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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JFA (Jurnal Fisika dan Aplikasinya) Journal of Information Systems Engineering and Business Intelligence Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Magister Pendidikan IPA Darmabakti Cendekia: Journal of Community Service and Engagements Indonesian Journal of Electrical Engineering and Computer Science Indonesian Applied Physics Letters ABSYARA: Jurnal Pengabdian Pada Masyarakat Medika Respati : Jurnal Ilmiah Kesehatan
Riries Rulaningtyas
Biomedical Engineering, Faculty Of Science And Technology, Universitas Airlangga, Surabaya, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Reconfiguration layers of convolutional neural network for fundus patches classification Wahyudi Setiawan; Moh. Imam Utoyo; Riries Rulaningtyas
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.1974

Abstract

Convolutional neural network (CNN) is a method of supervised deep learning. The architectures including AlexNet, VGG16, VGG19, ResNet 50, ResNet101, GoogleNet, Inception-V3, Inception ResNet-V2, and Squeezenet that have 25 to 825 layers. This study aims to simplify layers of CNN architectures and increased accuracy for fundus patches classification. Fundus patches classify two categories: normal and neovascularization. Data used for classification is MESSIDOR and Retina Image Bank that have 2,080 patches. Results show the best accuracy of 93.17% for original data and 99,33% for augmentation data using CNN 31 layers. It consists input layer, 7 convolutional layers, 7 batch normalization, 7 rectified linear unit, 6 max-pooling, fully connected layer, softmax, and output layer.
Detection of lung disease using relative reconstruction method in electrical impedance tomography system Lina Choridah; Riries Rulaningtyas; Lailatul Muqmiroh; Suprayitno Suprayitno; Khusnul Ain
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4940

Abstract

Lung disease can be diagnosed with the image-based medical devices, including radiography, computed tomography, and magnetic resonance imaging. The devices are very expensive and have negative effects. An alternative device is electrical impedance tomography (EIT). The advantages of EIT arelow cost, fast, real-time, and free radiation, so it is very appropriate to be used as a monitoring device. The relative reconstruction method has succeeded in producing functional images of lung anomalies by simulation. In this study, the relative reconstruction method was used to obtain functional images of four lungs conditions, namely a healthy person, patient with left lung tumor with organized left pleural effusion, one with pulmonary tuberculosis with right pneumothorax and one with pulmonary tuberculosis with left pleural effusion. The relative reconstruction method can be used to obtain functional images of an individual’s lung conditions by using expiratory-respiratory potential data with results that can distinguish between the lungs of a healthy person and a diseased patient, but the position of the lung disease may have less details. The potential data from comparison between the data of a patient and a healthy person can be used as a reference to obtain more accurate functional image information of lung disease.
Multimodal deep learning from sputum image segmentation to classify Mycobacterium tuberculosis using IUATLD assessment Saurina, Nia; Chamidah, Nur; Rulaningtyas, Riries; Aryati, Aryati
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9250

Abstract

Tuberculosis (TB) continues to be a major global health issue, especially in areas with limited resources where diagnostic tools are often insufficient. Traditional TB detection methods are slow and lack sensitivity, particularly for early-stage or low bacterial load cases. This study introduces a new multimodal deep learning model that integrates sputum image segmentation across RGB, hue, saturation, and value (HSV), and CIELAB color channels, using the YOLOv8 model for real-time detection and segmentation. The model uses the International Union Against Tuberculosis and Lung Disease (IUATLD) grading scale for accurate Mycobacterium tuberculosis (MTB) classification. Our approach shows high accuracy (92.24%) and precise forecasting (mean absolute percent error (MAPE) of 0.23%), greatly enhancing diagnostic speed and reliability. This research offers a novel method for classifying MTB using a multimodal deep learning model that integrates sputum image segmentation across RGB, HSV, and CIELAB color channels. By using the YOLOv8 model for real-time bounding box detection and segmentation, and the IUATLD grading scale for classification, our method achieves high accuracy and precision in identifying TB bacteria. Our findings indicate that this multimodal deep learning approach significantly improves diagnostic accuracy and speed, providing a reliable tool for early TB detection.
Co-Authors Abidah Alfi Maritsa Akila Firdausi Harahap Alfian Pramudita Putra Ama, Fadli Amillia Kartika Sari Amirul Amalia Amma, Fadli Anny Setijo Rahaju, Anny Setijo Aryati Aryati Budi Dwi Satoto Chandra Satria Arisgraha, Franky David Buntoro Kamandjaja Delfina Amarissa Sumanang Dewi, Rosa Amalia Eko Budi Khoendori Endah Purwanti Fitriyatul Qulub Franky Chandra Satria Arisgraha Hadiyyatan Waasilah Hanif Assyarify I Made Mas Dwiyana Prasetya Wibawa Imam Utoyo Indrawati Apriliyah Irfan Irhamni Katherine Katherine Katherine Kho Kezia Sarahsophia Immanuel Ryadi Khouliya Zalda Khusnul Ain Khusnul Ain Lina Choridah Miranda Syafira Widyananda Miranti Ayudya Kusumawardani Moh. Imam Utoyo Moh. Imam Utoyo Mohammad Kholil Muhammad Fadhel Maulama Muqmiroh, Lailatul Nafisa Rahmatul Laili Alami Nia Saurina Nita Lutfiyah Nur Chamidah Nur Rahma, Osmalina Osmalina Nur Rahma Pramudita, Alfian Pramulen, Aji Sapta Prihartini Widiyanti Quinolina Thifal Rahmatillah, Akif Rifai Chai Sayyidul Istighfar Ittaqillah Soegianto Soelistiono Solly Aryza Suprayitno Suprayitno Suryani Dyah Astuti Ummi Hanifah Wahyudi Setiawan Wahyudi Setiawan Wardhani, Inten Firdhausi Weni Endahing Warni Winarno Winarno, Winarno Yunardi, Riky Tri Yusrinourdi Muhammad Zuchruf