sakib; Md. Rayhan Ahmed Shis; Sakib Ahmed; Md. Fudail
TechComp Innovations: Journal of Computer Science and Technology Vol. 3 No. 1 (2026): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v3i1.192

Abstract

Modern food supply chains, particularly those involving essential commodities like rice, often suffer from major challenges such as product fraud, inefficient record-keeping, and a lack of consumer trust. Traditional centralized systems are prone to data tampering, limited transparency, and poor traceability, making it difficult to verify the authenticity and origin of goods. To address these issues, our research introduces TraceRoot, a blockchain-based traceability framework designed to enhance transparency, accountability, and trust in agricultural supply chains.TraceRoot leverages the immutability and decentralization of blockchain technology to maintain a secure, distributed ledger that records every transaction and movement of goods across the supply chain. Each stakeholder including farmers, distributors, retailers, and consumers has role-based access to authenticated data through a user-friendly interface. The framework integrates smart contracts to automate transactions and digital signatures to verify the integrity of the data being uploaded, minimizing the risk of human error or manipulation
IoT-Enabled Smart Plant Ecosystem Integrating Real-Time Monitoring, Automated Irrigation, and Intelligent Growth Optimization Frameworks Md. Safaet Hossain; Israt Jahan; Jawata Afnan; Israt Sultana Tanny; Nashid Sultana Mim; Kayes Mahmood
TechComp Innovations: Journal of Computer Science and Technology Vol. 3 No. 1 (2026): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v3i1.198

Abstract

Urban plant care is increasingly important for sustainable living, but many users face inconsistent watering, insufficient care knowledge, unsuitable plant selection and delayed disease recognition. This study presents Easy Grow Plants, an integrated web and Internet of Things (IoT) ecosystem that connects plant care guidance, soil-moisture monitoring, automated watering, plant recommendation, image-based plant health assistance, marketplace functions, community interaction and plant exchange. The prototype was implemented using a React frontend, Django REST backend, SQLite database and an Arduino UNO R4 WiFi smart pot with a soil moisture sensor, relay module and DC water pump. Functional, interface, API, IoT connectivity, sensor calibration, watering control and LAN deployment tests were conducted. The results show that the core modules operated together as an integrated academic prototype. The system demonstrates a practical foundation for smart urban gardening, although cloud deployment, multi-device testing and stronger AI validation remain future improvements

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