Article Per Year (5 Year)
p-Index From 2020 - 2025
0.778
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization Jurnal Teknologi Terpadu Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Prasetyo, Simeon Yuda
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mathematics

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Perbandingan Model Machine Learning pada Klasifikasi Tumor Otak Menggunakan Fitur Discrete Cosine Transform Prasetyo, Simeon Yuda; Nabiilah, Ghinaa Zain
Jurnal Teknologi Terpadu Vol 9 No 1 (2023): Juli, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i1.605

Abstract

Brain tumors are abnormal tissue growths characterized by excessive cell growth in certain brain parts. One of the reliable techniques currently available to identify brain tumors is using Magnetic Resonance Imaging (MRI) scans. The scanned MRI images are monitored and examined for tumor detection by a specialist. Developing more effective and efficient tools to help medical professionals identify brain tumors is urgent as the number of people suffering from brain tumors soars, and the death rate will reach 18,600 in 2021. In previous research, machine learning-based models demonstrated the ability to detect brain tumors with a classification accuracy of 92%, and this result is reliable. We computationally tested several hyperparameters using publicly available MRI datasets to obtain the most reliable binary classification accuracy in MRI brain images. A high level of model accuracy is achieved by testing various existing machine-learning model architectures and inserting a feature map extracted from the Discrete Cosine Transform (DCT). Classification of MRI images achieved the highest accuracy on test data at 93% using the Support Vector Machine (SVM) model.
Prediksi Gagal Jantung Menggunakan Artificial Neural Network Prasetyo, Simeon Yuda
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 13 No 1 (2023): Maret 2023
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v13i1.379

Abstract

Cardiovascular disease or heart problems are the leading cause of death worldwide. According to WHO (World Health Organization) every year there are more than 17.9 million deaths worldwide. In previous studies, there have been many studies related to the application of machine learning to predict heart failure and obtained quite good results, ranging from 85 percent to 90 percent, with sophisticated models optimized using neural networks. In this research, experiments were carried out using similar architectures based on the state of the art from previous research, namely Artificial Neural Networks by conducting several hyperparameter tests, namely the number of hidden layers and the number of neuron units in the hidden layer. Based on the test results, the Artificial Neural Network model get the best results by implementing 2 hidden layers with 15 units of neurons in the first hidden layer and 10 units of neurons in the second hidden layer. This model get accuracy on data testing of 92,032% and AUC of 93%.
Overcoming Overfitting in CNN Models for Potato Disease Classification Using Data Augmentation Prasetyo, Simeon Yuda
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11840

Abstract

Classification of diseases in potato plants is crucial for agriculture to ensure quality and yield. Potatoes, being staple foods worldwide, are vulnerable to diseases that cause significant production losses. Early and accurate disease identification is essential. This study evaluates the impact of data augmentation on reducing overfitting in deep learning models for potato disease classification. Various CNN architectures, including VGG16, VGG19, Xception, and InceptionV3, were compared in transfer learning and fine-tuning phases. The "Potato Disease Dataset", consisting of 451 images across seven classes, was used. The dataset was split into training, validation, and test sets, and augmentation increased the training set from 360 to 2160 images. The results indicate that models trained with augmented data exhibited improved performance in terms of accuracy, precision, recall, and F1-scores compared to those trained without augmentation. The learning curves show that data augmentation helps in reducing overfitting and enhancing model stability. Data augmentation is crucial for developing robust deep learning models for potato disease classification. Future work will explore advanced augmentation techniques and other architectures to enhance model performance.
Color and Attention for U : Modified Multi Attention U-Net for a Better Image Colorization Nathanael, Oliverio Theophilus; Prasetyo, Simeon Yuda
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1828

Abstract

Image colorization is a tedious task that requires creativity and understanding of the image context and semantic information. Many models have been made by harnessing various deep learning architectures to learn the plausible colorization. With the rapid discovery of new architecture and image generation techniques, more powerful options can be explored and improved for image colorization tasks. This research explores a new architecture to colorize an image by using pre-trained embeddings on U-Net combined with several attention modules across the model. Using embeddings from a pre-trained classifier provides a high-level feature extraction from the image. Conversely, multi-attention gives a little taste of image segmentation so that the model can distinguish objects in the image and further enhance the additional information given by the pre-trained embeddings. Adversarial training is also utilized as a normalization to make the generated image more realistic. This research preferred Parch GAN over base GAN as the discriminator model to ensure that the colorization has a consistent quality across all patches.  The study shows that this U-Net modification can improve the generated image quality compared to a normal U-Net. The proposed architecture reaches an FID of 48.6253, SSIM of 0.8568, and PSNR of 19.7831 by only training it for 25 epochs; hence, this research offers another view of image colorization by using modules that are often used for image segmentation tasks. 
Co-Authors Nabiilah, Ghinaa Zain Nathanael, Oliverio Theophilus