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Rizky Jumansyah
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International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Published by Universitas Komputer Indonesia
ISSN : 28100670     EISSN : 27755584     DOI : https://doi.org/10.34010/injiiscom
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering
FOCUS AND SCOPE INJIISCOM cover all topics under the fields of Computer Engineering, Information system, and Informatics. Informatics and Information system IT Audit Software Engineering Big Data and Data Mining Internet Of Thing (IoT) Game Development IT Management Computer Network and Security Mobile Computing Security For Mobile Decision Support System Web and Cloud Computing Accounting Information system Electrical and Computer Engineering Sensors and Trandusers Signal, Image, Audio and Video processing Communication and Networking Robotic, Control and Automation Fuzzy and Neural System Artificial Intelligent
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 146 Documents
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Plant Nutrition Monitoring System for Water Spinach Based on Internet of Things Alviana, Sopian; Dwi Nugraha, Rizki; Kurniawan, Bobi
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.12705

Abstract

The concept of plants using a hydroponic system has been widely used. Currently, the weakness in the management of the hydroponic system is the difficulty in managing the nutritional needs of plants. Nutrition is the main requirement for plants with the concept of a hydroponic system. In this research, a system will be proposed that can monitor the nutritional needs of hydroponic plants with a concentration of water spinach plants. The use of internet of things technology is proposed to be able to monitor in real time. With the existence of a monitoring system in real time, it can make it easier to monitor and control the nutritional needs of kangkong plants using a smartphone.
Detection of SQL Injection Attacks Based on Supervised Machine Learning Algorithms: A Review Salih Abdullah, Hilmi; Mohsin Abdulazeez, Adnan
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.12731

Abstract

In the ever-changing world of cybersecurity, it is becoming more important to ensure integrity of web applications as well as securing sensitive data. Among a variety of vulnerabilities, SQL injection is considered a significant risk with severe consequences. Addressing this crucial threat has always attracted the researchers to explore various approaches to identify and detect SQL injection attacks. The machine learning has captured the attention of the researchers to explore its potential due to its success in several different fields and the limitation of other rule-based approaches. This study provides a comprehensive review on a variety of the most recent researches that have been carried out using supervised learning algorithms. The study reveals that machine learning has a huge potential in the process of identification and detection of SQL injection attacks.
Bibliometric Analysis using Vos Viewer with Publish or Perish of Intelligent Tutoring System in Private Universities Kurniawan, Bobi; Meyliana, M; Leslie Hendric Spits Warnars, Harco; Suharjo, Bambang; Ahiase, Godwin
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.12732

Abstract

The objective of this study is to analyze the development of intelligent tutoring systems in private universities. We conducted the analysis using bibliometric methods, utilizing the Publish or Perish and VOSviewer applications. Data was obtained by using the publish or perish application with the keyword "intelligent tutoring system in private university" from the Google Scholar database from 2019 to 2024. According to search results, the number of research papers has decreased from 117 to 23 from 2020 to 2024. Mapping using VOSviewer application produces three types of visualization, namely network, overlay, and density visualization. In its conclusion, this research notes a decrease in the number of studies discussing in private universities since 2020, but still shows great potential for development by other researchers.
Revolutionizing Cybersecurity: The GPT-2 Enhanced Attack Detection and Defense (GEADD) Method for Zero-Day Threats Jones, Rebet; Omar, Marwan
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.12741

Abstract

The escalating sophistication of cyber threats, particularly zero-day attacks, necessitates advanced detection methodologies in cybersecurity. This study introduces the GPT-2 Enhanced Attack Detection and Defense (GEADD) method, an innovative approach that integrates the GPT-2 model with metaheuristic optimization techniques for enhanced detection of zero-day threats. The GEADD method encompasses data preprocessing, Equilibrium Optimization (EO)-based feature selection, and Salp Swarm Algorithm-Based Optimization (SABO) for hyperparameter tuning, culminating in a robust framework capable of identifying and classifying zero-day attacks with high accuracy. Through a comprehensive evaluation using standard datasets, the GEADD method demonstrates superior performance in detecting zero-day threats compared to existing models, highlighting its potential as a significant contribution to the field of cybersecurity. This study not only presents a novel application of deep learning for cyber threat detection but also sets a foundation for future research in AI-driven cybersecurity solutions
The Use of MATLAB Programming to Compare Experimental vs Modeled PEMFCs using the Nernst and Butler-Volmer’s Equation-Based Mathematical Models Bishwash Paneru; Biplov Paneru; Nitish Pandey; Kabita Neupne; Pukar Adhikari; Ramhari Poudyal
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 1 (2025): INJIISCOM: VOLUME 6, ISSUE 1, JUNE 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i1.13414

Abstract

This study evaluates Proton Exchange Membrane Fuel Cell (PEMFC) efficiency using mathematical models based on the Nernst and Butler-Volmer equations. Developed in MATLAB, these models simulated output current utilizing experimental parameters temperature, pressure, and reactant concentrations across varying external resistances. Comparing the simulated outcomes with experimental data revealed significant discrepancies, including negative current values in the models. These deviations stem from the limited operational parameters incorporated in the simulation. The sensitivity analysis further confirmed that PEMFC performance is heavily dependent on multiple variables. Consequently, the findings demonstrate that an accurate mathematical model for PEMFCs necessitates integrating a broader range of influencing parameters to reliably predict practical performance
Interactive Triangular Global Model (ITGM) to Bridge Knowledge of Parents Towards Their Special Needs Children to Mitigate Communication Gap: Interactive Triangular Global Model to Bridge Knowledge of Parents Asif Ali Syed; Hassan Ali Syed; Irfan M. Leghari; Anwar Ali Sanjrani; Sajid M
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 1 (2025): INJIISCOM: VOLUME 6, ISSUE 1, JUNE 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i1.13509

Abstract

This study discusses challenges faced by individuals, particularly the hard of hearing, in accessing education and employment due to societal stigmas and lack of support. Technology plays a crucial role in improving their quality of life. Parental knowledge and support are deemed vital, yet lacking, particularly in sign language understanding. The study uses a systematic literature review and proposes the ITGM model to address these issues. It highlights gaps in global sign language standards and grammar, suggesting training for parents and collaboration among educational institutions, NGOs, and policymakers. The aim is to enhance opportunities for the hard of hearing by fostering awareness and support, ultimately advocating for a global model in sign language development supported by adequate funding.
Healthcare Diseases Classification Based on Machine Leaning Algorithms: A Review Mohammed, Ahmed Jameel; M. Abdulazeez, Adnan
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.13581

Abstract

Researchers have increasingly focused on applying machine learning algorithms to enhance healthcare operations in the past few years. Machine learning has become increasingly popular and has shown to be a viable strategy for raising the standard of healthcare, preventing disease transmission and early disease detection, reducing hospital operational expenses, aiding government healthcare programs, and enhancing healthcare efficiency. This review offers a succinct and well-structured summary of machine learning research that has been done in the field of healthcare. Specifically, the emphasis is placed on the examination of non-communicable illnesses, which pose a significant risk to public health and rank among the primary contributors to global mortality. Moreover, the COVID-19 pandemic, which is among the world's deadliest illnesses and has recently been formally declared a public health emergency, is included. This study aims to assist health sector researchers in choosing appropriate algorithms. After conducting a comprehensive investigation, it was shown that the Decision Tree (DT), Gaussian Naive Bayes (GNB), and Random Forest (RF), algorithms had the highest performance in healthcare classification, achieving a remarkable accuracy rate of 100%. In most tests, the Random Forest (RF) and Support Vector Machine (SVM) demonstrated consistently better performance
Classification of Heart Diseases Based on Machine Learning: A Review Adnan Mohsin Abdulazeez; Shereen Sadiq Hasan
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 1 (2025): INJIISCOM: VOLUME 6, ISSUE 1, JUNE 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i1.13600

Abstract

This review emphasizes the critical need for the early and accurate diagnosis of cardiovascular diseases (CVDs), a leading global cause of mortality. Recent advancements in machine learning (ML) offer promising solutions for classifying cardiac disorders and enhancing healthcare practices. This article discusses the benefits and limitations of current ML algorithms, evaluating supervised learning techniques (e.g., support vector machines, decision trees, and neural networks) and unsupervised methods (e.g., clustering algorithms) in identifying significant patterns within diverse datasets. Furthermore, it explores ensemble approaches and deep learning to improve classification accuracy. Ultimately, this study highlights ML's transformative potential in advancing heart disease diagnosis, improving patient outcomes, and reducing healthcare costs. 
An Innovative Deep Neural Network Model for Precise Calorie Burn Prediction from Physical Activity Data Ahmed, Ayah M; Mohammed, Chira N.; Ali, Sardar Hasen
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.13870

Abstract

Accurate prediction of calories burned during physical activities is crucial for various applications in health monitoring, fitness tracking, and personalized nutrition. Traditional methods often lack the precision needed for individualized estimates, which has increased interest in advanced machine learning approaches. This research introduces a deep learning model designed to predict calories burned with enhanced accuracy by capturing complex, non-linear relationships in the data. The model employs a multilayer perceptron neural network, Leaky ReLU activations, dropout regularization, and the Adam optimizer to improve generalizability and prevent overfitting. The evaluation of training and validation loss over epochs demonstrated the model's robustness and capacity to generalize effectively to novel data. The model's performance was evaluated using various metrics, achieving superior results with a remarkable Mean Absolute Error (MAE) of 0.27% and an accuracy of 99.73%, outperforming other models discussed in the literature. These findings indicate that deep learning offers significant potential for improving calorie prediction models, providing more reliable fitness and health management tools.
A Bibliometric Analysis of Graph Labeling Study Using VOSviewer Kuan, Yoong Kooi; Azahri, Khairul Azri; Rijal, Muhammad Wirawan Nabil Budiman; Mazlan, Muhammad Imran
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 2 (2023): INJIISCOM: VOLUME 4, ISSUE 2, DECEMBER 2023
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v4i2.13893

Abstract

Graph labeling is a well-known theme of graph theory that involves an assignment of integers to the domain elements such as vertices or edges, or both, subject to certain conditions. A bibliometric and descriptive quantitative approach is used in this study to conduct a bibliometric analysis on graph labeling by integrating mapping analysis with VOSviewer software. The data was obtained from a Google Scholar search using the keyword "graph labeling" that resulted in 980 articles published between 2018 and 2023, but only 375 of these articles were relevant to the subject. The results show that research on graph labeling changed from 2018 to 2023. To sum up, this work is the first to use VOSviewer for bibliometric analysis of a graph labeling study. It is hoped that this will make it a useful resource for future research on related issues.

Page 8 of 15 | Total Record : 146

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