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All Journal TEKNIK INFORMATIKA Jurnal Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Khatulistiwa Informatika Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Techno Nusa Mandiri : Journal of Computing and Information Technology Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Informasi dan Teknologi Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal of Applied Data Sciences Jurnal Responsif : Riset Sains dan Informatika International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Sosial dan Abdimas INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Journal Medical Informatics Technology SWAGATI: Journal of Community Service
Sri Hadianti
Department Of Information System, STMIK Nusa Mandiri, Jl. Raya Jatiwaringin No 2, Cipinang Melayu, Makasar, Jakarta Timur 13620|STMIK Nusa Mandiri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Journal Medical Informatics Technology

 1 
Glaucoma Detection in Fundus Eye Images using Convolutional Neural Network Method with Visual Geometric Group 16 and Residual Network 50 Architecture Nugraha, Chandra; Hadianti, Sri
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.7

Abstract

Glaucoma is an eye disease usually caused by abnormal eye pressure. One of the causes of abnormal eye pressure is blockage of fluid flow, which if detected too late can lead to blindness. Glaucoma can be identified by examining specific areas on the retina fundus image. The aim of this study is to detect positive and negative glaucoma in fundus images. The image data was obtained from the glaucoma_detection dataset, consisting of 520 images, including 134 glaucoma-infected images and 386 normal images. This study uses the Convolutional Neural Network (CNN) method with Visual Geometric Group-16 (VGG-16) and Residual Network-50 (ResNet-50) architectures. The research and testing results using the VGG-16 architecture obtained an accuracy rate of 78%, while using the ResNet-50 architecture obtained an accuracy rate of 80%.
Optimization of The Machine Learning Approach using Optuna in Heart Disease Prediction Hadianti, Sri; Kodri, Wan Ahmad Gazali
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.v1i3.15

Abstract

Heart disease prediction is a critical area in healthcare, as early identification and accurate assessment of cardiovascular risks can lead to improved patient outcomes. This study explores the application of machine learning techniques for predicting heart disease. Various data attributes, including medical history, clinical measurements, and lifestyle factors, are utilized to develop predictive models. A comprehensive analysis of different machine learning algorithms is conducted to determine their efficacy in classification tasks. The dataset used for experimentation is sourced from a diverse patient population, enhancing the generalizability of the findings. Through rigorous evaluation and validation, the study aims to identify the most suitable machine learning approach for effectively predicting heart disease. The results highlight the potential of machine learning as a valuable tool in assisting healthcare professionals in making informed decisions and providing personalized care to individuals at risk of heart disease
Relevance of e-Health Needs and Usage in Indonesia Chairul, Yasrizal; Aziz, Faruq; Hadianti, Sri
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.20

Abstract

The eHealth application can be used for healthcare, supervision, literature, education, and research. It is a cost-efficient and secure application based on information and communication technology for the health and medical fields. The use of Information and Communication Technology (ICT) as an infrastructure or medium that connects hospitals and health centers using the eHealth electronic health application is the key problem facing the implementation of eHealth on a worldwide scale. eHealth is an ICT-based application for the healthcare industry and one of the Action Plans of the World Summit on the Information Society (WSIS) Geneva 2003. The goal of using the eHealth app is to increase patient access, medical process efficiency, effectiveness, and process quality. This covers the administration of medical services provided by hospitals, clinics, health centers, medical professionals (including therapists and doctors), laboratories, pharmacies, and insurance
Identification of Potato Plant Pests Using the Convolutional Neural Network VGG16 Method Hadianti, Sri; Aziz, Faruq; Nur Sulistyowati, Daning; Riana, Dwiza; Saputra, Ridwan; Kurniawantoro
Journal Medical Informatics Technology Volume 2 No. 2, June 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i2.37

Abstract

Pests are one of the main challenges in potato cultivation that can significantly reduce crop yields. Therefore, quick and accurate pest identification is crucial for effective pest control. This research aims to develop a pest identification system for potato plants using the Convolutional Neural Network (CNN) method with the VGG16 architecture. The dataset used consists of images of pests commonly found on potato plants. After the labeling process, these images were used to train the CNN VGG16 model. The research results show that the CNN VGG16 method can identify types of pests with an accuracy rate of 73%. The results serve as a reference to help farmers and agricultural practitioners detect the presence of pests earlier and take the necessary actions to reduce crop losses.
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 Achmad Baroqah Pohan Achmad Rifai Adilah Purnamasari Aditya Gusti Aditya, Diyar Akmaludin Akmaludin, Akmaludin Aldi Jakaria Arina Selawati Azhari, Azidan Allen Deva Aziz, Faruq Chairul, Yasrizal Chandra Nugraha, Chandra Daniati Uki Eka Saputri Dwiza Riana Dwiza Riana Dwiza Riana Dwiza Riana Dwiza Riana Dwiza Riana Fani Nurona Cahya Fiqi Pranastara Frieyadie Galih Widagdo Heckel Sungkar Iftina Salya Rosa Izni Nur Karimah Jajang Jaya Purnama Kodri, Wan Ahmad Gazali Kurniawantoro Kusumayudha, Mochammad Rizky Laela Kurniawati Lestari Yusuf Mayangky, Nissa Almira Mochamad Rizky Kusumayudha Mochammad Rizky Kusumayudha Mohamad Ragil Ramadhan Muhamad Hasan Muhammad Faitullah Akbar Nanik Wuryani Nila Hardi Nugraha, Fitra Septia Nur Fiyah Nur Sulistyowati, Daning Nurfalah, Ridan Nurul Jannah Salsabila Pioni, Pioni Putra, Mahesa Putri Permata Sari Rafly Pratama Resti Kartika Dewi Ridan Nurfalah Ridan Nurfalah Riski Febrianto Rizka Dahlia Rizky Ade Safitri Rosalina, Alya Rousyati Rousyati Rudini, Edwin Saputra, Ridwan Siti Nurdiani Siti Nurlela siti nurlela Sofiyatul Mu’minah Sri Rahayu SRI RAHAYU Sugiyanti Sugiyanti Sulistyowati, Daning Nur Widagdo, Galih Winda Astariyah Fatimah Windu Gata WITRIANA ENDAH PANGESTI Yutri Amelia