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Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK)
Published by PT. Lentera Ilmu Publisher
ISSN : 30312698     EISSN : 30312698     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Other
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) adalah jurnal nasional berbahasa Indonesia yang dikelola oleh Lentera Ilmu Publisher. Jurnal ini memuat hasil penelitian dengan topik-topik penelitian yang berasal dalam cakupan rumpun sistem informasi seperti perancangan sistem informasi, analisis sistem informasi, tata kelola IT, sistem pengambilan keputusan (SPK) dan teknik informatika meliputi rekayasa perangkat lunak, kecerdasan buatan, machine learning serta bidang-bidang lainnya yang terkait ke dalam rumpun ilmu tersebut. Jurnal ini diterbitkan 2 kali dalam 1 tahun yakni pada bulan Februari, dan Agustus dengan periode penerimaan artikel sepanjang tahun. Artikel yang masuk ke jurnal ini akan di-review oleh mitra bestari sebelum diterbitkan. Proses review artikel dilakukan secara double blind review yang mana mitra bestari tidak mengetahui siapa penulis artikel tersebut dan juga sebaliknya penulis tidak mengetahui mitra bestari yang mereview artikel tersebut. Jurnal JAFOTIK merupakan jurnal akses terbuka (open access) sehingga seluruh artikel yang diterbitkan oleh jurnal ini dapat diakses kapan saja dan di mana saja oleh siapa saja tanpa dipungut biaya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 3 No. 1 (2025): JAFOTIK - February" : 5 Documents clear
Machine Learning Approaches for Detection of SQL Injection Attacks Anwar, Ican
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.v3i1.50

Abstract

This study addresses the escalating cybersecurity challenges posed by SQL injection attacks in web applications and databases. This study aims to explore and evaluate the effectiveness of machine learning techniques in detecting SQL injection attacks, providing insights into the current state of research. The research involves collecting a relevant dataset of normal and malicious SQL queries, training and testing machine learning models (Support Vector Machines, Deep Neural Networks, and Random Forest). The Deep Neural Networks model stand out with the highest accuracy 0.95 and recall 0.98, indicating its robust capability to correctly classify instances of SQL Injection Attacks. The study contributes valuable insights into the current landscape of machine learning applications for SQL injection detection, providing a foundation for further exploration and analysis in this critical cybersecurity domain.
Machine Learning Innovations in Ophthalmology Amirah
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.v3i1.51

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in ophthalmology has significantly advanced diagnostic precision and patient care. Leveraging diverse datasets such as Electronic Health Records (EHRs), Optical Coherence Tomography (OCT) images, and genomic data, AI-driven approaches have proven effective in diagnosing eye conditions and systemic diseases with ocular manifestations. This study reviews various applications of AI in ophthalmology, including fungal keratitis, diabetic retinopathy, glaucoma, and rare genetic disorders. Techniques such as Lasso regression, deep transfer learning, and Random Forest analysis have been employed to enhance diagnostic models and improve prediction accuracy. For example, deep transfer learning models like VGG19 and DenseNet have demonstrated superior performance in identifying diabetic retinopathy from OCT scans. Additionally, AI’s application in genomic studies has shown promising results in detecting genetic markers for rare diseases. The contributions of these studies extend beyond clinical applications, emphasizing AI’s role in personalized medicine, early disease detection, and improved treatment planning. By validating models across multiple centers, the scalability and consistency of AI solutions in real-world clinical environments are reinforced. This review underscores the transformative potential of AI and ML in shaping the future of ophthalmology, fostering more accurate diagnoses and personalized treatment strategies.
Village Resource Profile for Speed-up Information Delivery Noviansyah, Deri
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.v3i1.52

Abstract

In many rural areas of Indonesia, accessing accurate and timely information about village resources remains a significant challenge due to scattered data records, limited digital infrastructure, and slow manual processes. To address these issues, this study proposes a Village Resource Profile for Speeding Up Information Delivery, a system designed to centralize and streamline village data management. By integrating digital tools, this system facilitates efficient resource allocation, enhances development planning, and supports better decision-making for local governments, businesses, and residents.The study builds upon previous research in smart villages, rural governance, and geotourism management, emphasizing the role of ICT and data-driven strategies in rural development. The proposed system consists of multiple modules, including data collection, information processing, and service distribution, ensuring that essential information—such as population demographics, economic activities, and public services—is readily available.By leveraging real-time updates and AI-driven insights, the system improves access to critical information, enhances disaster preparedness, and promotes equitable public service distribution. This initiative aligns with Indonesia’s Smart Village program, fostering digital transformation at the grassroots level.
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.
A Data-Driven Approach to Dengue Fever Mapping: A Review Maulana, Muhammad Mico; A, Sanmorino
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.v3i1.54

Abstract

Dengue fever remains a pressing public health issue, especially in tropical and subtropical regions where urbanization, climate change, and ineffective vector control contribute to frequent outbreaks. Traditional surveillance methods often fall short in providing timely and accurate insights, necessitating data-driven approaches for improved monitoring and intervention. This review explores various computational methodologies, including Geographic Information Systems (GIS), machine learning, and predictive modeling, to enhance dengue outbreak mapping and risk assessment. Studies from Bangladesh, Thailand, Malaysia, and Reunion Island demonstrate how integrating epidemiological data with environmental and socio-economic factors improves outbreak prediction and control efforts. Advanced techniques, such as dynamic mapping of the basic reproduction number (R0) and deep learning models like Long Short-Term Memory (LSTM) networks, further enhance forecasting accuracy. Additionally, innovative control strategies, such as Wolbachia-infected mosquito releases, show promise in reducing dengue transmission. By synthesizing recent research, this review underscores the critical role of data science in strengthening dengue surveillance, prediction, and intervention strategies.

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