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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.
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Articles 59 Documents
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Search results for , issue "Vol 8, No 4 (2024)" : 59 Documents clear
Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images Miah, Jonayet; M Cao, Duc; Sayed, Md Abu; Taluckder, Md Siam; Haque, Md Sabbirul
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2208

Abstract

Brain tumors pose a significant global health challenge due to their high prevalence and mortality rates across all age groups. Detecting brain tumors at an early stage is crucial for effective treatment and patient outcomes. This study presents a comprehensive investigation into the use of Convolutional Neural Networks (CNNs) for brain tumor detection using Magnetic Resonance Imaging (MRI) images. The dataset, consisting of MRI scans from both healthy individuals and patients with brain tumors, was processed and fed into the CNN architecture. The SoftMax Fully Connected layer was employed to classify the images, achieving an accuracy of 98%. To evaluate the CNN's performance, two other classifiers, Radial Basis Function (RBF) and Decision Tree (DT), were utilized, yielding accuracy rates of 98.24% and 95.64%, respectively. The study also introduced a clustering method for feature extraction, improving CNN's accuracy. Sensitivity, Specificity, and Precision were employed alongside accuracy to comprehensively evaluate the network's performance. Notably, the SoftMax classifier demonstrated the highest accuracy among the categorizers, achieving 99.52% accuracy on test data. The presented research contributes to the growing field of deep learning in medical image analysis. The combination of CNNs and MRI data offers a promising tool for accurately detecting brain tumors, with potential implications for early diagnosis and improved patient care
Enhancement of Secure Hospital Healthcare Monitoring System Based–Software Defined Network (SDN) with Machine Learning Ahmed, Sarah Shihab; Shakir, Huda Rashid
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2425

Abstract

Handling delicate and crucial knowledge by healthcare providers requires security measures to prevent unapproved use. Software-defined networks (SDNs) are extensively used in medical facilities to ensure resource efficiency, security, and superior network management and management. Despite these advantages, SDNs present a significant threat from various assaults due to the sensitivity  of patient information. Our work's primary goal is to propose a global connection between SDN technology and machine learning-based assaults in healthcare. This paper aims to draw attention to a few relevant options. Additionally, we give a framework using software-defined network principles that illustrate linkages between a collection of people, each of whom has a Nano network residing within their bodies, and medical providers via the local network of a medical center. In health care, the initiative is sometimes called an issue of machine learning assault systems and amenities. The current possibilities for machine learning cyberattacks on the medical industry are quite promising. It is also highly well-liked because of its capacity to identify and assess. From a single instrument to the enormous amounts of data gathered, this evolution radically changes how we approach medicine. This work uses a range of ML approaches and attacks to test MLCAH (Machine Learning-based Cyber Attacks Healthcare). For every combination of machine learning methods and assaults, an efficiency assessment highlights the benefits and drawbacks of different algorithms for defending against a specific assault.
Deep Learning-based Utility Pole Safety Assessment from Visual Data Abbas M. Elsayed, Mohamed; Hashim, Noramiza; Abdul Rahman, Abdul Aziz; Alhayek, Mohamed
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3039

Abstract

Utility poles are crucial infrastructure components, and efficiently assessing the safety of these structures and ensuring they adhere to the clearance guidelines, which specify the minimum distance between the pole and any surrounding objects, remains a challenge; the current manual inspection process is time-consuming, costly, and often subjective. This work proposes an automated deep learning-inspired solution to improve utility pole detection and measure the clearance distance. The biggest challenge was the lack of a comprehensive pole dataset; therefore, we collected a dataset containing utility poles in varied backgrounds, environments, and conditions. We compared data augmentation techniques and employed them to address the limited dataset size. The proposed approach consists of two main stages: pole detection and differentiation and pole distance measurement. The first stage is a comparison of multiple object detection models on our utility pole dataset; we used the results from the best-performing model to estimate the distance between the two pole objects. The results show that our pipeline with the YOLOv8 model outperforms SSD and achieves 83% accuracy in classifying pole compliance. The system can accurately detect and estimate clearance violations even with limited data. The success of the pipeline opens opportunities for future research; obtaining depth by using additional sensors or deep learning models could enhance the detection module. Scaling the approach to large utility pole networks while retaining real-time performance could lead to improved autonomous infrastructure maintenance.
Enhancing Potato Leaf Disease Detection: Implementation of Convolutional Vision Transformers with Synthetic Datasets from Stable Diffusion Astuti, Tri; Umar, Amri Nurkholis; Wahyudi, Rizki; Rifai, Zanuar
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2167

Abstract

Numerous studies have addressed the classification of potato plants. However, the available datasets often lack the necessary diversity to improve the accuracy of predictive classification models effectively. Our research capitalizes on synthetic datasets generated through the Stable Diffusion 1.5 image generation method to address this challenge. This study suggests a new way to solve the problem by using artificial datasets created with the Stable Diffusion 1.5 method to teach a Convolutional Vision Transformer (CvT) model how to identify diseases on potato leaves accurately. Our objective is to train the CvT model employing synthetic datasets to excel in detecting potato leaf diseases. Our methodology encompasses the model's training using synthetic datasets from Stable Diffusion 1.5. We employ a comprehensive dataset of 11,121 synthetic images to train the Convolutional Vision Transformer (CvT) model, which enables it to accurately identify various potato leaf diseases such as black leg/soft rot, mosaic, leaf roll, early blight, and late blight. We conduct evaluations at multiple training stages to gauge the model's performance and accuracy. The outcomes of our research underscore the effectiveness of employing synthetic datasets from Stable Diffusion 1.5, which significantly augments the available image data while preserving a high level of accuracy. The CvT model proficiently identifies potato leaf diseases with an evaluation accuracy of 84%. Additional testing reveals that by the fifth epoch, the CvT model attains an accuracy of 81% when assessed using 82 randomly selected images of diseased plants from Google. The implications of this research are far-reaching, particularly within the domains of image processing and agriculture. The strategy of utilizing synthetic datasets to train the CvT model presents an efficient remedy to address the limitations of original image datasets. The adept disease detection capability of the CvT model holds the potential to expedite plant condition identification, mitigate crop loss, and ultimately amplify agricultural productivity. This study effectively demonstrates that the Convolutional Vision Transformer (CvT), when leveraged with synthetic datasets from Stable Diffusion 1.5, produces a model capable of accurately identifying potato leaf diseases. These findings bear positive implications for both the agricultural and image-processing sectors. 
A Thorough Review of Vehicle Detection and Distance Estimation Using Deep Learning in Autonomous Cars Rahmat, Muhammad Abdillah; Indrabayu, Indrabayu; Achmad, Andani; Salam, Andi Ejah Umraeni
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2665

Abstract

Autonomous vehicle technologies are rapidly advancing, and one key factor contributing to this progress is the enhanced precision in vehicle detection and distance calculation. Deep Learning Networks (DLNs) have emerged as powerful tools to address this challenge, offering remarkable capabilities in accurately detecting and estimating vehicle positions. This study comprehensively reviews DLN applications for vehicle detection and distance estimation. It examines prominent DLN models such as YOLO, R-CNN, and SSD, evaluating their performance on widely used datasets such as KITTI, PASCAL VOC, and COCO. Analysis results indicate that YOLOv5, developed by Farid et al. achieves the highest accuracy level with a mAP (mean Average Precision) of 99.92%. Yang et al. showcased that YOLOv5 performs exceptionally in detection and distance estimation tasks, with a mAP of 96.4% and a low mean relative error (MRE) of 10.81% for distance estimation. These achievements highlight the potential of DLNs to enhance the accuracy and reliability of vehicle detection systems in autonomous vehicles. The study also emphasizes the importance of backbone architectures like DarkNet 53 and ResNet in determining model efficiency. The choice of the appropriate model depends on the specific task requirements, with some models prioritizing real-time detection and others prioritizing accuracy. In conclusion, developing DLN-based methods is crucial in advancing autonomous vehicle technology. Research and development remain crucial in ensuring road safety and efficiency as autonomous vehicles become more common in transportation systems.
Improved Fuzzy Possibilistic C-Means using Artificial Bee Colony for Clustering New Student’s Financial Capability to Determine Tuition Level Satriyanto, Edi; Surya Wardhani, Ni Wayan; Anam, Syaiful; Mahmudy, Wayan Firdaus
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3087

Abstract

Outliers in the dataset will affect the quality of the cluster, so a good clustering method is needed. Based on the Mahalanobis distance method, it is known that the research dataset has outliers. Clustering methods that are often used for this type of data are Fuzzy C-means (FCM), Possibilistic C-means (PCM), and Fuzzy Possibilistic C-means (FPCM). This study aims to develop a clustering method that is more robust to outliers by using the Artificial Bee Colony (ABC) algorithm to minimize the objective function of FPCM. This study produces a new algorithm called Artificial Bee Colony Fuzzy Possibilistic C-Means (ABCFPCM) so that the resulting clusters are not easily trapped in the local optimum. This study also provides cluster centroid initialization using K-Means++ to improve cluster quality. ABCFPCM performs best because it significantly increases the Silhouette value and the Between Sum Squares (BSS) and Total Sum Squares (TSS) ratio. ABCFPCM performance provides the best cluster quality of 72.16% based on the BSS/TSS ratio, FPCM of 70.71%, and FCM K-Means++ of 68.14%. K-Means++ in the cluster method does not affect cluster performance except for FCM, where cluster quality is slightly increased. The centroid results of 8 clusters as the best performance of ABCFPCM are used to determine the tuition rate level. The impact of this study is to improve the quality of FPCM performance because it is no longer trapped in a local optimum at the cluster centroid.
Mixed Learning Models and IoT Devices: Effectively Increasing Competence and Training Independent Learning Students in Unnormal Situations Purba, Ramen Antonov; Simarmata, Janner; Limbong, Tonni; Damanik, Romanus
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2553

Abstract

Abnormal situations often occur, such as natural disasters and COVID-19. Educational institutions struggle to regulate learning. The web programming course aims to shape students into website programmers. Independence in learning is needed so that competence is obtained. Students are not enough to rely on learning from the lecturer. This study aims to analyze the combination of the Inquiry-Based Learning model with IoT devices based on Android mobile. As a supporter, an application is built with a mobile programming language. This type of research is quasi-experimental. Calculations using SPSS 23.0. An experimental class learns to use the Inquiry-Based Learning model with IoT devices, and a control class learns with various media. The research subjects were 60 students of Information Management. The study found differences in students' competence and learning independence in those who learned to use the inquiry-based learning model with IoT devices compared to those who studied with various media. The test results showed a higher increase in the experimental class. The experimental class's value is 14.40 for a gain of 7.5. The sig. value is  .000, and the average gain is .83. Control class score is 11.87, an increase of 5.1, sig. value is .000, and the average gain is .53. Applying the inquiry-based learning model with IoT devices has also proven to be effective as a model and learning media in abnormal situations and reinforced by the average gain of the experimental class, which is greater than the control class. Future research could use different methods to determine what methods are most effective.
Implementation of Virtual Reality Moot Court for Simulation and Procedural Law Learning of the Constitutional Court Hidayah, Nur Putri; Wicaksono, Galih Wasis; Perdana, Muhammad Ilham; Faiz, Ahmad; Cholidah, -
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3125

Abstract

The limited space for moot court simulations in law learning is one of the main obstacles. In the Constitutional Court's judicial practice, no faculty has a Moot courtroom identical to the actual courtroom. Every law student must be able to practice trial to improve their argumentation, advocacy, legal reasoning, and other problem-solving skills. This research aims to build and develop a Virtual Reality (VR) Moot Court that can be used as a Moot Court in the trial of the Constitutional Court. VR Moot Court is a means of practicum in the constitutional procedure law course. This research was carried out through scenario preparation and system design stages, followed by 3D asset optimization, user interaction design, multi-user design, and testing. This research utilizes Unity to build 3D assets and Spatial.io as a VR platform. For more immersive use, users can use VR headsets such as Oculus. However, VR Moot Court can also be accessed via smartphone or PC for broader use. The development of VR Moot Court is quite complex, requiring the optimization of assets used across various devices. This study optimizes poly, texture, material, and lighting. The results of VR Moot Court development in this study tested the system's functionality and measured the optimization results. The results of system optimization tests have shown a decrease in GPU and CPU usage. Meanwhile, the results of the functionality and user satisfaction tests also show that VR Moot Court, in addition to taking course learning outcomes in the constitutional court's procedural law course, this system is also relevant to the actual Constitutional Court courtroom. This research in the future requires the development of a type of moot courtroom for other kinds of courts.
GLCM and PSNR Analysis of Woven Fabric Images Made from Natural Dyes Due to Sunlight Exposure Batarius, Patrisius; Santoso, Albertus Joko; Sinlae, Alfry Aristo Jansen
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2265

Abstract

Traditional woven fabrics generally use natural dyes that come from the local area. Natural dyes are often considered low quality if exposed to sunlight. This study aims to analyze the effect of sunlight on the image of woven fabrics made from natural dyes. The natural dyes used come from noni (Morinda citrofolia L), which produces a red color; Tarum (Indigofera tinctoria L), which produces a blue-black color; and corn starch juice, which produces a white color. A thread made of cotton is dipped and cooked to produce the desired color. The analysis is done by comparing the value of GLCM (Grey Lever Co-Coruent Matrix) features, changes in the value of Mean Square Error (MSE), and Peak Signal Noise Ratio (PSNR) with the original image. The original image is taken before the woven fabric is dried in the sun. The changing image is taken after the woven fabric is dried in the sun with variations in drying times. The drying time of woven fabric is 1 hour. Sun drying starts from 09:00 to 14:00. The distance between the original and sun-dried images is 30 cm. The original image and the sun-dried image went through cropping and resizing the image to be the same size. The grayscale image type is used for the GLCM, MSE, and PSNR comparison process. The image size used is 128x128 for woven fabric images with three kinds of colors (white, red, and blue) and 256x256 pixels for images with white color. The results showed that the quality of the images produced at drying hours of 09.00-10.00 to 14.00-15.00 tended to be low, with a significant difference between the original image and the changed image. The lowest point of quality lies in the drying time of 12.00-13.00 and 13.00-14.00. For the GLCM features, the sun-dried image at 14.00-15.00 has a homogeneity value close to the original value. For contrast features, the image dried at 10.00-11.00 has a contrast value that is close to the original image contrast value. This shows the smaller the difference in pixel intensity in the image.
Enhancing Land Management through U-Net Deep Learning: A Case Study on Climate-Related Land Degradation in Berembun Forest Reserve in Malaysia Chew, Yee Jian; Ooi, Shih Yin; Mohd-Razali, Sheriza; Pang, Ying Han; You Lim, Zheng
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2948

Abstract

In the face of accelerating climate change, effective management of land resources needs innovative technological approaches. This study, conducted in the Berembun Forest Reserve, Jelebu, Malaysia, leverages advancements in geospatial technology and machine learning to address the pressing issue of land degradation, focusing on forested areas vulnerable to landslides. Utilizing high-resolution Unmanned Aerial Vehicle (UAV) imagery, the U-Net convolutional neural network model is employed for the precise classification and early detection of landslide-induced land degradation. Through a systematic analysis of 15 high-quality UAV images of 5472 x 3647 pixels, segmented into 256 x 256-pixel patches, the U-Net model demonstrated remarkable accuracy, achieving a mean Intersection-over-Union (IoU) of 0.9466. These findings underscore the model's potential to significantly enhance land management practices by providing timely and cost-effective landslide detection. Adopting such deep learning techniques is a pivotal shift towards more sustainable and resilient land management strategies in the era of climate change. This research showcases the practical application of machine learning in environmental monitoring and paves the way for future innovations. Implications for further research include integrating additional spectral bands, addressing environmental variability, and expanding applications across diverse landscapes to improve environmental monitoring, conservation efforts, and resilience strategies. Developing automated frameworks for real-time data processing and model deployment could further revolutionize the field, enabling more responsive and efficient land management practices.

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