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All Journal Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK)
Erizo, Juan Jacob
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Other

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AI-Augmented Code Generation erizo, juan jacob
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 3 No. 1 (2025): JAFOTIK - February
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v3i1.53

Abstract

AI-augmented code generation is transforming software development by enhancing productivity, reducing repetitive tasks, and improving code quality. Tools like GitHub Copilot, OpenAI Codex, and IntelliCode assist developers by providing real-time code suggestions, generating functions from natural language prompts, and detecting potential errors. This technology simplifies coding workflows, allowing programmers to focus on complex problem-solving rather than routine coding tasks.AI-powered tools rely on deep learning models trained on vast code repositories to understand context and generate relevant code snippets. While these tools significantly speed up development, they also introduce challenges such as security risks, computational costs, and the need for human oversight. Despite these concerns, AI-driven coding assistants are proving invaluable in modern software engineering, supporting applications in cloud computing, competitive programming, and full-stack development.Beyond simple code suggestions, AI assists with debugging, performance optimization, and even full project generation. As AI models continue to evolve, their integration into software development will further enhance efficiency and accessibility.
Navigating the Frontier: Assessing the Extent of AI's Influence in Healthcare Erizo, Juan Jacob
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 2 No. 1 (2024): JAFOTIK - February
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v2i1.27

Abstract

This study explores the integration of Artificial Intelligence (AI) into healthcare, examining its applications across various domains, including diagnostic imaging, personalized medicine, predictive analytics, and administrative workflows. AI has demonstrated significant potential to enhance the accuracy, efficiency, and accessibility of medical services. For instance, AI-driven diagnostic tools improve cancer detection, while AI in personalized medicine tailors treatments based on genetic data. However, challenges such as ethical concerns, data privacy, and the "black box" nature of AI algorithms pose barriers to its widespread adoption. The study employs a mixed-method approach, including literature reviews, expert interviews, and case studies, to assess AI's impact on healthcare. Results indicate that while AI has achieved notable successes, such as reduced diagnostic errors and improved patient outcomes, the implementation faces obstacles like staff AI literacy and high costs.
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