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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 119 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 Paneru, Bishwash; Paneru, Biplov; Pandey, Nitish; Neupne, Kabita; Adhikari, Pukar; Poudyal, Ramhari
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

For the analysis of Proton Exchange Membrane Fuel Cell (PEMFC’s) efficiency, the Nernst equation and Butler-Volmer's concepts were used. The mathematical models using both equations were developed in MATLAB and compiled. The results generated by the output current based on the input parameters of the experimental data were compared with the experimental results for the two modelled PEMFCs. The parameters temperature, pressure, hydrogen concentration, and oxygen concentration at different values of external resistance were used to determine the change in output current in both models built in MATLAB. This sensitivity analysis generated negative output current values and highly dissimilar values with the experimental results for the same input parameters for both models due to the less use of input parameters in the model. The results showed that the PEMFC's performance is affected by most parameters, and many influencing parameters must be used to develop a perfect mathematical model of the PEMFC.
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 Syed, Asif Ali; Syed, Hassan Ali; Leghari, Irfan M.; Sanjrani, Anwar Ali; M, Sajid
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

With the ageing of population, the rate of disability is increasing. Its types are many including ‘hard of hearing’. In majority of the countries, they are considered as social liability by their own family on one hand, and by the society on the other. Now-a-days particularly in the developing countries, it is challenging for them to get suitable education and employment opportunities due to various reasons including social stigma, lack of facilities and lack of awareness, particularly among parents. In modern era, technology has played an important role in improving their quality of life. In this regard, parent knowledge and support play crucial role. Systematic literature review is adopted for this study. Based on findings and expertise of researcher, ITGM model is suggested. Findings suggest that due to lack of knowledge of their parents towards sign language and lack of awareness towards their needs. Further, there is no standard sign language system on global platform. In addition, expressions lack in sign language due to missing gap of grammar. Researchers suggest a global model, and recommend Higher Education Institutions, Educationists, NGOs, and Parents alongside policy makers to establish training courses for parents. With special regard to sign language, a global model suggested by the researchers should be further strengthened by funding agencies.
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 Abdulazeez, Adnan Mohsin; Hasan, Shereen Sadiq
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

The article emphasizes the critical need for early and accurate diagnosis of cardiovascular disease (CVD), a leading cause of global mortality. Recent advancements in machine learning (ML) have shown promising results in classifying cardiac disorders, aiming to enhance healthcare practices. It discusses both the benefits and limitations of current ML algorithms used in this field, highlighting their role in improving the management of cardiac diseases through accurate diagnosis. The study evaluates various supervised learning techniques like support vector machines, decision trees, and neural networks, illustrating their effectiveness in handling diverse datasets and identifying significant patterns. Furthermore, it explores unsupervised learning methods such as clustering algorithms, which uncover hidden patterns in cardiac data. The research also investigates the potential of ensemble approaches and deep learning to further enhance classification accuracy. In conclusion, the study provides an overview of the current state of ML-based heart disease classification research, aiming to inform policymakers, physicians, and researchers about the transformative potential of ML in advancing heart disease diagnosis and treatment, ultimately aiming for improved patient outcomes and reduced 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 12 | Total Record : 119

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