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All Journal JEECS (Journal of Electrical Engineering and Computer Sciences)
Adithya Kusuma Whardana
Tanri Abeng University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Detection Diabetic Retinopathy with Supervised Learning Adithya Kusuma Whardana; Parma Hadi Rantelinggi
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 8 No. 2 (2023): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v8i2.7

Abstract

Diabetic retinopathy is a common complication that occurs in people with diabetes mellitus. Diabetic retinopathy damage is characterized in the blood vessel system in the layer at the back of the eye, especially in tissues that respond to light. This research aims to detect diabetic retinopathy early by using SVM and Random forest. SVM is a classification technique that divides the input space into two classes. Random Forest is a supervised learning algorithm that utilizes a collection of decision trees trained using the bagging method. This research uses datasets from diaretdb1 and messidor to evaluate the performance of both methods. The diaretdb1 dataset consists of 178 data points with the diagnosis of Proliferative Diabetic Retinopathy and Non-Diabetic Retinopathy. In addition, the messidor dataset consists of 105 data points with the diagnosis of Diabetic Retinopathy and Non-Diabetic Retinopathy. Experimental results on the diaretdb1 dataset showed that SVM achieved 88% accuracy, while Random Forest achieved 91% accuracy. Similarly, on the messidor dataset, SVM achieved 80% accuracy, while Random Forest achieved 85% accuracy.
Deep Learning-Based Road Traffic Density Analysis and Monitoring Using Semantic Segmentation Adithya Kusuma Whardana; Parma Hadi Rentelinggi
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 1 (2024): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i1.1

Abstract

Due to factors such as a growing population, more people using private vehicles, and outdated transportation infrastructure, Jakarta, the capital city of Indonesia, suffers from chronic traffic congestion. The environment, citizens' safety, productivity, and quality of life are all negatively impacted by these interruptions. In response to these difficulties, this study proposes a novel method for traffic monitoring. By combining YOLOv5, optical flow, and recurrent neural networks (RNN) with image processing and artificial neural networks, a unified traffic monitoring system can be achieved. We went with YOLOv5 because of how well it identifies various automobiles. The number of vehicles is counted between video frames using Optical Flow, and then the traffic density is classified using RNN. With an accuracy of 87% following testing, RNN was clearly a winner when it came to vehicle density classification. The goals of this research are to lessen the societal and environmental toll of traffic congestion, increase our knowledge of and ability to control Jakarta's traffic, and lay the groundwork for the creation of more advanced traffic monitoring systems. The growing traffic issues in the nation's capital are anticipated to be alleviated with this strategy.
Hemorrhage Segmentation on Retinal Images for Early Detection of Diabetic Retinopathy Hendar Hermawan; Adithya Kusuma Whardana
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.5

Abstract

Diabetes mellitus is a chronic disorder that can lead to serious complications, including diabetic retinopathy, which affects the eyes and can potentially lead to blindness. Rapid identification of diabetic retinopathy is crucial to facilitate quicker and more efficient treatment for patients. This study aims to segment hemorrhages in retinal images using the Laplacian of Gaussian (LoG) approach in conjunction with threshold-based segmentation and analysis of region properties, including eccentricity. The LoG approach is utilized for its ability to detect edges, features, and abrupt variations in image intensity, thereby optimally highlighting the bleeding lesion area. With accurate segmentation, it is hoped that early detection and monitoring of diabetic retinopathy can be improved. This research uses the IDRiD, DR_2000, and DIARETDB1 datasets, recommending the use of IDRiD and DIARETDB1 for optimal results. Through this methodology, it is expected to make a significant contribution to reducing the risk of blindness in diabetes patients.
Alzheimer Disease Prediction Through Guided Predictive Modeling With Machine Learning Mahesa Pramudya Alfayat; Adithya Kusuma Whardana
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 1 (2025): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i1.5

Abstract

Alzheimer's disease is a progressive neurodegenerative disorder characterized by the accumulation of misfolded brain proteins, especially beta-amyloid plaques, resulting in cognitive deterioration and memory impairment. However, there has been no effort of early detection to facilitate prompt intervention and preventive strategies. This research fulfills this essential need by employing the Open Access Series of Imaging Studies (OASIS) dataset supplied by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The research employs the Cross-industry Standard Process for Data Mining (CRISP-DM) methodology to create and assess a classification model utilizing Artificial Neural Networks (ANN). The model attains a remarkable accuracy rate of 96%, exhibiting elevated precision, recall, and F1-scores across all categories. A 10-fold cross-validation technique was utilized to assess the model's robustness, resulting in an average accuracy of 90.7%. These findings underscore the efficacy of artificial neural networks in identifying Alzheimer's disease in its initial phases. This research utilizes advanced data mining approaches to improve predictive capacities and highlights the promise of machine learning in tackling intricate healthcare issues.
Co-Authors Hendar Hermawan Mahesa Pramudya Alfayat Parma Hadi Rantelinggi Parma Hadi Rentelinggi