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International Journal of Research and Applied Technology (INJURATECH)
Published by Universitas Komputer Indonesia
ISSN : 28100662     EISSN : 28077229     DOI : https://doi.org/10.34010/injuratech.v1i2.5902
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
INJURATECH cover all topics under the fields of Computer Science, Information system, and Applied Technology. Scope: Computer Based Education Information System Database Systems E-commerce and E-governance Data mining Decision Support System Management Information System Social Media Analytic Data visualization Cloud computing platforms Distributed file systems and databases Big data technologies Data capture and storage Computer Architecture and Embedded Systems Geographic information system (GIS) Remote Sensing Software Engineering Internet and Web Applications Mobile Computing Hardware and physical security Mobile Computing Security management and policies Block chain Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 210 Documents
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Dynamics of The U.S.-China Relationship Post-Firewall Malware Attack Boer, Dara Cantika Putriadin; Wardhana, Fahriy Aulia; Arifin, Muhammad; Zahra, Nadhira Fitria; Triwahyuni, Dewi
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injuratech.v5i1.16169

Abstract

This study analyzes the impact of the Firewall Malware attack on US-China bilateral relations and its implications for global cybersecurity policy. The attack, detected in late 2024, exploited firewall vulnerabilities in US government and private sector systems, leading to significant data breaches. The US attributed the attack to a hacker group allegedly affiliated with the Chinese government, while China denied the allegations, escalating geopolitical tensions and undermining the 2015 US-China Cyber Agreement. Using a qualitative research methodology with a case study approach, this study examines government reports, cybersecurity analyses, policy statements, and media coverage, applying content analysis to assess geopolitical consequences and the effectiveness of international cybersecurity agreements. The findings reveal that the attack intensified US-China tensions, prompting sanctions, policy shifts, and heightened cybersecurity measures. The US reinforced its cyber defence strategies and imposed economic restrictions, while China sought to build alliances to counter the accusations. The study highlights the failure of existing international cybersecurity agreements in preventing state-sponsored cyber threats, emphasizing the urgent need for stronger global cooperation and regulatory frameworks to mitigate cyber conflicts and enhance cybersecurity resilience.
Digital Comic Strip as an Information Media on the Impact of Sleep Call Fauzi, Ahmad Nurzaeni; Wantoro; Melati
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injuratech.v5i1.16170

Abstract

Sleep call is a phone or video call activity between two people, usually a couple, that takes place when they are about to sleep. It is not uncommon for sleep calls to be carried out from night until both of them wake up the next morning. This phenomenon, although it has a positive impact on maintaining emotional closeness in the couple's relationship, can also cause various negative impacts such as disturbed sleep patterns, fatigue, and decreased productivity. This design aims to create an effective information media in conveying messages about the impact of sleep calls through comic strip media. Comic strips were chosen as a medium because of their ability to convey messages visually and narratively that are easy to understand and attractive to the target audience, the majority of whom are teenagers to young adults. An informative comic strip can increase audience awareness about the impact of sleep calls and encourage audiences to reconsider habits for good health
Integrating Web Technologies with Augmented Reality Rafdhi, Agis Abhi
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

This study aims to provide a comprehensive review of the integration between web technologies and augmented reality (AR) in engineering and computing contexts. Using a structured literature review method, 75 peer-reviewed articles published between 2019 and 2024 were selected based on defined criteria, including relevance, scope, and methodological quality. The findings reveal that WebAR enhances accessibility, interactivity, and real-time visualization, particularly in education, remote maintenance, and smart systems. Despite these advantages, several challenges persist, such as limited infrastructure, technical constraints, and lack of curriculum alignment. This research concludes that a cross-disciplinary approach is essential to fully realize the potential of AR through scalable web-based frameworks, supported by collaboration among educators, developers, and system engineers.
Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction Sari, Annisa Wulan
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

The increasing volatility and complexity of cryptocurrency markets have led to the growing application of machine learning (ML) techniques for accurate price prediction. This study presents a comparative analysis of eleven recent research papers on cryptocurrency forecasting using various ML and deep learning models, including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and ensemble methods. The findings highlight that deep learning models, particularly GRU and LSTM, often outperform traditional statistical models in capturing non-linear patterns and temporal dependencies. Moreover, feature diversity—such as on-chain data, market sentiment, and macroeconomic indicators—has been shown to significantly enhance predictive performance. However, many studies still lack comprehensive validation strategies and rely solely on historical price data, limiting generalizability. This review identifies key gaps in model benchmarking, feature integration, and evaluation consistency, providing a foundation for future research focused on hybrid models and interpretable AI for financial decision-making.
Artificial Intelligence in Web-Based Geographic Information Systems: A Cross-Disciplinary Review Hayati, Euis Neni
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

This study examines the integration of Artificial Intelligence (AI) into Web-Based Geographic Information Systems (WebGIS) from a cross-disciplinary perspective. By conducting a structured review of 15 scholarly articles published between 2019 and 2025, this paper aims to explore how AI technologies—such as machine learning, natural language processing, and computer vision enhance spatial analysis and decision-making across various sectors. The findings indicate a growing trend in AI-WebGIS research, particularly in areas like environmental monitoring, disaster management, smart agriculture, public health, and education. While AI integration offers significant advancements in automation, scalability, and user interactivity, several limitations remain, including ethical considerations, data standardization issues, and limited real-world implementation. This review highlights future opportunities in building more inclusive, interoperable, and operationally scalable WebGIS platforms powered by AI.
Classification of Medical Images Based on Unsupervised Algorithms: A Review Zeebaree, Imad Majed; Abdulazeez, Adnan Mohsin
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

Artificial intelligence models are becoming increasingly essential in biomedical research and healthcare services. Various healthcare organizations utilize information-based machine learning and image-processing methods for the diagnosis of diseases. This review delves explicitly into elucidating the challenges and considerations of developing unsupervised learning for clinical decision support systems in real-world contexts. In recent years, supervised and unsupervised deep learning have demonstrated promising medical imaging and image analysis outcomes. Unsupervised learning gathers data, draws insights from it, and makes data-driven judgments without bias, unlike supervised learning, which requires manual class labeling. A systematic review of unsupervised medical image analysis methods is presented here. This extensive review introduces diverse methodologies rooted in unsupervised classification for detecting diseases and analyzing images. Moreover, we offer insights into publicly available image benchmarks, datasets, and performance measurement details. Each method's strengths and weaknesses are thoroughly discussed, complemented by tabular summaries illuminating each category's outcomes. Additionally, the article furnishes detailed descriptions of the frameworks employed by each approach and the image datasets utilized.
A Systematic Literature Review: The Use of Artificial Intelligence and Machine Learning in Financial Risk Management and Predictive Analytics Fahrezi, Muhamad
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 1 (2024): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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Abstract

This systematic literature review explores the role of Artificial Intelligence (AI) and Machine Learning (ML) in financial risk management and predictive analytics by analyzing 20 peer-reviewed articles published between 2019 and 2025. From an initial pool of 131 articles, a rigorous screening process was conducted to ensure relevance and quality. The findings indicate that AI and ML have significantly enhanced the accuracy, speed, and adaptability of financial risk assessments, particularly in areas such as credit risk prediction, fraud detection, and market volatility forecasting. However, challenges such as lack of model transparency, limited implementation in real-world settings, and insufficient coverage of emerging markets remain prevalent. This review identifies future research opportunities including the development of explainable AI (XAI), alignment with regulatory frameworks, expansion into underexplored financial domains, and the creation of localized models for inclusive finance. Overall, AI and ML demonstrate transformative potential, but their effectiveness depends on responsible, context-aware, and interdisciplinary application
Classification of Ultra Sound Images Breast Cancer Based on Deep Learning: A review Abdulazeez, Adnan Mohsin; Alnabi, Nisreen Luqman Abd
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

Breast cancer is the second most common cause of mortality for women, after lung cancer. Women's death rates can be decreased if breast cancer is identified early. The artificial intelligence model has the ability to predict breast cancer with the same level of accuracy as an experienced radiology technician. For early cancer detection, an automated approach is necessary because manual breast cancer diagnosis is time-consuming. Deep learning is a type of artificial intelligence that enables software applications to predict more accurate results without being explicitly programmed. The main objective of this paper is to evaluate the performance of a general deep learning algorithm (DLS) with human readers with varying degrees of breast imaging experience in order to train it to identify cancer of the breast on ultrasound pictures. Moreover, this study will examine five deep learning methods that have aided in breast cancer prediction, these are Convolutional Neural Network (CNN), Genetic Algorithm GA-CNN, Deep Belief Network (DBN), Computer Aided Diagnosis (CAD), and Generative Adversarial Networks (GAN). Our main goal is to identify the most appropriate and accurate algorithm for the prediction of breast cancer.
Feature Selection Methods of Gene Expression Based on Machine Learning: A Review Merceedi, Karwan Jameel; Abdulazeez, Adnan Mohsin
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

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Abstract

This article offers a thorough analysis of feature selection strategies that use machine learning to analyze gene expression data. In order to extract significant biological insights, the explosion of high-dimensional genomic data has required the invention and use of sophisticated analysis techniques. In this situation, feature selection is essential because it finds the most pertinent genes that have a major impact on the prediction ability of machine learning models. The paper examines a range of feature selection techniques, classifying them into filter, wrapper, and embedding approaches, each having special advantages and disadvantages. The importance of gene expression data in comprehending the molecular mechanisms underlying complicated diseases and biological processes. The difficulties presented by high-dimensional datasets are next explored, with a focus on feature selection as a means of enhancing model interpretability, lowering computational cost, and raising prediction accuracy. In order to shed light on the fundamental ideas and practical uses of well-known feature selection algorithms, the writers thoroughly examine a number of them, including Mutual Information, Relief, and Recursive Feature Elimination (RFE). Additionally, the study assesses these methods' performance critically across a range of datasets and experimental situations, emphasizing important factors like interpretability, scalability, and resilience. The paper also discusses new developments in feature selection, such as the incorporation of deep learning techniques, ensemble methods, and domain expertise. In order to fully realize the promise of gene expression data for biomedical research and clinical applications, the study ends with a discussion of the present issues and prospective future directions in the field. This discussion emphasizes the significance of creating reliable and understandable feature selection techniques. This thorough study will be an invaluable tool for practitioners, researchers, and bioinformaticians in the field of genomics as they navigate the challenging terrain of feature selection techniques in the context of machine learning-based gene expression analysis.
Designing A Website-Based Cash Flow Accounting Information System Using Php and Mysql at Raudhatul Atfal Dewi, Annisa Ammyla; Supriyati
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

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Abstract

This study aims to design a web-based cash flow accounting information system to address the issue of manual financial record-keeping still applied at Raudhatul Atfal Khalidya. The system was developed using the Waterfall model, which includes stages of needs analysis, design, implementation, testing, and maintenance. Data were collected through observation, interviews, documentation, and literature review. The results show that the system successfully replaces manual processes with digital ones, minimizes recording errors, accelerates reporting, and improves the accuracy and transparency of financial information. The system follows the ISAK 35 standard and generates cash flow reports based on operating, investing, and financing activities. Using PHP and MySQL technology, the system can be accessed flexibly and in real time by authorized personnel. This research provides practical contributions for nonprofit educational institutions to manage finances more effectively.

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