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All Journal Syntax: Journal of Software Engineering, Computer Science and Information Technology Journal Medical Informatics Technology
Iin Karmila Septiani
Universitas Majalengka
Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health

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Mobile Web App Development for Diabetic Foot Screening Using Inlow’s 60-Second Screen with Automated Risk Classification Suhendri; Wildan Zhilal Manafi; Bayu Reviyadi; Sri Rahayu; Iin Karmila Septiani; Mita Nurmala
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.152

Abstract

Diabetic foot complications constitute a major contributor to preventable lower-extremity amputation, yet primary care screening remains inconsistent due to the absence of integrated digital tools implementing validated clinical protocols. This study presents the design, implementation, and system-centric evaluation of Podiatrix, a mobile web application that operationalizes Inlow's 60-Second Diabetic Foot Screen through an automated, condition-based clinical workflow. Unlike existing tools that address isolated screening criteria, Podiatrix implements all seven Inlow criteria within a unified five-step wizard and applies a deterministic hierarchical classification engine that directly mirrors the original Inlow protocol logic rather than relying on fixed score thresholds. The system was evaluated using three complementary methods: black-box testing across 50 simulated clinical scenarios, Nielsen's heuristic usability evaluation conducted by three independent evaluators, and performance load testing using Apache JMeter under concurrent user conditions. Results demonstrated 100% classification accuracy (50/50 scenarios) matching manual Inlow protocol interpretation, an average heuristic severity score of 1.15 out of 4 indicating high usability, and a mean response time of 820 ms with less than 1% error rate under 100 concurrent users. These findings confirm that Podiatrix provides a computationally robust, highly usable, and scalable digital infrastructure that lays the groundwork for future prospective clinical trials in primary care and community health settings.
ANALISIS LITERATUR SISTEMATIS TERHADAP METODE IMAGE DENOISING BERBASIS DEEP LEARNING UNTUK COMPUTER VISION Amelia Putri; Iin Karmila Septiani
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 7, No 1 (2026): Juni 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v7i1.8865

Abstract

ABSTRAKPenggunaan citra digital dalam berbagai aplikasi Computer Vision seringkali terkendala oleh kehadiran noise yang menurunkan kualitas informasi visual. Makalah ini menyajikan Analisis Literatur Sistematis (SLR) terhadap perkembangan metode image denoising berbasis deep learning. Proses pencarian artikel dilakukan secara terstruktur melalui database yang terindeks Scopus dengan mengadaptasi protokol PRISMA. Melalui analisis terhadap 40 literatur kunci yang sepenuhnya bersumber dari database Scopus, ditemukan pergeseran signifikan dari metode yang membutuhkan data bersih (supervised) menuju pendekatan yang lebih fleksibel seperti Noise2Noise dan blind denoising untuk menangani noise pada dunia nyata. Hasil tinjauan ini memberikan gambaran komprehensif mengenai tren arsitektur, dataset benchmark, serta tantangan dalam mencapai efisiensi komputasi untuk restorasi citra resolusi tinggi.Kata kunci— Image Denoising, Deep Learning, Systematic Literature Review, Computer Vision, Scopus, PRISMA.ABSTRACT The use of digital imagery in various Computer Vision applications is often hindered by the presence of noise, which degrades visual information quality. This paper presents a Systematic Literature Review (SLR) on the development of deep learning-based image denoising methods. The article search process was structured through Scopus-indexed databases using the PRISMA protocol. Through an analysis of 40 key literatures completely sourced from the Scopus database, a significant shift was identified from supervised methods requiring clean data toward more flexible approaches, such as Noise2Noise and blind denoising, to handle real-world noise. The results of this review provide a comprehensive overview of architectural trends, benchmark datasets, and the challenges in achieving computational efficiency for high-resolution image restoration.Keyword— Image Denoising, Deep Learning, Systematic Literature Review, Computer Vision, Scopus, PRISMA.
Co-Authors Amelia Putri Bayu Reviyadi Mita Nurmala Sri Rahayu Suhendri Wildan Zhilal Manafi