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All Journal TEKNIK INFORMATIKA Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Jurnal Pemikiran dan Pengembangan Sekolah Dasar (JP2SD) Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains JOIV : International Journal on Informatics Visualization IT JOURNAL RESEARCH AND DEVELOPMENT Journal of Teaching and Learning in Elementary Education Dinamisia: Jurnal Pengabdian Kepada Masyarakat Journal of Psychology and Instructions Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Instructional Development Journal Journal of Applied Engineering and Technological Science (JAETS) International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Aksara: Jurnal Ilmu Pendidikan Nonformal Jurnal Manajemen Informatika Jayakarta Journal for Lesson and Learning Studies Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Jurnal Teknik Informatika Journal of Applied Business and Technology Jurnal Elementaria Edukasia
Dewi Nasien
Instititut Bisnis Dan Teknologi Pelita Indonesia
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

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Classifying Vehicle Types from Video Streams for Traffic Flow Analysis Systems Imran B. Mu’azam; Nor Fatihah Ismail; Salama A. Mostafa; Zirawani Baharum; Taufik Gusman; Dewi Nasien
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.739

Abstract

This paper proposes a vehicle types classification modelfrom video streams for improving Traffic Flow Analysis (TFA) systems. A Video Content-based Vehicles Classification (VC-VC) model is used to support optimization for traffic signal control via online identification of vehicle types.The VC-VC model extends several methods to extract TFA parameters, including the background image processing, object detection, size of the object measurement, attention to the area of interest, objects clash or overlap handling, and tracking objects. The VC-VC model undergoes the main processing phases: preprocessing, segmentation, classification, and tracks. The main video and image processing methods are the Gaussian function, active contour, bilateral filter, and Kalman filter. The model is evaluated based on a comparison between the actual classification by the model and ground truth. Four formulas are applied in this project to evaluate the VC-VC model’s performance: error, average error, accuracy, and precision. The valid classification is counted to show the overall results. The VC-VC model detects and classifies vehicles accurately. For three tested videos, it achieves a high classification accuracy of 85.94% on average. The precession for the classification of the three tested videos is 92.87%. The results show that video 1 and video 3 have the most accurate vehicle classification results compared to video 2. It is because video 2 has more difficult camera positioning and recording angle and more challenging scenarios than the other two. The results show that it is difficult to classify vehicles based on objects size measures. The object's size is adjustable based on the camera altitude and zoom setting. This adjustment is affecting the accuracy of vehicles classification.
Convolutional Neural Network Model for Sex Determination Using Femur Bones Nasien, Dewi; Adiya, M. Hasmil; Afrianty, Iis; Farkhan, Mochammad; Butar-Butar, Rio Juan Hendri
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1711

Abstract

Forensic anthropology is the critical discipline that applies physical anthropology in forensic education. One valuable application is the identification of the biological profile. However, in the aftermath of significant disasters, the identification of human skeletons becomes challenging due to their incompleteness and difficulty determining sex. Researchers have explored alternative indicators to address this issue, including using the femur bone as a reliable sex identifier. The development of artificial intelligence has created a new field called deep learning that has excelled in various applications, including sex determination using the femur bone. In this study, we employ the Convolutional Neural Network (CNN) method to identify the sex of human skeleton shards. A CNN model was trained on 91 CT-scan results of femur bones collected from Universiti Teknologi Malaysia, comprising 50 female and 41 male patients. The data pre-processing involves cropping, and the dataset is divided into training and validation subsets with varying percentages (60:4, 70:30, and 80:20). The constructed CNN architecture exhibits exceptional accuracy, achieving 100% accuracy in both training and validation data. Moreover, the precision, recall, and F1 score attained a perfect score of 1, validating the model's precise predictions. The results of this research demonstrate excellent accuracy, confirming the reliability of the developed model for sex determination. These findings demonstrate that using deep learning for sex determination is a novel and promising approach. The high accuracy of the CNN model provides a valuable tool for sex determination in challenging scenarios. This could have important implications for forensic investigations and help identify victims of disasters and other crimes.
Co-Authors Adiya, M. Hasmil Agus Joko Purwanto Agus Setiawan ahmad kamal, ahmad Ahmad Mulyadi Akbar Marunduri, Alberta Alin Meisya Putri Alyauma Hajjah Amalia Sapriati Andi Andi Anggara, Devi Willeam Angriawan, Sherkhing Anwar Senen Baharum, Zirawani Butar-Butar, Rio Juan Hendri Charles Lo, Kevin Charles Wijaya, Ryan Cia, Alexander Cici Oktaviani Dahliyusmanto, Dahliyusmanto Darwin, Ricalvin Deny Deny, Deny Deny Jollyta Desnelita, Yenny Devi Willieam Anggara Diniya, Diniya Dipuja, Diah Anugrah Erlin Erma Yunita Farkhan, Mochammad Fenly, Fenly Feri Candra Firman Afriadi Fitri Indriani Fitriani, Mike Go, Jerry Gusman, Taufik Gustientiedina Habibollah Haron Ihsan, M. Nurul Iis Afrianty Iis Afrianty Imran B. Mu’azam Jack Billie Chandra Jesi Alexander Alim Johan Johan Johanes Johanes, Johanes Leo Winata, Andrean Leo, Leo Lina Warlina Lombu, Frendly M.C, Richard Mahbubah, Khoiro Mahmud Dwi Sulistiyo Marlim, Yulvia Nora Mestika Sekarwinahyu Mike Fitriani Muhammad Rakha Muhammad Ridha Nazara, Elvin Meiwati Neni Hermita Nopendri Nopendri Nor Fatihah Ismail Nurwijayanti Oraple, Ezri Trivena Owen, Steven Pamungkas, Dwi Pandapotan, Boris Yosua Prawinata See, Richardo Putra Yansen, Eka Rahmadhani, Ummi Sri Rahmadian Yuliendi, Rangga Ramalia Noratama Putri Ria Asrina Marza Rianda, Gilang Rio Asikin Rio Rokhima, Nur Roni Sanjaya Ryan Syahputra, Ryan Syahputra Salama A. Mostafa Samah, Azurah A. Sardius, Sardius Setiawan, Laurensius Rendi Siddik, M. Sirait, Andrio Pratama Sirvan, Sirvan Sri Tatminingsih Sukabul, Ahmad Suliana Supriati, Amelia Suroyo Suroyo Tavip, Achmad Wicaksono, Mahfuzan Hadi Wijaya, Tommy Tanu Wilda Susanti Yacob, Azliza Yuli Astuti Yulianti, Deni Yusnita Rahayu Zetra Hainul Putra Zeva Adi Fianto Zirawani Baharum