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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik ITS Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Journal of Urban Society´s Arts Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi SMATIKA Journal of Animation & Games Studies EMITTER International Journal of Engineering Technology Jurnal Teknologi dan Sistem Komputer Journal of Information Technology and Computer Science Knowledge Engineering and Data Science Berkala Arkeologi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SENTIA 2015 Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Mnemonic Jurnal Informatika dan Sistem Informasi Journal of Innovation Information Technology and Application (JINITA) JAREE (Journal on Advanced Research in Electrical Engineering) JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati Berkala Arkeologi
Eko Mulyanto Yuniarno
Dapartemen Teknik Komputer Iinstitut Teknologi Sepuluh Nopember
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : EMITTER International Journal of Engineering Technology

 1 
3D Visualization for Lung Surface Images of Covid-19 Patients based on U-Net CNN Segmentation FX Ferdinandus; Esther Irawati Setiawan; Eko Mulyanto Yuniarno; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.709

Abstract

The Covid-19 infection challenges medical staff to make rapid diagnoses of patients. In just a few days, the Covid-19 virus infection could affect the performance of the lungs. On the other hand, semantic segmentation using the Convolutional Neural Network (CNN) on Lung CT-scan images had attracted the attention of researchers for several years, even before the Covid-19 pandemic. Ground Glass Opacity (GGO), in the form of white patches caused by Covid-19 infection, is detected inside the patient’s lung area and occasionally at the edge of the lung, but no research has specifically paid attention to the edges of the lungs. This study proposes to display a 3D visualization of the lung surface of Covid-19 patients based on CT-scan image segmentation using U-Net architecture with a training dataset from typical lung images. Then the resulting CNN model is used to segment the lungs of Covid-19 patients. The segmentation results are selected as some slices to be reconstructed into a 3D lung shape and displayed in 3D animation. Visualizing the results of this segmentation can help medical staff diagnose the lungs of Covid-19 patients, especially on the surface of the lungs of patients with GGO at the edges. From the lung segmentation experiment results on ten patients in the Zenodo dataset, we have a Mean-IoU score = of 76.86%, while the visualization results show that 7 out of 10 patients (70%) have eroded lung surfaces. It can be seen clearly through 3D visualization.
Modified Deep Pattern Classifier on Indonesian Traditional Dance Spatio-Temporal Data Mulyanto, Edy; Yuniarno, Eko Mulyanto; Hafidz, Isa; Budiyanta, Nova Eka; Priyadi, Ardyono; Hery Purnomo, Mauridhi
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.832

Abstract

Traditional dances, like those of Indonesia, have complex and unique patterns requiring accurate cultural preservation and documentation classification. However, traditional dance classification methods often rely on manual analysis and subjective judgment, which leads to inconsistencies and limitations. This research explores a modified deep pattern classifier of traditional dance movements in videos, including Gambyong, Remo, and Topeng, using a Convolutional Neural Network (CNN). Evaluation model's performance using a testing spatio-temporal dataset in Indonesian traditional dance videos is performed. The videos are processed through frame-level segmentation, enabling the CNN to capture nuances in posture, footwork, and facial expressions exhibited by dancers. Then, the obtained confusion matrix enables the calculation of performance metrics such as accuracy, precision, sensitivity, and F1-score. The results showcase a high accuracy of 97.5%, indicating the reliable classification of the dataset. Furthermore, future research directions are suggested, including investigating advanced CNN architectures, incorporating temporal information through recurrent neural networks, exploring transfer learning techniques, and integrating user feedback for iterative refinement of the model. The proposed method has the potential to advance dance analysis and find applications in dance education, choreography, and cultural preservation.
Improving 3D Human Pose Orientation Recognition Through Weight-Voxel Features And 3D CNNs Riansyah, Moch. Iskandar; Putra, Oddy Virgantara; Rahmanti, Farah Zakiyah; Priyadi, Ardyono; Wulandari, Diah Puspito; Sardjono, Tri Arief; Yuniarno, Eko Mulyanto; Hery Purnomo, Mauridhi
EMITTER International Journal of Engineering Technology Vol 13 No 1 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i1.847

Abstract

Preprocessing is a widely used process in deep learning applications, and it has been applied in both 2D and 3D computer vision applications. In this research, we propose a preprocessing technique involving weighting to enhance classification performance, incorporated with a 3D CNN architecture. Unlike regular voxel preprocessing, which uses a zero-one (binary) approach, adding weighting incorporates stronger structural information into the voxels. This method is tested with 3D data represented in the form of voxels, followed by weighting preprocessing before entering the core 3D CNN architecture. We evaluate our approach using both public datasets, such as the KITTI dataset, and self-collected 3D human orientation data with four classes. Subsequently, we tested it with five 3D CNN architectures, including VGG16, ResNet50, ResNet50v2, DenseNet121, and VoxNet. Based on experiments conducted with this data, preprocessing with the 3D VGG16 architecture, among the five architectures tested, demonstrates an improvement in accuracy and a reduction in errors in 3D human orientation classification compared to using no preprocessing or other preprocessing methods on the 3D voxel data. The results show that the accuracy and loss in 3D object classification exhibit superior performance compared to specific preprocessing methods, such as binary processing within each voxel.
Co-Authors Aditya Nur Ikhsan Soewidiatmaka Agung Dewa Bagus Soetiono Agung Wicaksono Agustinus Bimo Gumelar Ahmad Zaini Alan Luthfi Ali Sofyan Kholimi Alwali, Muhammad Anang Kukuh Adisusilo Andreas Agung Kristanto, Andreas Agung Ardyono Priyadi Arief Kurniawan Arik Kurniawati Aris Widayati Atyantagratia Vidyasmara Daryanto Bambang Purwantana Beny Yulkurniawan Victorio Nasution Beny Yulkurniawan Victorio Nasution Boedinoegroho, Hanny Citra Ratih Prameswari Diah Puspito Wulandari Endang Setyati Endang Sri Rahayu Enggartiasto Faudi Ristyawan Esther Irawati Setiawan Evi Septiana Pane, Evi Septiana F.X. Ferdinandus Fakih, Muhammad Fadli Fandiantoro, Dion Hayu Farah Zakiyah Rahmanti Farodisa, Annida Miftakhul Feby Artwodini Muqtadiroh Fresy Nugroho Gijsbertus Jacob Verkerke Gijsbertus Jacob Verkerke Goenawan A Sambodo Gunawan Gunawan Gunawan Hardianto Wibowo Harfianti, Nadya Putri Herman Thuan Herman Thuan To Saurik Hermawan, Norma Hervit Ananta Vidada Hutama Harsono, Nathanael I Ketut Eddy Purnama I Made Gede Sunarya Imam Robandi Indar Sugiarto Isa Hafidz Ismoyo Sunu Jaya Pranata Joan Santoso Joko Priambodo Khairunnas Khairunnas Koeshardianto, Meidya Kurniawan, Arief Lailatul Husniah Lutfi Ananditya Septiandi Masy Ari Ulinuha Matahari Bhakti Nendya Matahari Bhakti Nendya, Matahari Bhakti Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mauridhi Hery Purnomo Moch. Iskandar Riansyah Mochamad Hariadi Mochamad Yusuf Alsagaff Muhammad Fadli Fakih Muhammad Reza Pahlawan Muhammad Zulfikar Alfathan Rachmatullah Muhtadin Mulyanto, Edy Myrtati Dyah Artaria Nasrulloh, Muhammad Nova Eka Budiyanta Nugroho, Vidityar Adith Oddy Virgantara Putra Pambudi, Sevito Fernanda Pramunanto, Eko Pramunanto, Eko Priambodo, Joko Putu Hendra Suputra R Dimas Adityo Radi Radi Rafly Azmi Ulya, Amik Ragil Bintang Brilyan Rahman, Muhammad Daffa Abiyyu Reza Fuad Rachmadi Rika Rokhana Rika Rokhana Riris Diana Rachmayanti Rokhana, Rika S. Suprapto Saiful Yahya Samuel Gandang Gunanto Sensusiati, Anggraini Dwi Setiawan, Rachmad Setijadi, Eko Soetiono, Agung Dewa Bagus Suciningtyas, Laras Sugiyanto - Sulistyono, Marcelinus Yosep Teguh Supeno M Susiki Nugroho Supeno Mardi Susiki Supeno Mardi Susiki Supeno Mardi Susiki N Supeno Mardi Susiki Nugroho, Supeno Mardi Surya Sumpeno Surya Sumpeno Susiki N, Supeno Mardi Syauqi Sabili Tita Karlita Tita Karlita Tita Karlita Tri Arief Sardjono Tsuyoshi Usagawa, Tsuyoshi Wicaksono, Alif Aditya Willy Achmat Fauzi Wisnu Widiarto Wiwik Anggraeni Yose Rizal Yoyon K. Suprapto Yoyon Kusnendar Suprapto Yuhana, Umi Laili Zaini, Ahmad