Article Per Year (5 Year)
p-Index From 2021 - 2026
1.633
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pilar Nusa Mandiri INTI Nusa Mandiri Jurnal Responsif : Riset Sains dan Informatika Jurnal Sosial dan Abdimas Journal Medical Informatics Technology SWAGATI: Journal of Community Service
Nurfalah, Ridan
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Economics, Econometrics & Finance Engineering Medicine & Pharmacology Public Health Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal Medical Informatics Technology

 1 
Comparison Algorithm on Machine Learning for Student Mental Health Data Nuarini, Sri; Siti Fauziah; Mayangky, Nissa Almira; Nurfalah, Ridan
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.18

Abstract

The COVID-19 pandemic has posed unparalleled difficulties, encompassing substantial repercussions on the emotional well-being of students. This study utilises machine learning methodologies to forecast the mental health condition of students during and following the pandemic. The dataset consists of 11 distinct attributes and a total of 101 data points, which have been gathered from multiple sources. The preprocessing stage encompasses the removal of unnecessary characteristics, handling missing data, and partitioning the dataset into separate subsets for training and validation purposes. This study utilises three machine learning algorithms, namely RF, KNN, and NB, in order to make predictions regarding the potential need for psychiatric support among students. These algorithms are carefully optimised to enhance their predictive capabilities. Evaluation metrics commonly used in several fields of study. The findings suggest that the KNN and RF algorithms had outstanding performance, but the Naïve Bayes algorithm exhibited satisfactory accuracy and a balanced trade-off between precision and recall. The optimised models have practical consequences that may be applied at educational institutions and inform policymakers. These implications include the ability to provide tailored interventions and support services specifically designed for students who are facing mental health difficulties as a result of the epidemic. Future research endeavours encompass the need for additional improvement of existing models and the fostering of interdisciplinary collaboration. This study provides significant contributions to the field by examining the utilisation of machine learning techniques in addressing the mental health needs of students both during and after the epidemic.
Image Analysis of Skin Diseases Using DenseNet-121 Architecture Putra, Mahesa; Pioni, Pioni; Rosalina, Alya; Aditya, Diyar; Azhari, Azidan Allen Deva; Hadianti, Sri; Nurfalah, Ridan
Journal Medical Informatics Technology Volume 3 No. 2, June 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i2.99

Abstract

Skin diseases such as dermatitis, psoriasis, and tinea often exhibit similar visual characteristics, which can lead to frequent errors in early diagnosis. Accurate diagnosis is critical, as each disease requires different treatment approaches. This study aims to develop an automated classification model for these three skin diseases using a deep learning approach based on the DenseNet-121 architecture, which consists of 121 layers designed to facilitate efficient feature reuse and gradient flow. The dataset consists of 300 labeled images, evenly distributed among the three disease classes. To enhance model generalization, preprocessing steps were applied, including data normalization and augmentation techniques such as image rotation (±20°), horizontal and vertical flipping, random zooming (range 0.8-1.2×), and brightness adjustment (±20%). The model was trained and validated using a stratified 5-fold cross-validation strategy. Experimental results demonstrated an overall classification accuracy of 94.59%, with high precision and recall scores across all classes. These results indicate the potential of using DenseNet-based deep learning models as decision support tools for early skin disease diagnosis. Further validation with larger datasets and clinical input from dermatologists is recommended to ensure reliability in real-world healthcare settings. Visual comparison through Grad-CAM heatmaps was also conducted to enhance interpretability and validate model focus on relevant skin features.
Co-Authors Aditya, Diyar Ai Ilah Warnilah Azhari, Azidan Allen Deva Daniati Uki Eka Saputri Fachry Abda El Rahman Fatimah, Winda Astariyah Kusumayudha, Mochammad Rizky M. Rangga Ramadhan Saelan Mayangky, Nissa Almira Muhammad Faittullah Akbar Nuarini, Sri Nugraha, Fitra Septia Pioni, Pioni Putra, Mahesa Ramadhan, Mohamad Ragil Rosalina, Alya Ruhiyat, Yayat Siti Fauziah Siti Masturoh siti nurlela Sri Hadianti SRI RAHAYU