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Journal of Computers and Digital Business
Published by PT Delitekno Media Mandiri
ISSN : -     EISSN : 28303121     DOI : 10.56427
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Journal of Computers and Digital Business is an interdisciplinary and open access journal covering Computers and Digital Business. The Journal of Computers and Digital Business is open to submission from experts and scholars in the wide areas of Information System, Security, Artificial Intelligent , Cloud Computing, Machine Learning, Digital Business Technology and other areas listed in the focus and scope of this journal. Focus and Scope Information System Information Security Information Retrieval Geographic Information System Fuzzy Logics Genetic Algorithms Neural Networks Machine Learning Decision Support System Data Mining Cloud Computing E-Learning E-Goverment E-Commerce E-Business Digital Business Management Digital Business Technology Digital Business Analysis & Design Big Data & Business Intelligence Cyber Security for Digital Business
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 58 Documents
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Selection of Marketing Staff Using Simple Additive Weight and VIKOR Algorithm Muhammad Ali; Nurhayati; Ayu Azzahra Batubara
Journal of Computers and Digital Business Vol. 3 No. 3 (2024)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v3i3.600

Abstract

In today’s rapidly evolving technological landscape, decision-making processes within organizations are increasingly relying on advanced computational methods to enhance efficiency and accuracy. This is particularly relevant in human resource management, where selecting suitable candidates for key positions is critical. Traditional methods of staff recruitment often rely on subjective assessments, which may lead to biases and inconsistencies. To address these challenges, this study proposes the use of the Simple Additive Weighting (SAW) and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) algorithms as multi-criteria decision-making tools for selecting marketing staff. The SAW method offers a straightforward approach by assigning weighted scores to various criteria. In contrast, the VIKOR method provides a ranking system that considers ideal and compromise solutions for candidate selection. Integrating these two algorithms makes the selection process more objective and data-driven, reducing the risk of human error and improving overall decision quality. This paper outlines implementing the combined SAW-VIKOR model in the marketing staff recruitment process, highlighting its potential to optimize candidate evaluation and selection. The results demonstrate that utilizing these algorithms enhances the decision-making process, leading to better alignment of selected staff with organizational goals. This approach is valuable for organizations looking to leverage technology in their recruitment strategies.
The Impact of Artificial Intelligence on Healthcare: A Systematic Review of Innovations, Challenges, and Ethical Considerations Majeed Zangana, Hewa; Zina Bibo Sallow; Banaz Abed Salih
Journal of Computers and Digital Business Vol. 4 No. 1 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i1.601

Abstract

Artificial Intelligence (AI) is revolutionizing healthcare by providing innovative solutions for diagnosis, treatment, and patient care. This systematic review examines recent advancements in AI-driven technologies, focusing on their applications in clinical decision support, operational optimization, and precision medicine. Findings highlight significant improvements in diagnostic accuracy, personalized treatment plans, and healthcare delivery efficiency. Despite these advancements, the integration of AI poses challenges, including concerns about data privacy, algorithmic bias, and the transparency of AI systems. Ethical considerations, such as ensuring equity, protecting patient rights, and maintaining trust in AI-driven interventions, are also critically assessed. The review underscores the importance of responsible innovation, proposing strategies for ethical deployment and regulatory oversight to mitigate potential risks. This comprehensive overview provides valuable insights into the transformative potential of AI in healthcare, alongside its associated challenges and ethical imperatives.
Analisis Code Review Menggunakan SonarQube Terhadap Aplikasi Rumah Kreatif Toba Berbasis Website Sitorus, Nehemia; Sirait, Asri; Lumbantoruan, Efran; Panjaitan, Frans; Harahap, Handika Sukri Husni; Simangunsong, Christian Yohanes
Journal of Computers and Digital Business Vol. 4 No. 1 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i1.606

Abstract

Rumah Kreatif Toba merupakan platform digital yang diakses melalui situs resmi https://kreatif.tobakab.go.id dan dirancang untuk mendukung pengelolaan serta pemasaran produk Usaha Mikro, Kecil, dan Menengah (UMKM) di Kabupaten Toba. Penelitian ini bertujuan untuk mengevaluasi efektivitas penerapan tool-assisted review menggunakan SonarQube dalam pengembangan aplikasi tersebut. Metode yang digunakan meliputi analisis kode sumber aplikasi dengan SonarQube, yang mendeteksi 36 masalah kritis (blocker), 227 titik kerentanan keamanan, dan tingkat duplikasi kode sebesar 38,8%. Hasil penelitian menunjukkan bahwa penggunaan SonarQube efektif dalam mengidentifikasi area yang perlu diperbaiki, terutama terkait keamanan, efisiensi, dan kemudahan pemeliharaan kode. Kesimpulannya, alat analisis kode seperti SonarQube sangat penting untuk meningkatkan kualitas dan keamanan perangkat lunak. Penelitian ini merekomendasikan agar penelitian di masa depan mempertimbangkan penggunaan alat dan metode analisis lain untuk memperoleh wawasan yang lebih komprehensif.
Pengujian Backtesting Expert Advisor Berbasis Donchian Channel pada 10 Pasangan Forex dengan Volume Perdagangan Tertinggi Mutiara Akbar Nasution
Journal of Computers and Digital Business Vol. 4 No. 1 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i1.609

Abstract

Penelitian ini mengevaluasi kinerja Expert Advisor (EA) berbasis indikator Donchian Channel pada sepuluh pasangan mata uang dengan volume perdagangan tertinggi melalui metode backtesting. Pengujian dilakukan menggunakan data historis dari 1 Desember 2021 hingga 1 Desember 2024 pada platform MetaTrader 5 dengan model "Every Tick." Parameter yang digunakan meliputi stop loss 50 pips, take profit 100 pips, trailing stop 100 pips, dan optimasi lot berdasarkan modal awal $10,000 dengan leverage 1:100. Hasil menunjukkan bahwa pasangan USD/JPY mencatatkan kinerja terbaik dengan total net profit $14,771.94, Profit Factor 2.47, dan Sharpe Ratio 5.16, sementara pasangan USD/CAD menunjukkan hasil terburuk dengan drawdown maksimum 11.12% dan Profit Factor 0.33. Strategi berbasis Donchian Channel efektif untuk pasangan dengan volatilitas tinggi, tetapi kurang optimal pada pasangan dengan volatilitas rendah tanpa penyesuaian parameter. Penelitian ini berkontribusi pada pengembangan strategi trading otomatis dengan menyediakan evaluasi empiris indikator Donchian Channel pada berbagai tingkat volatilitas. Hasil penelitian memberikan panduan praktis untuk menyesuaikan parameter strategi sesuai karakteristik pasar, serta mengurangi pengaruh emosional dalam trading. Studi ini juga membuka peluang untuk mengintegrasikan indikator tambahan guna meningkatkan akurasi prediksi.
Investigating the Level of Experience in Using More Effective Software Design Tools for Enhancing Security among Federal College of Education, Gidan Madi Staff and Students Hajara Abdulkadir; Bello Alhaji Buhari; Abdulrahman Umar
Journal of Computers and Digital Business Vol. 4 No. 1 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i1.629

Abstract

Higher education institutions operate under strict regulations and standards to ensure compliance with data protection rules, safeguard sensitive information, and preserve the privacy of employees and students. This study evaluates the expertise of staff and students at the Federal College of Education, Gidan Madi, in employing software design tools to enhance security. Using a mixed-methods approach, the research involves a wide range of stakeholders, including academics, non-academics, and students. The findings reveal that software design tools significantly improve the institution's ability to detect and respond to security events. Participants highlighted the importance of data encryption, expressed confidence in their knowledge of the latest advancements in security tools, and rated institutional support for security measures as excellent. The study also identified gaps in network monitoring capabilities, which are critical for proactive security management. Based on these findings, the study recommends the implementation of advanced network monitoring tools to enhance security measures at the institution. These results are expected to benefit higher education stakeholders, IT administrators, and decision-makers responsible for designing and maintaining robust security systems. By addressing current challenges and improving expertise in using security tools, this research contributes to the broader goal of strengthening data protection in higher education institutions.
The Contribution of Artificial Intelligence to Addressing the Global Goals for Sustainable Development Hany Fathy Abdel-Elaah; Amira Hassan Abed
Journal of Computers and Digital Business Vol. 4 No. 1 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i1.633

Abstract

The increasing prevalence of Artificial Intelligence (AI) across various industries necessitates an assessment of its impact on achieving the Sustainable Development Goals (SDGs). Studies indicate that AI has the potential to support 134 targets across all goals through professional, consensus-based data collection strategies. However, it may also hinder progress toward 59 targets, presenting a complex interplay between benefits and challenges. Key concerns include gaps in safety, transparency, and ethical standards, which arise when regulatory frameworks fail to keep pace with the rapid advancement of AI technologies. These issues highlight the need for robust governance and oversight mechanisms to address potential risks. Additionally, overlooked components in the study, such as social equity, environmental justice, and accessibility, are critical for ensuring AI-based solutions contribute effectively to sustainable growth. This research emphasizes the importance of aligning AI applications with global regulatory and ethical standards to maximize positive outcomes while mitigating adverse effects. By fostering collaboration among policymakers, industry leaders, and researchers, AI can become a transformative tool for achieving SDGs. Future efforts should prioritize addressing regulatory gaps and ensuring that AI-driven innovation remains inclusive, transparent, and aligned with the core principles of sustainability.
Artificial Intelligence-Driven Pharmaceutical Research: A Comprehensive Analysis of Applications and Challenges Amira Hassan Abed
Journal of Computers and Digital Business Vol. 4 No. 1 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i1.634

Abstract

This review investigates the integration of Artificial Intelligence (AI) in pharmaceutical product development, focusing on its applications in drug discovery, design, manufacturing, and quality control. Key AI methodologies, such as machine learning (ML) and deep learning (DL), are analyzed for their contributions to critical stages, including target identification, molecular screening, and clinical trial optimization. The findings highlight AI's capacity to streamline workflows, reduce development costs, and enhance efficacy, with notable improvements in drug discovery speed, prediction accuracy of drug safety and efficacy, and novel approaches in drug repurposing and personalized medicine. Despite these advancements, challenges such as fragmented data integration, limited availability of specialized skillsets, and resistance to AI adoption remain significant barriers. This review emphasizes the need for industry-wide collaboration to address these issues and leverage AI's full potential. In conclusion, AI demonstrates transformative capabilities in accelerating drug development cycles and enabling precision-driven innovations, promising a paradigm shift in pharmaceutical practices through the convergence of computational power and biological sciences.
Banking Cybersecurity: Safeguarding Financial Information in the Digital Era Hewa Majeed Zangana; Harman Salih Mohammed; Mamo Muhamad Husain
Journal of Computers and Digital Business Vol. 4 No. 2 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i2.751

Abstract

This study explores the escalating cybersecurity challenges in the banking sector and the potential of large language models (LLMs) to enhance digital defense mechanisms. Employing a qualitative methodology that includes a systematic literature review, expert interviews, and case study evaluations, the research investigates the integration of LLMs in cybersecurity operations such as threat detection, automated incident response, and user authentication. The findings reveal that LLMs offer significant advantages in real-time anomaly detection, predictive analytics, and natural language-based security training. However, their adoption is hindered by concerns over algorithmic transparency, data privacy, and the need for specialized technical expertise within financial institutions. A key contribution of this work is the development of an integrated cybersecurity framework that combines AI-driven technologies, blockchain-based transaction security, digital forensic tools, and human-centered security practices. The proposed framework aims to guide financial institutions in implementing adaptive, intelligent cybersecurity strategies aligned with evolving global regulatory standards. This research offers both theoretical insights and practical recommendations for enhancing cyber resilience in digital banking environments. It emphasizes the importance of a multidimensional approach that addresses technical innovation, organizational preparedness, and regulatory compliance. Future studies are encouraged to validate the proposed framework through empirical testing across diverse banking infrastructures.
Machine Learning (ML) Algorithms for Diagnosing Blood Cancer in Blood Smear Images Amira Hassan Abed
Journal of Computers and Digital Business Vol. 4 No. 2 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i2.756

Abstract

Artificial intelligence (AI), particularly deep learning (DL), has significantly advanced medical image analysis, including the detection and classification of blood cancer through blood smear images. This review explores the state-of-the-art data mining (DM) and DL techniques applied in the identification and classification of white blood cells (WBCs), with a focus on leukemia diagnosis. By systematically analyzing relevant literature from 2014 to 2024, the study highlights key AI algorithms, including traditional machine learning models such as SVM, KNN, and ANN, as well as modern DL architectures like CNN, RCNN, ResNet, and hybrid models. The review evaluates their performance, clinical applicability, and implementation challenges. Particular attention is given to the strengths of DL in feature extraction and classification accuracy, which often surpass traditional DM approaches. Despite these advances, issues such as data scarcity, computational cost, and the need for medical expertise remain major challenges. The study also outlines future directions involving lightweight DL models, transfer learning, and open-access datasets to enhance clinical deployment. Ultimately, this work provides a comprehensive foundation for researchers and developers aiming to improve blood cancer diagnosis through automated medical imaging systems powered by AI.
DNA Sequence Classification Using Machine Learning Models Based on k-mer Features Kautsar, Afthar
Journal of Computers and Digital Business Vol. 4 No. 2 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i2.762

Abstract

Cell-free DNA (cfDNA) has emerged as a promising biomarker in various clinical applications, particularly in cancer detection, prenatal diagnostics, and disease monitoring. Accurate classification of cfDNA sequences is crucial for improving diagnostic reliability and enabling timely clinical decisions. This study investigates the application of machine learning models—Decision Tree (DT), Support Vector Machine (SVM), and Deep Neural Network (DNN)—for classifying cfDNA sequences using k-mer-based feature extraction, with k set to 3. A total of 3,000 DNA sequences comprising both normal and tumor-derived samples were transformed into numerical feature vectors based on the frequency of 3-mer patterns. The models were trained and evaluated using standard metrics including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the DNN model achieved the highest classification performance, effectively distinguishing between normal and tumor cfDNA. In contrast, the DT and SVM models exhibited relatively lower performance, particularly in identifying normal sequences. The study also addresses challenges such as class imbalance and limitations of simple k-mer representations. These findings highlight the potential of deep learning approaches in improving cfDNA sequence analysis and open avenues for future research using more complex models, larger datasets, and feature engineering techniques to enhance classification accuracy and clinical applicability.

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