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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : -
Articles 1,172 Documents
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Mobile Implementation of Retinal Image Analysis for Efficient Vessel, Optic Disc, and Lesion Detection Hossain, Mubdiul; Ali, Aziah; Hashim, Noramiza; Mohd Isa, Wan Noorshahida; Wan Zaki, Wan Mimi Diyana; Hussain, Aini
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2363

Abstract

Smartphone-based mobile fundus photography is gaining popularity due to the rise of handheld fundus lenses, allowing a portable solution for a mobile-based computer-assisted diagnostic system (CADS). With such a system, professionals can monitor and diagnose numerous retinal diseases, including diabetic retinopathy (DR), glaucoma, age-related macular degeneration, etc. on their smartphone devices. In this study, we proposed a unified CADS tool for smartphone devices that can detect and identify six crucial retinal features utilizing both a filtering approach and a deep learning (DL) approach. These features are retinal blood vessels (RBV), optic discs (OD), hemorrhages (HM), microaneurysm (MA), hard exudates (HE), and soft exudates (SE). Traditional filtering is applied for RBV segmentation using B-COSFIRE and Frangi filter, whereas vessel inpainting and automatic canny edge-based Hough transform are used to localize OD center and radius. The DR lesions (HM, MA, HE, OD segmentation, and SE) are detected using a transfer learning-based Resnet50 backbone and multiclass DL U-net architecture. RBV segmentation achieved 94.94% and 94.44% accuracy in the DRIVE and STARE datasets. OD localization achieved an accuracy of 99.60% in the MESSIDOR dataset. Lastly, the IDRiD dataset is used to train and validate the DR lesions with an overall accuracy of 99.7%, F1-score of 77.4, and mean IoU of 59.2. The smartphone application can perform all the segmentation tasks at once in an average of 30 seconds. Given the availability, it is possible to improve the accuracy of the DL method further by training with more mobile fundus datasets.
Reducing Cognitive Bias of Pre-Service History Teachers through Augmented Reality Elfa Michellia Karima; Nurlizawati Nurlizawati; Firza Firza; Nur Fatah Abidin; Yusuf Ibrahim
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2222

Abstract

Cognitive biases can be problematic and dangerous in history learning. This study aimed to identify the extent to which the independent variables of the case study method and augmented reality influence the dependent variables, as well as evaluate the strength and direction of the relationship between these variables in reducing the cognitive biases of pre-service history teachers. The method used is multiple linear regression to identify the extent to which the application of the case method and the use of augmented reality in learning affect the dependent variable under study. The results showed that augmented reality contributed to the understanding of the history of prospective pre-service history teachers more than the case study method. The effect of the case study method was 7.6% on historical knowledge, and augmented reality media had a 13.9% effect on historical experience. Lecturers can use augmented reality in learning for prospective pre-service history teachers to increase student understanding of history learning material and reduce cognitive biases. This research has implications for using technology and digitalization in history learning for prospective pre-service history teachers to understand history, conceptions, and past events and reduce bias. Understanding history is essential for prospective pre-service history teachers. Prospective pre-service history teachers must also understand a historical event broadly and from various perspectives. Technology-based learning in history learning is one of the right ways to avoid cognitive bias.
Drowsiness Detection System Through Eye and Mouth Analysis Belle Lim, Bey-Ee; Ng, Kok Why; Ng, Sew Lai
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.7.4.2288

Abstract

Traffic jams are one of the serious issues in many developed countries. After the pandemic, many employees were allowed to travel interstate to work. This contributes to more severe jams, especially in the capital and nearby states. Long-distance driving and congestion can easily make the drivers sleepy and thus lead to traffic accidents. This paper aims to study and analyze facial cues to detect early symptoms of drowsy driving. The proposed method employs a deep learning approach, utilizing ensemble CNNs and Dlib's 68 landmark face detectors to analyze the facial cues. The analyzed symptoms include the frequency of eyes opened or closed and yawning or no yawning. Three individual CNN models and an ensemble CNN structure are built for the classification of the eyes and mouth yawn. The model training and validation accuracy graph and training loss and validation loss graph are plotted to verify that the models have not been overfitted. The ensemble CNN models achieved an approximate accuracy of 97.4% from the eyes and 96.5% from the mouth. It outperforms the other pre-trained models. The proposed system can immediately alert the driver and send text drowsy messages and emails to the third party, ensuring timely intervention to prevent accidents. The proposed method can be integrated into vehicles and transportation systems to ensure driver's safety. It can also be applied to monitor the driving behavior of those who drive long distances
A New Feature Extraction Approach in Classification for Improving the Accuracy in Iris Recognition Qadir, Tara Othman; Taujuddin, Nik Shahidah; Fuad, Norfaiza
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.7.4.1373

Abstract

Personal identity is becoming increasingly vital to meet the increasing security standards of today's business society. Iris recognition is one of the most accurate biometric technologies currently in use. Iris recognition is employed in high-security sectors due to its dependability and flawless identification rates. The steps of iris identification, comprising image preparation, extraction of features, and classifier creation, are described thoroughly in the primary portion of this research. The feature extraction stage is the most important in an iris identification system since it extracts the iris's distinctive feature. Several methods have been devised to extract the various characteristics that are unique to everyone. Modern iris identification systems frequently use Gabor filters to identify iris textural characteristics. However, in the application, it is necessary to identify the appropriate Gabor modules and to generate a pattern of iris Gabor characteristics. This research aims to provide a novel multi-channel Gabor filter and Wavelet filter for breaking down and extracting iris data from two different iris datasets. Because wavelet is the most scalable method of image processing, the research investigates using it to create a unique pattern for the iris recognition system. The MATLAB program is used to implement these ideas. CASIA and MMU are the datasets used for this purpose, and their comparative analysis is addressed in the research. To show how well the method performs, experimental results are given. We demonstrate through experiments that the suggested approach results in excellent iris identification performance.
Evaluation of Joint Technique Iterative Clipping Filtering (ICF) and Neural Network Predistortion on SDR-based MIMO-OFDM System Gulo, Melki Mario; Astawa, I Gede Puja; Sudarsono, Amang; Moegiharto, Yoedy; Priambodo, Naufal Ammar; Gunawan, M. Wisnu
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1981

Abstract

Multiple-input, multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a communications technology that powers numerous modern communication systems, including 5G and WiFi-6. This technology is utilized in current communication systems due to its high performance and extensive channel capacity. MIMO-OFDM does have disadvantages, such as large Peak-to-Average Power Ratio (PAPR) values. If the signal is processed by a nonlinear Power Amplifier (PA) device, a high PAPR value signal can result in both in-band and out-of-band signal distortion. To combat high PAPR values, PAPR reduction strategies such as Iterative Clipping Filtering (ICF) are utilized. From this study, using ICF with iteration 2 and Clipping Ratios (CR) 3 and 4 can improve the system's minimum Bit Error Rate (BER) by about 22.8% and 91.1%, respectively. Choosing the correct CR will improve the system, but using the lower CR will make it worse than a system without ICF. This occurs in systems using ICF with iterations two and CR 2 and at the same SNR conditions as systems without ICF; using ICF with iterations two and CR 2 results in higher BER values. The use of Predistortion Neural Network (PDNN) can overcome this problem. By using PDNN, there is an improvement in the system where the minimum BER value can reach 0.1 × 10-5. The percentage decrease in BER from using PDNN for ICF with iterations two and CR 2, 3, and 4 is 99.88%, 99.86%, and 98.807%, respectively. Thus, the joint techniques of ICF and PDNN can significantly enhance the performance of MIMO-OFDM systems with nonlinear PA. Importantly, the experiment was conducted on an SDR device, ensuring the real-world applicability of the results.
Performance Analysis of Feature Mel Frequency Cepstral Coefficient and Short Time Fourier Transform Input for Lie Detection using Convolutional Neural Network Kusumawati, Dewi; Ilham, Amil Ahmad; Achmad, Andani; Nurtanio, Ingrid
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2062

Abstract

This study aims to determine which model is more effective in detecting lies between models with Mel Frequency Cepstral Coefficient (MFCC) and Short Time Fourier Transform (STFT) processes using Convolutional Neural Network (CNN). MFCC and STFT processes are based on digital voice data from video recordings that have been given lie or truth information regarding certain situations. Data is then pre-processed and trained on CNN. The results of model performance evaluation with hyper-tuning parameters and random search implementation show that using MFCC as Voice data processing provides better performance with higher accuracy than using the STFT process. The best parameters from MFCC are obtained with filter convolutional=64, kerneconvolutional1=5, filterconvolutional2=112, kernel convolutional2=3, filter convolutional3=32, kernelconvolutional3 =5, dense1=96, optimizer=RMSProp, learning rate=0.001 which achieves an accuracy of  97.13%, with an AUC value of 0.97. Using the STFT, the best parameters are obtained with filter convolutional1=96, kernel convolutional1=5, convolutional2 filters=48, convolutional2 kernels=5, convolutional3 filters=96, convolutional3 kernels=5, dense1=128, Optimizer=Adaddelta, learning rate=0.001, which achieves an accuracy of 95.39% with an AUC value of 0.95. Prosodics are used to compare the performance of MFCC and STFT. The result is that prosodic has a low accuracy of 68%. The analysis shows that using MFCC as the process of sound extraction with the CNN model produces the best performance for cases of lie detection using audio. It can be optimized for further research by combining CNN architectural models such as ResNet, AlexNet, and other architectures to obtain new models and improve lie detection accuracy.
A Review of Neural Network Approach on Engineering Drawing Recognition and Future Directions Mohd Yazed, Muhammad Syukri; Ahmad Shaubari, Ezak Fadzrin; Yap, Moi Hoon
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.7.4.1716

Abstract

Engineering Drawing (ED) digitization is a crucial aspect of modern industrial processes, enabling efficient data management and facilitating automation. However, the accurate detection and recognition of ED elements pose significant challenges. This paper presents a comprehensive review of existing research on ED element detection and recognition, focusing on the role of neural networks in improving the analysis process. The study evaluates the performance of the YOLOv7 model in detecting ED elements through rigorous experimentation. The results indicate promising precision and recall rates of up to 87.6% and 74.4%, respectively, with a mean average precision (mAP) of 61.1% at IoU threshold 0.5. Despite these advancements, achieving 100% accuracy remains elusive due to factors such as symbol and text overlapping, limited dataset sizes, and variations in ED formats. Overcoming these challenges is vital to ensuring the reliability and practical applicability of ED digitization solutions. By comparing the YOLOv7 results with previous research, the study underscores the efficacy of neural network-based approaches in handling ED element detection tasks. However, further investigation is necessary to address the challenges above effectively. Future research directions include exploring ensemble methods to improve detection accuracy, fine-tuning model parameters to enhance performance, and incorporating domain adaptation techniques to adapt models to specific ED formats and domains. To enhance the real-world viability of ED digitization solutions, this work highlights the importance of conducting testing on diverse datasets representing different industries and applications. Additionally, fostering collaborations between academia and industry will enable the development of tailored solutions that meet specific industrial needs. Overall, this research contributes to understanding the challenges in ED digitization and paves the way for future advancements in this critical field.
Jaccard-based Random Distribution with Least and Most Significant Bit Hiding Methods for Highly Patients MRI Protected Privacy Tayh Albderi, Ali Jaber; Al-Shammary, Dhiah; Said, Lamjed Ben
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2385

Abstract

In this study, the main goal is to improve patient care by making it easier for patient data and pictures to be sent between medical centers without problems. Still, one of the biggest problems with telemedicine is keeping patient information private and ensuring data is safe. This is especially important because even small changes to patient information could have serious consequences, such as wrong evaluations and lower-quality care. This study develops a new model that uses the unique Jaccard distribution of the least significant bit (LSB) and the most significant bit (MSB) to solve this complex problem. The goal of this model is to hide much information about a patient in the background of an MRI cover picture. The careful creation of this model is a crucial part of the current study, as it will ensure a solid way to hide information securely. A more advanced method is also suggested, which involves randomly putting private text in different places on the cover picture. This plan is meant to strengthen security steps and keep private patient information secret. The peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and the mean square error (MSE) all improved significantly when this method was tested in the real world. With these convincing results, the study shows telemedicine is more effective than traditional methods for keeping patient data safe. This proves that the model and method shown have the potential to greatly improve patient privacy and data accuracy in telemedicine systems, which would improve the general quality of health care.
CSS for CVR: A Reciprocal Velocity Obstacle-Based Crowd Simulation System for Non-Playable Character Movement of Campus Virtual Reality Arif, Yunifa Miftachul; Janitra, Geovanni Azam; Imamudin, M.; Safitri A Basid, Puspa Miladin Nuraida; Setiawan, Dedy Kurnia
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1997

Abstract

Along with the development of multimedia technology, an overview of the campus environment for prospective new visitors can be visualized through a 3D virtual environment based on virtual reality. A crowd simulation system is needed to provide an overview of the crowds in campus virtual reality (CVR). The simulation helps make it easier for individuals to predict crowds in certain areas virtually. In this study, we propose using the Reciprocal Velocity Obstacle (RVO) method to support the simulation of Non-Playable Character (NPC) crowds in a visualized virtual environment. RVO implements multi-agent navigation by estimating the possibility of moving without communication between agents and being able to perform collision avoidance. The use of RVO in this study aims to contribute to the collision detection development process for each NPC. The application of RVO is carried out in the development of virtual reality by using Unity3D and Blender asset support tools. The results of this study indicate that the RVO method can be applied in multi-agent navigation. These results were confirmed by the success of the NPC as a simulation agent in selecting routes and independently navigating to avoid collisions between agents without the need for communication. In every simulation, collisions will occur within a set of agents due to high density, which causes complex computations. The development of CSS can help every CVR user experience a virtual environment. In addition, each user can experience a more natural experience with the presence of 3D objects and virtual reality with RVO-based CSS. Furthermore, this research material is expected to be developed from various perspectives and themes related to crowd simulation for games and other simulation media.
Exploring Classification Algorithms for Detecting Learning Loss in Islamic Religious Education: A Comparative Study Sapdi, Rohmat Mulyana; Maylawati, Dian Sa'adillah; Ramdania, Diena Rauda; Budiman, Ichsan; Al-Amin, Muhammad Insan; Fuadi, Mi'raj
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1823

Abstract

This study investigates the detection of learning loss in Islamic religious education subjects in Indonesia. Focusing on the effectiveness of multiple classification algorithms, the research assesses learning loss across literacy, numeracy, writing, and science domains. While education traditionally involves knowledge transmission, it also seeks to instill values. Given Indonesia's predominantly Islamic demographic, Islamic Religious Education (IRE) is pivotal in disseminating moral and cultural values, encompassing teachings from the Koran, Hadith, Aqedah, morality, Fiqh, and Islamic history. The study's central aim is to discern learning loss in IRE in Islamic schools, utilizing the Gradient Boosting Classifier as its primary analytical tool. Various classification algorithms, including the Cat Boost Classifier, Light Gradient Boosting Machine, Extreme Gradient Boosting, and others, were tested. The study engaged a sample of 38,326 Islamic Elementary school students, 29,350 Islamic Junior High school students, and 13,474 Islamic High school students across Indonesia. The findings revealed that the Light Gradient Boosting Machine was the most effective model for Islamic Elementary and High school data, while the Cat Boost Classifier excelled for Islamic Junior High school data. These results highlight the extent of learning loss in IRE and offer invaluable perspectives for education stakeholders. Future studies are encouraged to further explore the root causes of this learning loss and devise specific interventions to tackle these issues effectively.

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