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Journal : Digitus : Journal of Computer Science Applications

Toward Data-Driven Health Transformation: Accessibility, Interpretability, and Institutional Readiness for AI Soderi, Ahmad
Digitus : Journal of Computer Science Applications Vol. 2 No. 2 (2024): April 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i2.833

Abstract

Artificial intelligence (AI) and big data analytics are increasingly recognized as vital tools in transforming healthcare delivery, particularly within hospital settings. This narrative review aims to explore the challenges and opportunities associated with the implementation of these technologies in urban healthcare systems. Using literature obtained from Scopus, PubMed, and Google Scholar, the review employs keywords such as "AI in healthcare," "big data analytics," and "predictive analytics in medicine" to synthesize peer-reviewed studies that examine both theoretical and practical dimensions of AI adoption. The analysis reveals that while developed countries are more equipped with infrastructure and training, developing nations often face systemic challenges such as limited funding, inadequate technology, and insufficient regulatory support. Accessibility remains a key concern, with disparities in technological adoption driven by geographic, demographic, and institutional factors. Furthermore, the review identifies gaps in the interpretability and integration of AI tools, especially in infection management and clinical decision-making. The discussion emphasizes the need for adaptive policy interventions, targeted investments in healthcare training, and the development of transparent AI systems. The study also recommends enhancing cross-sector collaboration to build scalable and inclusive health innovations. In conclusion, addressing the structural, ethical, and educational dimensions of AI deployment is essential for realizing its full potential in global healthcare improvement.
Decentralized Identity in FinTech: Blockchain Based Solutions for Fraud Prevention and Regulatory Compliance Yuni T, Veronika; Soderi, Ahmad
Digitus : Journal of Computer Science Applications Vol. 3 No. 3 (2025): July 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i3.955

Abstract

The FinTech sector is facing escalating threats from identity theft and digital fraud, with global losses exceeding US$42 billion annually. This study explores how blockchain based identity systems particularly Verifiable Credentials (VC), Decentralized Identifiers (DID), and selective disclosure protocols can enhance digital security, reduce onboarding time, and ensure compliance with evolving global standards. A qualitative and comparative methodology was applied, analyzing data from regulatory bodies (FTC, FATF, NIST), industry case studies, and technical frameworks (OpenID4VC, SD JWT, W3C). Results reveal that blockchain identity solutions reduce fraud risk by preventing synthetic identity use, while significantly improving authentication success rates through biometric and passkey based logins. Reusable KYC models integrated with VC/DID frameworks cut onboarding durations from weeks to days, demonstrating substantial operational efficiency. Furthermore, alignment with GDPR, eIDAS 2.0, and AML/CFT standards confirms the regulatory readiness of these systems. The findings suggest that decentralized identity offers a viable, scalable alternative to traditional identity verification, enabling secure, privacy preserving, and user controlled authentication. Despite challenges such as integration complexity and regulatory fragmentation, the strategic advantages in security and compliance position blockchain identity systems as essential tools for the future of FinTech.
Sentiment Analysis of Public Opinion on the 2024 Presidential Election in Indonesia Using Twitter Data with the K-NN Method Diantoro, Karno; Soderi, Ahmad; Rohman, Abdur; Sitorus, Anwar T.
Digitus : Journal of Computer Science Applications Vol. 1 No. 1 (2023): October 2023
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v1i1.27

Abstract

Twitter is often used by the public as a platform to speak and express their opinions, especially in the context of the 2024 Presidential Election. Tweets related to the '2024 Presidential Election' can be used as a source of data for social media analysis to determine whether the expressed opinions tend to be positive or negative. The research process involves data collection of tweets, preprocessing, tokenization, class attribute determination, directory filling, sentiment analysis, and classification steps, including testing the value of k and testing the confusion matrix. The research and testing results show that the K-NN method successfully achieves a sentiment classification accuracy rate of 86.48%.