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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Computer Science and Information Technology
Rizky Ananda, Muhammad
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Computer Science & IT Electrical & Electronics Engineering

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Design of Application Framework for Vital Monitoring Mobile-Based System Rizky Ananda, Muhammad; Faisal, Mohammad Reza; Herteno, Rudy; Nugroho, Radityo Adi; Abadi, Friska
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28416

Abstract

In the realm of modern healthcare, continuous monitoring can leverage the affordable wearable devices available on the market to manage costs. However, these devices face several limitations, such as restricted access for other parties, including nurses and doctors, and the need for redevelopment to integrate new devices for data accessibility. This study addresses these challenges by establish an application framework tailored for mobile-based systems, by ensuring accessibility by external parties. The research contribution is encompassing two key aspects: the potential implementation of Feature-Oriented Domain Analysis (FODA) in the domain of mobile-based vital sign monitoring, particularly in the absence of prior studies addressing the same context, and the identification reusable (frozen spots) and adaptable components (hot spots), providing guidance for the development of mobile-based vital sign monitoring. FODA is utilized during the analysis activity. Design patterns are then implemented when creating class diagrams in the design activity. This study finding reveal 7 primary features and 18 sub-features essential that must be incorporated into the application framework. The framework includes 5 hot spots and 7 frozen spots, with the implementation of Strategy and Filter design patterns. In conclusion, the developed application framework represents a significant advancement in vital sign monitoring, particularly within mobile-based systems. This study emphasizing limitations in analysis and design phases. In future research, the focus will shift to the construction and stabilization phases, all crucial for refining the framework. Implementing framework in actual applications can aid in developing vital sign monitoring systems and potentially improving healthcare outcomes.
Deteksi Dini Diabetes Mellitus Menggunakan Algoritma Random Forest pada Data Klinis Rizky Ananda, Muhammad; Kurniawan, Rizky; Lestari Putri, Dewi
Journal of Computer Science and Information Technology Vol. 2 No. 1 (2026): Journal of Computer Science and Information Technology, March 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i1.410

Abstract

Early detection of Diabetes Mellitus is essential to reduce complications and improve patient outcomes. Machine learning approaches, particularly the Random Forest algorithm, have demonstrated promising performance in medical prediction tasks using clinical datasets. This study aims to analyze the effectiveness of the Random Forest algorithm for early diabetes detection based on clinical variables through a literature-based analytical research design. Data were synthesized from empirical findings reported in recent peer-reviewed studies published between 2022–2025. The analysis indicates that Random Forest consistently achieves high predictive performance, with reported accuracy ranging from 79.2% to 99.64% across multiple datasets and experimental configurations. Feature selection, data balancing techniques such as SMOTE and ADASYN, and hyperparameter optimization significantly improve model robustness. Comparative evaluation shows Random Forest outperforms several conventional machine learning classifiers in handling imbalanced medical datasets and identifying key risk factors. The findings highlight the algorithm’s reliability for clinical decision support systems and early screening applications. This study contributes a comprehensive synthesis of current evidence supporting Random Forest implementation in healthcare analytics and provides recommendations for future development of intelligent diabetes prediction systems.
Co-Authors Friska Abadi Lestari Putri, Dewi Muhammad Reza Faisal, Muhammad Reza Radityo Adi Nugroho Rizky Kurniawan Rudy Herteno