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Rijois Iboy Erwin Saragih
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International Journal of Information System and Innovative Technology
Published by Geviva Edukasi Trans Teknologi
ISSN : -     EISSN : 29647207     DOI : https://doi.org/10.63322/ijisit
Core Subject : Science,
Computer Science & IT
IJISIT (International Journal of Information System and Innovative Technology) is a peer-reviewed journal in Applied Information Technology published twice a year in June and December and organized by the PT Geviva Edukasi Trans Teknologi. Focus & Scope International Journal of Information System & Innovative Technology aims to publish original research results on the implementation of the information systems. International Journal of Information System & Innovative Technology covers a broad range of research topics in information technology. The topics include but are not limited to avionics. 1. Artificial Intelligence and Soft Computing 2. Computer Science and Information Technology 3. Telecommunication System and Security 4. Digital Signal, Image and Video Processing 5. Automation, Instrumentation and Control Engineering 6. Internet of Things, Big Data and Cloud Computing
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 45 Documents
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Design of a Compact Multi-Band Circularly Polarized Antenna for Tracking and Localization Applications Shanmuka Rooban Gunasekaran; Nordiana Mohamad Saaid; Thennarasan Sabapathy; Muzammil Jusoh; Mohamed Nasrun Osman; Saidatul Norlyana Azemi; Siddarth Pichandi; Suresh Ponnan
International Journal of Information System and Innovative Technology Vol. 5 No. 1 (2026): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/mje6dj25

Abstract

This project is about designing a compact multi-band circularly polarized antenna for tracking and localization applications which is receiver type of antenna. Existing commercial GPS antenna uses 2 frequency bands which have linear polarization (LP), less robust to the future GPS receiver system. Thus, in this project, a multi-band circularly polarized antenna will be designed. The antenna is a multi-band type antenna since it is radiating at three GPS frequency bands. The antenna development starts with creating three different size of patch which is known as L1, L2 and L5. L5 (1.164GHz -1.189GHz) is the smallest frequency among those three, L2 (1.215GHz -1.240GHz) is the middle range frequency and the biggest frequency is L1 (1.563GHz -1.588GHz). The size of the antenna will be approximately 100mm by 50mm because it is a handheld receiver antenna hence it is required to be small in size. This antenna uses Rogers and FR-4 as the substrate and copper as the ground plane and patch of the radiating element. All the design and simulation results are conducted using CST Studio Suite 2016 software. Based on the result, it is shows that the antenna producing 3 different frequency band with all the return loss value is under -10 dB. It is also producing an omnidirectional radiation pattern with axial ratio less than 3dB. For polarization, the antenna is right hand circular polarization (RHCP) and producing a reasonable gain for GPS application.
Classification of Teacher Certification Eligibility Using the C4.5 Algorithm Agnes Irene Silitonga; Mismauli Nainggolan; Tasya Arcinta; Yoakim Simamora; Ferry Indra Sakti H Sinaga
International Journal of Information System and Innovative Technology Vol. 5 No. 1 (2026): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/2ar4tf74

Abstract

Determining teacher certification eligibility is a crucial process in improving the quality of education. The C4.5 algorithm is a decision tree-based machine learning algorithm. This algorithm offers a systematic approach to data analysis and provides accurate results for decision-making. This study aims to develop a predictive model using the C4.5 algorithm to assess teacher certification eligibility based on relevant data such as teaching experience, education, and competency exam results. This study reveals that the C4.5 algorithm is capable of producing transparent decision rules and enabling clear interpretation of the results. This research is expected to make a significant contribution to supporting a more objective and efficient teacher certification policy.
Ethical and Inclusive Adoption of Artificial Intelligence in Education: A Conceptual Review and Institutional Framework Rijois Saragih
International Journal of Information System and Innovative Technology Vol. 5 No. 1 (2026): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/qfyshr03

Abstract

The adoption of Artificial Intelligence (AI) in education has created significant opportunities for personalized learning, scalable educational services, and improved administrative efficiency. However, the increasing integration of AI technologies has also introduced ethical and inclusivity challenges, including data privacy concerns, algorithmic bias, unequal access to digital resources, and limited institutional readiness. This study presents a conceptual review of ethical and inclusive considerations in AI adoption within educational environments. Relevant literature published between 2019 and 2024 was analyzed through thematic synthesis to identify key dimensions influencing responsible AI implementation. The findings reveal four critical dimensions: ethical governance, inclusivity and accessibility, institutional readiness, and human–AI collaboration. Based on these dimensions, an institutional framework is proposed to guide educational institutions in implementing AI technologies responsibly while promoting equitable and inclusive learning environments. The framework emphasizes organizational governance, policy alignment, capacity building, and human-centered practices rather than technical system optimization. The proposed framework may serve as a practical reference for educational leaders, policymakers, and practitioners seeking sustainable and responsible AI adoption in education.
Hybrid Explainable Intrusion Detection Framework for Cyber-Physical Systems Using Random Forest and Long Short-Term Memory Networks Thandy Simanjuntak; Rijois Iboy Erwin Saragih
International Journal of Information System and Innovative Technology Vol. 5 No. 1 (2026): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/3tgpdy72

Abstract

Cyber-Physical Systems (CPS) connect computational processes with physical operations and are increasingly used in industrial control, energy management, healthcare, and transportation. This connectivity improves automation and monitoring, but it also creates security risks because attacks on CPS may affect both digital assets and physical processes. Existing intrusion detection approaches based on machine learning and deep learning have shown promising performance, yet many of them provide limited explanation for their decisions. This limitation reduces trust, especially in critical infrastructure environments where security decisions must be understandable. This study proposes a Hybrid Explainable Intrusion Detection System (HX-IDS) that combines Random Forest, Long Short-Term Memory (LSTM), SHAP, and LIME. Random Forest is applied to identify important features, while LSTM learns temporal attack behavior from CPS traffic. SHAP and LIME are used to explain model predictions at global and local levels. The proposed framework is evaluated using benchmark CPS-related datasets. The results show that HX-IDS improves detection performance, reduces false alarms, and provides clearer explanations for security analysts. This study contributes to the development of more transparent and trustworthy AI-based intrusion detection for CPS security.
Explainable Blockchain-Enabled Intrusion Detection Framework for Secure and Trustworthy 5G-IIoT Networks Joe Silitonga; Rijois Iboy Erwin Saragih
International Journal of Information System and Innovative Technology Vol. 5 No. 1 (2026): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/1vyght40

Abstract

The integration of 5G networks and the Industrial Internet of Things (IIoT) enables real-time industrial automation but also expands the cybersecurity attack surface. Although previous studies have proposed AI and blockchain-based security frameworks, intrusion detection in 5G-IIoT remains limited by black-box AI models, low interpretability, and blockchain mechanisms that mainly support logging rather than attack detection. This study proposes an Explainable Blockchain-Enabled Intrusion Detection System (XB-IDS) for secure 5G-IIoT networks. The framework integrates deep learning-based intrusion detection, SHAP-based explainability, and blockchain-enabled security logging with smart contracts. A hybrid CNN-LSTM model is used to detect spatial and temporal attack patterns, while SHAP provides interpretable explanations for security analysts. Public IIoT cybersecurity datasets such as TON_IoT, Edge-IIoTset, and CICIoT2023 are used for evaluation. The proposed framework is assessed using accuracy, precision, recall, F1-score, false positive rate, detection latency, throughput, and explainability analysis. The proposed XB-IDS aims to improve detection performance, transparency, and trustworthiness in 5G-IIoT security operations. This study contributes an experimentally evaluable framework that extends prior AI-blockchain security research toward explainable and accountable intrusion detection.

Page 5 of 5 | Total Record : 45

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