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Journal Innovation in Information and Computer Technology
Published by PT. Altaf Publishing Corp
ISSN : -     EISSN : 30640245     DOI : https://doi.org/10.70895/jictech
Core Subject :
Journal Innovation in Information and Computer Technology (JICTECH) provides a forum for publishing the original research articles from contributors related to embedded system and control, processor and IC design, network and infrastructure, and computing algorithms.
Arjuna Subject : -
Articles 29 Documents
 1   2   3 
Hybrid Deep Learning Model for Early Prediction of Student Academic Risk Mahmud; Anggoro Aryo Pramuditho; Shahnawaz Ahmad
Journal Innovation in Information and Computer Technology Vol. 3 No. 2 (2026): (May) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v3i2.114

Abstract

The increasing reliance on digital network infrastructures in higher education institutions has significantly increased the exposure of campus networks to various cyber threats. Cyberattacks such as distributed denial-of-service (DDoS), malware infections, and unauthorized access attempts can disrupt academic services and compromise sensitive institutional data. Therefore, developing an effective intrusion detection mechanism is essential to enhance cybersecurity within campus network environments. This study proposes a machine learning framework for cyberattack detection in campus networks by analyzing network traffic patterns using supervised machine learning algorithms. The proposed framework consists of several stages including dataset acquisition, data preprocessing, feature selection, model training, and performance evaluation. Experiments were conducted using the CICIDS2017 dataset, which contains both benign network traffic and multiple types of cyberattack scenarios. Three machine learning models, namely Random Forest, Support Vector Machine (SVM), and XGBoost, were implemented and compared in order to evaluate their effectiveness in detecting malicious network activities. The experimental results indicate that the XGBoost model achieved the highest performance, with an accuracy of 95.3%, outperforming the other evaluated models. These findings demonstrate that machine learning techniques can effectively identify abnormal network traffic patterns and improve cyberattack detection capabilities. The proposed framework provides a promising approach for strengthening cybersecurity monitoring and enhancing the resilience of campus network infrastructures against evolving cyber threats.
Large Language Model-Based Intelligent Tutoring System for Programming Education Herli Cecilia; Muhammad Ridho Ardiansyah
Journal Innovation in Information and Computer Technology Vol. 2 No. 1 (2025): (January) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i1.115

Abstract

The rapid advancement of artificial intelligence, particularly large language models (LLMs), has significantly transformed the landscape of digital learning environments. In programming education, students often face difficulties such as limited instructor availability, delayed feedback, and insufficient personalized guidance during the learning process. Intelligent Tutoring Systems (ITS) have been widely proposed as a solution to provide adaptive and individualized learning support. However, traditional ITS architectures often rely on predefined rule-based models that limit their scalability and contextual understanding. This study proposes a large language model-based intelligent tutoring system designed to enhance programming education through adaptive learning support, automated feedback, and natural language interaction between students and the system. The proposed framework integrates LLM capabilities with a tutoring architecture that supports real-time code explanation, debugging assistance, and concept clarification tailored to individual learner needs. The system leverages prompt engineering and retrieval mechanisms to improve response relevance and pedagogical effectiveness. The results demonstrate that integrating LLM technologies into tutoring systems can improve students’ learning engagement, programming performance, and problem-solving abilities. Furthermore, the proposed approach enables scalable educational assistance that can support learners in environments with limited teaching resources. The findings suggest that LLM-based tutoring systems have strong potential to become an effective solution for personalized programming education in modern digital learning ecosystems.
Machine Learning-Based Learning Analytics for Predicting Student Engagement in Online Learning Deva Rahma aulia; Amelia Anggraini; Irwansyah
Journal Innovation in Information and Computer Technology Vol. 2 No. 1 (2025): (January) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i1.116

Abstract

The rapid adoption of online learning environments has generated massive amounts of educational data through Learning Management Systems (LMS). However, one of the major challenges in online education is maintaining and predicting student engagement, which significantly affects learning outcomes and course completion rates. This study proposes a machine learning-based learning analytics approach to predict student engagement in online learning environments. Data are collected from student interaction logs within a Learning Management System, including activity frequency, time spent on learning materials, forum participation, and assessment performance. Several machine learning algorithms, including Random Forest, Support Vector Machine (SVM), and Logistic Regression, are utilized to develop predictive models. The experimental results demonstrate that machine learning models can effectively predict student engagement levels and identify students at risk of disengagement. The findings provide insights for educators and institutions to design adaptive interventions and improve the quality of online learning environments. This research contributes to the growing field of learning analytics by demonstrating how machine learning techniques can enhance engagement prediction and support data-driven decision making in education.
Hybrid Collaborative Filtering Model for Personalized Digital Content Recommendation Systems Bella Paramitha; Indah Rahma Sari
Journal Innovation in Information and Computer Technology Vol. 2 No. 1 (2025): (January) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i1.117

Abstract

The increasing reliance on digital network infrastructures in higher education institutions has significantly increased the exposure of campus networks to various cyber threats. Cyberattacks such as distributed denial-of-service (DDoS), malware infections, and unauthorized access attempts can disrupt academic services and compromise sensitive institutional data. Therefore, developing an effective intrusion detection mechanism is essential to enhance cybersecurity within campus network environments. This study proposes a machine learning framework for cyberattack detection in campus networks by analyzing network traffic patterns using supervised machine learning algorithms. The proposed framework consists of several stages including dataset acquisition, data preprocessing, feature selection, model training, and performance evaluation. Experiments were conducted using the CICIDS2017 dataset, which contains both benign network traffic and multiple types of cyberattack scenarios. Three machine learning models, namely Random Forest, Support Vector Machine (SVM), and XGBoost, were implemented and compared in order to evaluate their effectiveness in detecting malicious network activities. The experimental results indicate that the XGBoost model achieved the highest performance, with an accuracy of 95.3%, outperforming the other evaluated models. These findings demonstrate that machine learning techniques can effectively identify abnormal network traffic patterns and improve cyberattack detection capabilities. The proposed framework provides a promising approach for strengthening cybersecurity monitoring and enhancing the resilience of campus network infrastructures against evolving cyber threats.
Generative AI-Based Adaptive Learning Model for Personalized Higher Education Dimas Ardhana; Muhammad Ridho Ardiansyah; Yuliza Aryani
Journal Innovation in Information and Computer Technology Vol. 2 No. 1 (2025): (January) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i1.121

Abstract

The rapid development of Artificial Intelligence (AI) technologies has significantly transformed the landscape of higher education, particularly in the context of personalized learning. Traditional learning systems often apply uniform instructional methods that fail to address individual differences in students' learning pace, preferences, and cognitive abilities. Consequently, there is a growing need for adaptive learning systems capable of providing personalized educational experiences. Generative Artificial Intelligence (Generative AI) has emerged as a promising technology that can dynamically generate educational content, feedback, and learning pathways tailored to individual learners. This study proposes a Generative AI-Based Adaptive Learning Model designed to support personalized learning in higher education environments. The model integrates machine learning algorithms, learning analytics, and generative AI techniques to analyze student learning behavior and automatically generate adaptive learning materials and recommendations. The research adopts a design science research methodology (DSRM) to develop and evaluate the proposed model through conceptual design and system architecture analysis. The results indicate that integrating generative AI into adaptive learning systems can enhance learning personalization, improve student engagement, and support instructors in delivering more efficient and scalable educational experiences. Furthermore, the proposed model provides a flexible framework that can be implemented in various digital learning platforms and learning management systems. The findings of this study contribute to the development of intelligent learning environments that leverage generative AI to improve the quality of higher education in the digital era.
Deep Learning Approach for Anomaly Detection in Digital Financial Transactions Mahmud; Akhmad Sayuti
Journal Innovation in Information and Computer Technology Vol. 2 No. 1 (2025): (January) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i1.122

Abstract

The rapid growth of digital financial services has significantly increased the volume and complexity of financial transactions, creating new challenges in maintaining transaction security and preventing fraudulent activities. Anomaly detection has become an essential mechanism for identifying suspicious transaction patterns that may indicate potential fraud within digital financial systems. This study aims to develop a deep learning–based approach for detecting anomalies in digital financial transactions by analyzing behavioral patterns within transaction datasets. The proposed method utilizes a deep neural network architecture to analyze multiple transaction attributes such as transaction amount, transaction time, user identification, merchant category, and device information. Prior to model development, transaction data undergoes several preprocessing stages including data cleaning, data transformation, and feature normalization to ensure data quality and compatibility with deep learning algorithms. The dataset is divided into training and testing subsets to evaluate the effectiveness of the proposed model in identifying anomalous transactions. Experimental results demonstrate that the deep learning model achieves strong performance in anomaly detection tasks. The proposed model obtained an accuracy of 96.8%, precision of 94.5%, recall of 92.7%, and an F1-score of 93.6%. In addition, the ROC-AUC value of 0.97 indicates that the model is highly effective in distinguishing between normal and anomalous transactions. These results show that deep learning techniques are capable of capturing complex transaction patterns and improving the reliability of fraud detection systems in digital financial environments. The findings of this study suggest that the integration of deep learning–based anomaly detection models into financial monitoring systems can significantly enhance transaction security and assist financial institutions in detecting suspicious activities more efficiently. As digital financial ecosystems continue to evolve, intelligent anomaly detection systems will play an important role in strengthening financial security and supporting the development of secure digital financial services.
Design of Augmented Reality Learning Media with AI Chatbot Support for Computer Network Learning Muhammad Ridho Ardiansyah; Andri Saputra; Md. Ruhul Amin
Journal Innovation in Information and Computer Technology Vol. 3 No. 2 (2026): (May) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v3i2.123

Abstract

Computer network learning often involves abstract concepts, invisible data flows, and physical devices that are not always available in the classroom. This condition can make it difficult for beginner students to understand network devices, topology structures, and basic communication processes. This study aims to design augmented reality learning media with AI chatbot support for computer network learning. The research used a research and development approach adapted from the Multimedia Development Life Cycle (MDLC), consisting of concept, design, material collecting, assembly, testing, and distribution stages. The developed media integrates AR visualization, learning materials, and chatbot-based assistance in one learning environment. The AR feature presents three-dimensional models of network devices and topology structures, while the AI chatbot provides simple explanations and guidance related to computer network concepts. Functional testing was conducted to ensure that the main features operated according to the expected results, including the main menu, learning material page, AR object display, topology visualization, AI chatbot interaction, instruction menu, and navigation buttons. User response evaluation was also conducted using a Likert-scale questionnaire involving 20 students. The results showed that all main features worked properly, and the user response evaluation obtained an average score of 85.2%, categorized as Very Good. These findings indicate that the developed media is feasible as a supporting tool for computer network learning. The integration of AR visualization and AI chatbot support can help students learn more independently, understand network concepts more clearly, and receive immediate learning assistance. Future development may focus on improving AR object optimization, expanding chatbot knowledge, and testing the media’s effectiveness on students’ learning outcomes.
Machine Learning Model for Malware Attack Prediction in Computer Network Systems Muhammad Ammar; Indah Rahma Sari
Journal Innovation in Information and Computer Technology Vol. 2 No. 2 (2025): (May) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i2.124

Abstract

The rapid development of information technology has significantly increased the complexity of computer network infrastructures. Along with these developments, cyber threats such as malware attacks have also increased in frequency and sophistication. Malware attacks can cause serious damage to computer systems, including data breaches, service disruption, and financial losses. Therefore, early detection and prediction mechanisms are crucial to enhance network security systems. Machine Learning has emerged as an effective approach for detecting and predicting cyber threats by analyzing large-scale network traffic data and identifying abnormal patterns. This study aims to develop a machine learning-based model for predicting malware attacks in computer network systems. Several machine learning algorithms such as Random Forest, Support Vector Machine, and Decision Tree are evaluated to determine the most effective model for malware prediction. The proposed model analyzes network traffic features and classifies them into normal or malicious behavior using supervised learning techniques. The experimental results demonstrate that machine learning models can significantly improve the accuracy of malware attack prediction and provide an efficient mechanism for proactive network security defense. This research contributes to the development of intelligent cybersecurity systems capable of detecting and predicting malware threats in modern computer networks.
Design of a Web-based Production and Sales Management Information System for the Small Business Convection Industry Alfabian Akbar; Dimas Ardhana
Journal Innovation in Information and Computer Technology Vol. 2 No. 2 (2025): (May) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i2.125

Abstract

This research aims to develop a web-based information system to overcome production and sales management problems in the small-scale convection industry. The main challenge identified is the lack of operational efficiency due to manual record-keeping, which frequently leads to data discrepancies in stock management, order tracking, and production scheduling. To address these issues, this study designs a centralized management information system featuring raw material inventory control, customer order processing, production monitoring, and automated sales reporting. The system was developed using the Waterfall methodology, ensuring a structured development lifecycle from requirement analysis to functional implementation. The results demonstrate a significant increase in operational efficiency through real-time stock monitoring and accelerated order processing. Functional validation using the Black-box testing method confirms that all system features operate correctly and align with the business requirements. This digital transformation provides a scalable solution for convection SMEs to minimize human error and enhance data-driven decision-making in a competitive market environment.
Implementation of Decision Tree C4.5 and Naïve Bayes for Data Mining-Based Chronic Kidney Disease Prediction Amali Amali; Candra Naya
Journal Innovation in Information and Computer Technology Vol. 2 No. 3 (2025): (September) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i3.126

Abstract

Chronic Kidney Disease (CKD) is a serious health problem with increasing prevalence worldwide. Early detection is important to prevent severe kidney failure and reduce healthcare costs. This study aims to implement and compare the performance of Decision Tree C4.5 and Naïve Bayes algorithms for CKD prediction using a dataset from the UCI Machine Learning Repository containing 400 patient records and 24 clinical attributes. Data preprocessing included handling missing values, transforming categorical data into numerical form, and selecting relevant attributes. Model evaluation was conducted using 10-Fold Cross Validation with performance indicators including accuracy, precision, recall, and Area Under Curve (AUC). The results show that Decision Tree C4.5 achieved 93.00% accuracy, 84.27% precision, 100% recall, and 0.944 AUC. Meanwhile, Naïve Bayes obtained slightly better performance with 93.50% accuracy, 85.23% precision, 100% recall, and 0.948 AUC. The findings indicate that both algorithms are effective for CKD classification, although Naïve Bayes demonstrated better predictive performance, while Decision Tree C4.5 provided more interpretable classification rules. This research contributes to the development of intelligent decision support systems for early CKD diagnosis.

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