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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Semantik JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor EMITTER International Journal of Engineering Technology Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Health Notions Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JAVA Journal of Electrical and Electronics Engineering JAREE (Journal on Advanced Research in Electrical Engineering) International Journal of Robotics and Control Systems Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sistim Informasi dan Teknologi Sewagati
Tri Arief Sardjono
Departemen Teknik Biomedik Institut Teknologi Sepuluh Nopember Surabaya
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : EMITTER International Journal of Engineering Technology

 1 
Towards Improvement of LSTM and SVM Approach for Multiclass Fall Detection System Herti Miawarni; Eko Setijadi; Tri Arief Sardjono; Wijayanti; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.639

Abstract

Telemonitoring of human physiological data helps detect emergency occurrences for subsequent medical diagnosis in daily living environments. One of the fatal emergencies in falling incidents. The goal of this paper is to detect significant incidents such as falls. The fall detection system is essential for human body movement investigation for medical practitioners, researchers, and healthcare businesses. Accelerometers have been presented as a practical, low-cost, and dependable approach for detecting and predicting outpatient movements in the user. The accurate detection of body movements based on accelerometer data enables the creation of more dependable systems for incorporating long-term development in physiological remarks. This research describes an accelerometer-based platform for detecting users' body movement when they fall. The ADXL345, MMA8451q, and ITG3200 body sensors capture activity data, subsequently classified into 15 fall incident classes based on SisFall dataset. Falling incidents classification is performed using Long Short-Term Memory results in best AUC-ROC value of 97.7% and best calculation time of 6.16 seconds. Meanwhile, Support Vector Machines results in the best AUC-ROC value of 98.5% and best calculation times of 17.05 seconds.
An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images Nada Fitrieyatul Hikmah; Tri Arief Sardjono; Windy Deftia Mertiana; Nabila Puspita Firdi; Diana Purwitasari
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.695

Abstract

Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment.
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 A. Surbakti, Armand Faris Ainani Shabrina Febrianti Al Kindhi, Berlian Alan Novi Tompunu Ardyono Priyadi Arifin, Achmad Aris Nasuha Arraziqi, Dwi Asmaria, Talitha Atar Fuady Babgei Berlian Al Kindhi Budiman, Fajar Cahya Perbawa Aji Dewi Mayangsari Dewi Mayangsari Diah Puspito Wulandari Diana Purwitasari Dimas Herjuno Djoko Purwanto Durrotun Munafiah Eko Agus Suprayitno Eko Mulyanto Yuniarno Farah Zakiyah Rahmanti Fatchul Arifin Firmansyah, Riza Agung Gde Bayu Adityaputra Hajjanto, Ariq Dreiki Harris Pirngadi Helmy Widyantara Hendra Kusuma Hendra Kusuma Hermawan, Norma Herti Miawarni Hikmah, Nada Fitrieyatul I Ketut Eddy Purnama I Made Gede Sunarya Ismoyo Sunu Jaenal Arifin Joko Priambodo Joko Subur Kharis Sugiarto Kusuma, Hendra Siswanto Lilik Subiyanto Mauridhi Hery Purnomo Mauridhy Hery Purnomo Melyana Nurul Widyawati Moch. Iskandar Riansyah Mochamad Hariadi Moh. Yani Mohammad Nuh Muhammad Hilman Fatoni Muhammad Rivai Munafiah, Durrotun Nabila Puspita Firdi Nabilah Ashriyah Nabilah Ashriyah Nada Fitrieyatul Hikmah Oddy Virgantara Putra Paskalis Kurniaji Prakosa, Nadhira Anindyafitri RD Kusumanto Retno Tri Wahyuni Rika Rokhana Ronny Mardiyanto, Ronny Rudy Dikairono Setiawardhana Setiawardhana Setiawardhana Setijadi, Eko Tita Karlita Totok Mujiono, Totok Ubaidillah Umar, Ubaidillah Wijayanti . Windy Deftia Mertiana Windy Deftia Mertiana Yulianto Tejo Putranto Yulianto, Lucas Yunardi, Riky Tri