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All Journal Computatio : Journal of Computer Science and Information Systems Jurnal Keperawatan dan Kesehatan (JKK)
Faqih Dzulqarnain, Muhammad
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Computer Science & IT Health Professions Medicine & Pharmacology Neuroscience Nursing Public Health

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A Smart and Budget-Friendly Android Application for Motorcycle Safety and Engine Control System Rakhmadi, Aris; Ihsan Adi, Mahfud; Ary Prasetya, Dedi; Muhammad; Faqih Dzulqarnain, Muhammad
Computatio : Journal of Computer Science and Information Systems Vol. 9 No. 1 (2025): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v9i1.33870

Abstract

Motorcycle theft remains a significant security issue, necessitating an affordable and effective solution. This study presents a cost-effective Android-based motorcycle security and engine control system using Arduino, integrating Bluetooth communication for remote operation. The system enables users to start the engine, activate an alarm, and monitor security conditions via a custom Android application, enhancing both security and convenience. The hardware consists of an Arduino Uno microcontroller, Bluetooth HC-05 module, and a four-channel relay module, allowing secure wireless control. System testing demonstrated stable Bluetooth connectivity up to 10 meters, low power consumption (150 mW for Bluetooth HC-05), and reliable command execution. A key advantage is its low cost, totaling approximately 223,000 IDR (~ USD 15), making it significantly more affordable than conventional GPS-based security systems. Results confirm that the proposed system offers a low-cost yet effective alternative to traditional security methods, providing real-time monitoring, user-friendly operation, and enhanced theft prevention. However, limitations such as Bluetooth range constraints and potential security risks highlight areas for improvement. Future enhancements could include biometric authentication, GPS tracking, and cloud-based monitoring to strengthen security and scalability. By leveraging affordable components and smartphone integration, this study contributes to developing innovative, accessible, and cost-efficient motorcycle security solutions, improving vehicle protection for a wider audience.
IMPLEMENTASI DEEP LEARNING DALAM SISTEM DETEKSI MANDIRI BERBASIS MOBILE UNTUK LUKA DIABETIK GUNA MENDUKUNG KESEHATAN MASYARAKAT Hermanto, Hermanto; Adhi Prasetya, Irwan; Faqih Dzulqarnain, Muhammad; Wulandari, Mira; Sujatmiko, Wandi; Habibi, Muhammad
Jurnal Keperawatan dan Kesehatan Vol 17 No 1 (2026): Jurnal Riset Keperawatan dan Kesehatan
Publisher : Institut Teknologi dan Kesehatan Muhammadiyah Kalimantan Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54630/jk2.v17i1.586

Abstract

Diabetic foot ulcers affect 15-25% of diabetic patients globally, causing significant morbidity, healthcare costs, and amputation risks. Early detection is crucial for preventing severe complications, yet limited access to specialized healthcare services, especially in remote areas, creates substantial barriers to timely wound management. This study developed a mobile-based self-detection system using deep learning technology to enable early diabetic wound identification and improve healthcare accessibility for patients in resource-limited settings. A Convolutional Neural Network with MobileNetV2 architecture was trained on over 5,000 diabetic wound images categorized into five classes: healthy skin and Wagner grades 1-4 ulcers. The cross-platform mobile application features AI-based wound detection, diagnosis history tracking, educational content, and integrated telemedicine consultation. Clinical validation compared AI predictions with three certified wound care specialists across 500 cases, while usability testing involved 100 diabetic patients.The model achieved 92.4% accuracy with 94.2% sensitivity and 91.7% specificity. For high-grade ulcers, performance improved to 96.8% sensitivity with 1.8-second processing time per image. Clinical validation showed substantial agreement with specialists (Cohen's kappa = 0.89). The application scored 82.5/100 on System Usability Scale with 88.5% user satisfaction and 91.2% willing to recommend it. This mobile-based deep learning system demonstrates high accuracy and clinical reliability for diabetic wound self-detection, successfully bridging healthcare gaps for underserved populations and showing significant potential for early intervention and healthcare cost reduction.
Co-Authors Adhi Prasetya, Irwan Aris Rakhmadi Ary Prasetya, Dedi Hermanto Hermanto Ihsan Adi, Mahfud Muhammad Muhammad Habibi Sujatmiko, Wandi Wulandari, Mira