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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 35 Documents
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Design of Mobile-Based Samosir Tour Hero Tourism Application Silitonga, Agnes Irene
International Journal of Information System and Innovative Technology Vol. 4 No. 1 (2025): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/stcja626

Abstract

Samosir Tour Hero is an innovative platform designed to help tourists who want to explore Samosir Island easily, integrated, and informatively. The application offers detailed information on vehicle rentals, accommodations, and tourist destinations, and serves as a direct link between tourists and experienced local tour guides. With advanced features such as searching for nearby tourist locations, customized tour packages, and user reviews and recommendations, Samosir Tour Hero provides a comprehensive solution to enhance the tourist experience. The application has significant potential to boost the local economy, support tour guide activities, and strengthen the tourism sector in Samosir. With a user-friendly approach, simple yet effective interface design, and integration of transportation, accommodation, and tour guide services, Samosir Tour Hero aims to create a more personalized, comfortable, and memorable travel experience.
Predicting Student Academic Performance Using Random Forest Regression: A Case Study on LMS Behavioral Data Saragih, Rijois I. E.
International Journal of Information System and Innovative Technology Vol. 4 No. 1 (2025): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/rm0wcg63

Abstract

In the evolving landscape of digital education, Learning Management Systems (LMS) have become pivotal in managing student engagement and academic resources. These platforms not only facilitate course delivery but also log extensive behavioral data, including attendance rates, quiz performances, LMS usage time, and forum activities. Leveraging this data, educators and institutions can enhance academic outcomes through predictive analytics. This study investigates the use of Random Forest Regression, a machine learning technique, to predict student final grades based on LMS behavioral data. A synthetic dataset comprising 100 student records was used, each containing features that reflect engagement and performance. The data underwent standard preprocessing procedures including normalization and partitioning into training and testing sets. The Random Forest model was trained and evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as performance metrics. The model achieved a MAE of 4.57 and RMSE of 5.90, indicating a high level of predictive accuracy. Feature importance analysis revealed that average quiz score and attendance rate were the most significant predictors, followed by LMS time and forum activity. These findings demonstrate the effectiveness of ensemble learning methods in educational settings and support the integration of predictive systems in LMS platforms for real-time academic monitoring. Such systems could provide early alerts for at-risk students and assist educators in designing targeted interventions. This research contributes to the field of Educational Data Mining by validating the practical utility of Random Forest Regression in supporting personalized and data-driven learning strategies.
The Potential of Blockchain Technology in the Financial Industry Rahmadani, Rahmadani; Silaban, Indra Marto
International Journal of Information System and Innovative Technology Vol. 4 No. 1 (2025): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/86jwrg39

Abstract

Blockchain technology has emerged as a revolutionary solution across various industries, with its potential being particularly recognised in the financial sector. This research aims to explore blockchain technology in the financial industry, focusing on its role in improving transparency, security, and efficiency in financial transactions. A decentralised data management technique, blockchain will eliminate intermediaries, reduce transaction costs, and provide a secure and immutable ledger to increase trust among financial stakeholders. This research will also highlight key areas where blockchain is transforming the financial system, such as cryptocurrencies, smart contracts, and decentralised finance (DeFi). The research also discusses the opportunities and challenges faced by the financial industry in adopting blockchain, including regulatory issues, scalability concerns, and integration with existing infrastructure. Through a comprehensive review of current trends, case studies, and theoretical frameworks, this research aims to offer insights into the future potential of blockchain technology on the financial industry, so that the long-term benefits of blockchain adoption can result in a more efficient, transparent, and secure global financial ecosystem.  
Toward Intelligent and Secure 5G-IIoT Networks: A Framework for AI-Driven Optimization and Blockchain-Based Security Silitonga, Joe Laksamana
International Journal of Information System and Innovative Technology Vol. 4 No. 1 (2025): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/4zfv5855

Abstract

The rapid evolution of 5G networks has unlocked new capabilities for the Industrial Internet of Things (IIoT), enabling ultra-reliable, low-latency, and scalable communication. However, challenges such as dynamic resource allocation, network congestion, and growing cybersecurity threats continue to limit the full potential of 5G-IIoT integration. Building upon previous literature, this paper proposes a novel architectural framework that combines artificial intelligence (AI) for intelligent network optimization and blockchain technology for decentralized, secure data management.The framework introduces a layered system comprising four integrated components: AI-driven resource management, a permissioned blockchain layer for secure authentication, an edge-cloud coordination module for low-latency computing, and a network slicing orchestrator for application-specific service differentiation. Each component is designed to address specific limitations in existing 5G-IIoT deployments by enhancing adaptability, reducing security risks, and optimizing performance.This paper outlines the architectural design, data flow, and interaction between components. It also defines the evaluation metrics and simulation setup for benchmarking the proposed framework against conventional 5G-IIoT systems. Preliminary findings and existing literature suggest that integrating AI and blockchain mechanisms can significantly improve latency, throughput, energy efficiency, and security resilience.This work aims to serve as a foundational blueprint for building intelligent, secure, and future-ready IIoT infrastructures powered by 5G. Future work will focus on simulation, real-world deployment, and integration with upcoming 6G paradigms.
Benchmarking AI-Based Intrusion Detection Models for Cyber-Physical Systems: A Dataset-Driven Analysis Simanjuntak, Tandhy
International Journal of Information System and Innovative Technology Vol. 4 No. 1 (2025): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/f5t5s523

Abstract

Cyber-Physical Systems (CPS) are increasingly deployed across sectors such as energy, manufacturing, and healthcare, where real-time monitoring and secure operations are essential. As these systems become targets for sophisticated cyber threats, the need for accurate, low-latency intrusion detection has become critical. While many studies have proposed AI-based solutions for securing CPS, there remains a lack of systematic benchmarking across diverse datasets and attack scenarios.This paper presents a dataset-driven benchmarking study of machine learning and deep learning-based Intrusion Detection Systems (IDS) tailored for CPS environments. Using publicly available CPS-related datasets—including UNSW-NB15, CICIDS2017, BATADAL, and the ICS-Cyber Attack Dataset—we evaluate the performance of Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory (LSTM) networks, and a proposed Hybrid AI model. Evaluation metrics include accuracy, precision, recall, F1-score, and false positive rate, providing a holistic view of each model's effectiveness. Results indicate that while traditional models like RF and SVM offer faster inference times, deep learning models such as LSTM consistently outperform in terms of detection accuracy and false positive reduction. The Hybrid AI model demonstrates a balanced trade-off between performance and efficiency, making it a promising approach for real-world CPS deployments. This benchmarking effort serves as a foundation for selecting and optimizing IDS solutions in CPS, highlighting the importance of aligning detection models with dataset characteristics and operational constraints.

Page 4 of 4 | Total Record : 35

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