Article Per Year (5 Year)
p-Index From 2021 - 2026
1.42
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Yogi Ramadhani
Jenderal Soedirman University
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Hippocampus’s volume calculation on coronal slice’s for strengthening the diagnosis of Alzheimer’s Retno Supriyanti; Yogi Ramadhani; Eko Wahyudi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 1: February 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i1.20746

Abstract

Alzheimer’s is one of the most common types of dementia in the world. Although not a contagious disease, this disease has many impacts, especially in socio-economic life. In diagnosing Alzheimer’s and using interview techniques, physical examination methods are also used, namely using an magnetic resonance imaging (MRI) machine to get a clear image of the patient’s brain condition, with a focus on the hippocampus and ventricular area. In this paper, we discuss the calculation of the volume of the hippocampus, especially the coronal slice, to provide information to doctors in making decisions on diagnosing the severity of Alzheimer’s. Using the basis of volume calculations, we made a 3D visualization reconstruction of the coronal hippocampus slice area in order to make it easier for doctors to analyze the condition of the hippocampus area, which in the end will be used as a recommendation in the classification of the severity of Alzheimer’s. Our experimental results show, the lower the severity, the bigger the volume, the more slices, and the longer the counting time.
Deep learning-based cervical cancer detection via colposcopy images integrated into an Android mobile application Retno Supriyanti; Arsil Kultura Anzil; Yogi Ramadhani; Suroso Suroso; Wahyu Widanarto; Muhammad Alqaaf; Kartika Dwi Hapsari; Futiat Diana Kartika
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp338-349

Abstract

Cervical cancer is a form of cancer that develops in the cells of the cervix, the lower part of the uterus that connects the uterus to the vagina. Early detection is essential for improving the chances of recovery from cervical cancer. One method for early detection is colposcopy image analysis, a medical procedure that examines the cervix and captures images for evaluation. These images were analyzed to observe color changes after the visual inspection with acetic acid (VIA) process. However, this analysis requires experienced and specially trained medical personnel. To address this challenge, a system that can automatically classify cervical cancer images is needed. Therefore, researchers proposed designing and developing an Android mobile application to enable early detection of cervical cancer using the convolutional neural network (CNN) algorithm. The CNN model was tested using test data to evaluate its performance. The optimized CNN model utilizing the ResNet50 architecture achieved 86% test accuracy, 85% precision, and 87% recall. The test results indicate that the model's accuracy is consistent before and after its implementation on the mobile application, confirming the effectiveness of both the model and its implementation as diagnostic tools.
Co-Authors Ahmad Fariz Alfin Chrisanty Anugerah Kevin Marchel Ariep Soelaiman Setiadi Arsil Kultura Anzil Eko Murdyantoro Eko Wahyudi Elvin Pranata Fibra R. Firmanda Futiat Diana Kartika Haris B. Widodo Haris Budi Widodo Haris Budi Widodo Kartika Dwi Hapsari Muhammad Alqaaf Muhammad Alqaaf Muhammad Rifqi Kurniawan Pangestu F. Wibowo Retno Supriyanti Suroso Suroso Suwitno Suwitno Syadzwina Luke Dzihniza Teddy Septiana Tutik Ida Rosanti Wahyu Siswandari Wahyu Widanarto