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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Advances in Applied Sciences ComEngApp : Computer Engineering and Applications Journal Jupiter TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Ilmu Komputer dan Agri-Informatika Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Scientific Journal of Informatics ICON-CSE Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Plastik Rekonstruksi Bioscientia Medicina : Journal of Biomedicine and Translational Research Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Generic Bioscientia Medicina : Journal of Biomedicine and Translational Research Jurnal Penelitian Pendidikan
Siti Nurmaini
University Of Sriwijaya
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Other

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Enhancing ultrasound image quality using deep structure of residual network Sapitri, Ade Iriani; Nurmaini, Siti; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Firdaus, Firdaus; Islami, Anggun; Tutuko, Bambang; Arum, Akhiar Wista
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3779-3794

Abstract

Ultrasonography, a medical imaging technique, is often affected by various types of noise and low brightness, which can result in low image quality. These drawbacks can significantly impede accurate interpretation and hinder effective medical diagnoses. Therefore, improving image quality is an essential aspect of the field of ultrasound systems. This study aims to enhance the quality of ultrasound images using deep learning (DL). The experiment is conducted using a custom dataset consisting of 2,175 infant heart ultrasound images collected from Indonesian hospitals, and the model is subsequently generalized using other datasets. We propose enhanced deep residual network combined convolutional neural networks (EDR-CNNs) to improve the image quality. After the enhancement process, our model achieved peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM) scores of 38.35 and 0.92 respectively, outperforming other methods. The benchmarking with other ultrasound medical images indicates that our proposed model produces good performance, as evidenced by higher PSNR, lower SSIM, a decrease in mean square error (MSE), and a lower contrast improvement index (CII). In conclusion, this study encapsulates the forthcoming trends in advancing low-illumination image enhancement, along with exploring the prevailing challenges and potential directions for further research.
Text clustering for analyzing scientific article using pre-trained language model and k-means algorithm Firdaus, Firdaus; Nurmaini, Siti; Yusliani, Novi; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Kunang, Yesi Novaria; Fachrurrozi, Muhammad; Armansyah, Risky
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9670

Abstract

Text clustering is a technique in data mining that can be used for analyzing scientific articles. In Indonesia-accredited journals, SINTA, there are two languages used, Indonesian and English. This is the first research focusing on clustering Indonesian and English texts into one cluster. In this research, bidirectional encoder representations from transformers (BERT) and IndoBERT are used to represent text data into fixed feature vectors. BERT and IndoBERT are pre-trained language models (PLMs) that can produce vector representations that take care of the position and context in a sentence. To cluster the articles, the K-Means algorithm is implemented. This algorithm has good convergence and adapts to the new examples, which helps in improved clustering performance. The best k-value in the K-Means algorithm is defined by using the silhouette score, the elbow method, and the Davies-Bouldin index (DBI). The experiment shows that the silhouette score can produce the most optimal k-value in clustering the articles, which has a mean score of 0.597. The mean score for the elbow method is 0.425, and for the DBI is 0.412. Therefore, the silhouette score optimizes the performance of PLMs and the K-Means algorithm in analyzing scientific articles to determine whether in scope or out of scope.
Multiclass instance segmentation optimization for fetal heart image object interpretation Syaputra, Hadi; Nurmaini, Siti; Partan, Radiyati Umi; Roseno, Muhammad Taufik
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4137-4150

Abstract

This research aims to develop a multi-class instance segmentation model for segmenting, detecting, and classifying objects in fetal heart ultrasound images derived from fetal heart ultrasound videos. Previous studies have performed object detection on fetal heart images, identifying nine anatomical classes. Further, these studies have conducted instance segmentation on fetal heart images for six anatomical classes. This research seeks to expand the scope by increasing the number of classes to ten, encompassing four main chambers left atrium (LA), right atrium (RA), left ventricle (LV), right ventricle (RV); four valves tricuspid valve (TV), pulmonary valve (PV), mitral valve (MV), and aortic valve (AV); one aorta (Ao), and the spine. By developing an instance segmentation method for segmenting ten anatomical structures of the fetal heart, this research aims to make a significant contribution to improving medical image analysis in healthcare. It also aims to pave the way for further research on fetal heart diseases using AI. The instance segmentation approach is expected to enhance the accuracy of segmenting fetal heart images and allow for more efficient identification and labeling of each anatomical structure in the fetal heart.
Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture Saputra, Tommy; Nurmaini, Siti; Roseno, Muhammad Taufik; Syaputra, Hadi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1976

Abstract

Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.
Peningkatan Kemampuan Membaca Nyaring Melalui Media Audio Visual Pada Peserta Didik Kelas IV Nadia Ayu Oktabella, nadia ayu oktabella; Arief Cahyo Utomo; Siti Nurmaini; Tri Undari
Jurnal Penelitian Pendidikan Vol 16 No 2 (2024): Jurnal Penelitian Pendidikan
Publisher : LPPM Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21137/jpp.2024.16.2.6

Abstract

Kemampuan peserta didik kelas IV SD Negeri Beskalan 14 yang belum tepat dalam membaca nyaring menjadi masalah utama dalam penelitian ini. Permasalahan tersebut membuat pembelajaran menjadi tidak efektif dan efisien. Hal tersebut menjadi salah satu dampak dari rendahnya kemampuan membaca nyaring peserta didik. Dalam hal ini peneliti menggunakan media audio visual untuk meningkatkan kemampuan membaca nyaring. Penelitian tindakan kelas (PTK) menjadi metode penelitian yang digunakan. Penelitian tindakan kelas ini dilakukan sebanyak 2 siklus dengan tujuan untuk meningkatkan kemampuan peserta didik membaca nyaring melalui media audio visual. Pada kegiatan pra siklus hasil yang didapatkan adalah peserta didik yang mampu membaca nyaring hanya sebesar 34,7%. Setelah tindakan siklus I, berubah menjadi 43,4%. Sedangkan setelah tindakan siklus II meningkat sebesar 86,9%, artinya kemampuan peserta didik dalam membaca nyaring mengalami peningkatan. Dari hasil penelitian, kesimpulan yang dapat diambil yaitu kemampuan peserta didik kelas IV dalam membaca nyaring dapat meningkat melalui penerapan media audio visual.
Deteksi Lesi Pra-Kanker Serviks Pada Citra Kolposkopi Menggunakan Convolutional Neural Network dengan Arsitektur YOLOv7 Nurqolbiah, Fatihani; Nurmaini, Siti; Saputra, Tommy
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7152

Abstract

Pre-cancerous cervical lesions detection is crucial in the diagnosis and analysis of medical images. Because visual observations are weak, computer-based detection is needed. This research proposes a pre-cancerous cervical lesion detection model using a Convolutional Neural Network with the YOLOv7 architecture, capable of accurately detecting these lesions. The data used was 913 colposcopy image data from 200 cases. The dataset is divided into training and testing data, resulting in a detection model for pre-cancerous cervical lesions. The model achieves an mAP of 91.9%, precision of 87.7%, recall of 96%, and an F1-score of 93%. The study demonstrates that the performance of YOLOv7 indicates the model's ability to accurately detect pre-cancerous lesions in the cervix.
YOLOv8 Based on Data Augmentation for MRI Brain Tumor Detection Passa, Rahma Satila; Nurmaini, Siti; Rini, Dian Palupi
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.45361

Abstract

Purpose: This research aimed to detect meningioma, glioma, and pituitary brain tumors using the YOLOv8 architecture and data augmentations.Methods: This research employed the YOLOv8 architecture with data augmentation techniques to detect meningioma, glioma, and pituitary brain tumors. The study collected a dataset of T1-weighted contrast-enhanced images. The dataset is used for training, validation, and testing. Preprocessing and augmentation are applied to enhance the training data.Result: After applying data augmentation techniques, the performance of all tumor types improves significantly. Meningioma, Glioma, and Pituitary tumors demonstrate increased Precision, Recall, and mAP50 scores compared to the results before augmentation. The findings highlight the effectiveness of the proposed method in enhancing the model's ability to accurately detect brain tumors in MRI scans. The research conducted both with and without augmentation followed a similar procedure: data collection was first undertaken, followed by preprocessing and with or without augmentation. Subsequently, the collected data was partitioned into training and validation subsets for training with the YOLOv8 architecture. Finally, the model's performance was evaluated through testing to assess its effectiveness in detecting brain tumors.Novelty: The novelty of this research lies in the YOLOv8 architecture and data augmentation techniques for MRI brain tumor detection. The study contributes to the existing knowledge by demonstrating the effectiveness of deep learning-based approaches in automating the detection process and improving the model's performance. By combining YOLOv8 with data augmentation, the proposed method enhances the model's accuracy and efficiency. The research findings emphasize the potential of this approach in facilitating early diagnosis and treatment planning, thereby improving patient care in the context of brain tumor detection. 
Augmentasi Data Sintetis Berbasis DCGAN untuk Citra Neoplasia Intraepitel Serviks (CIN) Nabilah, Aini; Nurmaini, Siti
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 12 No. 2 (2025)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.12.2.167-177

Abstract

One of the major challenges in medical imaging is the limited availability of high-quality datasets. To address this, Generative Artificial Intelligence (Generative AI) offers a promising solution by generating synthetic medical images to augment existing datasets. This study explores the application of Deep Convolutional Generative Adversarial Networks (DCGAN) for data augmentation in CIN imaging. Two training scenarios were implemented: DCGAN with manual data augmentation and another without manual augmentation. The image quality was evaluated using the Fréchet Inception Distance (FID). The results indicate that incorporating data augmentation improves the stability of training and enhances the quality of generated images FID scores of 2.21. In contrast, training DCGAN without manual augmentation resulted in a higher FID score of 2.52, indicating lower image quality. These findings highlight the effectiveness of DCGAN in medical image augmentation and its potential to enhance deep learning-based diagnostic models for cervical cancer detection or classification.
Co-Authors A. Darmawahyuni A. I. Sapitri Ade Iriani Sapitri Ade Iriani Sapitri Ade Iriani Sapitri Ade Silvia Ade Silvia Ade Silvia Handayani Aditya Aditya Aditya, Aditya Agung Juli Anda Agus Triadi Agus Triadi Agus Triadi Ahmad Zarkasi Ahmad Zarkasi Ahmad Zarkasi Ahmad Zarkasih Akhiar Wista Arum Andre Herviant Juliano Anggun Islami Anggun Islami Annisa Darmawahyuni Ardy Hidayat Arief Cahyo Utomo Armansyah, Risky Arnaldo, Muhammad Arum, Akhiar Wista Aulia Rahman Thoharsin B. Tutuko Bambang Tutuko Bambang Tutuko Bayu Wijaya Putra Benedictus Wicaksono Widodo Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Cindy Kesty Darmawahyuni, Annisa Darmawahyuni, Annisa Deris Stiawan Dewi, Kemala Dewi, Tresna Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dimas Budianto Dinda Lestarini Dodo Zaenal Abidin Dwi Mei Rita Sari Ekawati Prihatini Erliza Yuniarti Fachrudin Abdau Fahreza, Irvan Falah Yuridho Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus, Firdaus Firsandaya Malik, Reza Ganesha Ogi GITA FADILA FITRIANA Hadipurnawan Satria Hanif Habibie Supriansyah Huda Ubaya Huda Ubaya Huda Ubaya Husnawati Husnawati Husnawati Husnawati Husnawati Husni, Nyayu Latifah Husni, Nyayu Latifah Irfannuddin Irfannuddin Irsyadi Yani Irvan Fahreza Iryadi Yani Iryadi Yani, Iryadi Isdwanta, Rendy Islami, Anggun Jasmir Jasmir Jasmir Jasmir Jordan Marcelino Kemala Dewi Khairunnisa, Cholidah Zuhroh Krisna Murti Kurniawan, Anggy Tias Kurniawan, Anggy Tyas Legiran Legiran M. Hashim, Siti Zaiton M. N. Rachmatullah M. Naufal Rachmatullah Maharani, Masayu Nadila Marcelino, Jordan Masayu Nadila Maharani Mira Afrina Muhamad Akbar Muhammad Afif Muhammad Anshori Muhammad Arnaldo Muhammad Fachrurrozi Muhammad Fachrurrozi Muhammad Irham Rizki Fauzi Muhammad Naufal Rachmatullah Muhammad Naufal, Muhammad Muhammad Roriz Muhammad Taufik Roseno, Muhammad Taufik Muzakkie, Mufida Nabilah, Aini Nadia Ayu Oktabella, nadia ayu oktabella Novi Yusliani Nurqolbiah, Fatihani Nuswil Bernolian Nuswil Bernolian Nyayu Latifah Husni Nyayu Latifah Husni, Nyayu Latifah Oky Budiyarti Osvari Arsalan Passa, Rahma Satila Patiyus Agustiansyah PATIYUS AGUSTIANSYAH, PATIYUS Pola Risma PP Aditya, PP, Aditya, PP Pratama, Jimiria Putri Mirani Rachmamtullah, Muhammad Naufal Radiyati Umi Partan Radiyati Umi Partan Radiyati Umi Partan Radiyati Umi Partan, Radiyati Umi Rahma Satila Passa Rendy Isdwanta Renny Amalia Pratiwi Reza Firsandaya Malik Reza Firsandaya Malik Ria Nova Ricy Firnando Ricy Firnando Ricy Firnando Rizal Sanif Rizki Kurniati Rossi Passarella Sahat Pangidoan Samsuryadi Samsuryadi Saparudin Saparudin Saparudin, Saparudin Sapitri, Ade Iriani Saputra, Tommy Sari, Dwi Mei Rita Sarifah Putri Raflesia Sarifah Putri Raflesia, Sarifah Putri Sastradinata, Irawan Sigit Prasetyo Noprianto Siti Zaiton Siti Zaiton M. Hashim Soedjana, Hardi Siswo Sri Desy Siswanti Suci Dwi Lestari Suci Dwi Lestari Suhandono, Nugroho Sukemi Sukemi Sukemi Sukemi Sukemi Sukman Tulus Putra Sutarno Sutarno Syamsul Arifin Syaputra, Hadi Tio Artha Nugraha Tresna Dewi Tresna Dewi Tri Undari Triadi, Agus Triadi, Agus Varindo Ockta Keneddi Putra Velia Yuliza Winda Kurnia Sari Wisnu Adi Putra Yani, Iryadi Yesi Novaria Kunang Yurni Oktarina Zaqqi Yamani