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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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Enhancing Batik Classification Leveraging CNN Models and Transfer Learning Perdana, Am Akbar Mabrur; Fajar B, Muhammad; Mappalotteng, Abdul Muis
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2535

Abstract

Batik is a traditional art originating from Indonesia and recognized by UNESCO. Batik motifs vary depending on the region of origin. The diverse batik motifs reflect the rich cultural heritage and unique traditions owned by each region in Indonesia. From Sabang to Merauke, each motif features a different story and values, depicting the beauty and diversity of nature and the lives of diverse local people. However, in the context of the modern era that continues to develop, batik motifs also experience renewal and creativity that always adapts to the times. As a result, the diversity of batik motifs is increasingly abundant in Indonesia. Thus, complicating efforts to identify and categorize batik motifs appropriately. Therefore, in the context of this study, we chose to combine the MobileNetV2 model with Transfer Learning to classify batik motifs. We used a dataset consisting of 3000 batik images and categorized them into three main classes, namely Kawung batik, Mega Mendung batik, and Parang batik. This approach not only leads to the introduction and understanding of traditional batik motifs but also applies the latest technology for a more in-depth and accurate analysis. The results of this model show an extremely high level of testing accuracy, reaching 0.9946%, and training accuracy of 0.8916%, and the time required by the model to train and test the entire dataset is 18 minutes 1 second. Future research can explore the integration of other technologies or new approaches to improve accuracy and efficiency in classifying batik motifs.
A Model for Classification Usability Testing Practically from the Agile Methodology Aspect Salman, Fouad; Baluch, Bakhtawar; Bakar, Zuriana Abu
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2459

Abstract

Usability is a crucial feature in the success of software products. Development practitioners know that to preserve and improve the quality of the product and usability has to be carefully considered throughout the development process. The tendency toward empowering users in software development drives the ongoing pursuit of solutions to reconcile agile and usability goals. In this paper, the authors aim to develop a model for classifying usability testing methods according to aspects of agile methodologies. This model can enable agile practitioners to obtain end-user feedback when implementing usability tests at the appropriate time and place during development and thus produce useful and usable software. Mixed methods (qualitative and quantitative) were used in this research to collect primary and secondary data. This research adopted the convenience non-probability sampling technique for evaluating the model.  The evaluation determines whether it could provide valuable information supporting consistent usability tests. The method of performance profiles is also applied in this evaluation to gain accurate results and avoid any biases that might unnecessarily affect the outcomes. The evaluation results were encouraging, and the model showed beneficial effects in integrating usability work into an agile approach, especially since all attributes showed high importance among participants' accepted satisfaction, representing the least essential scale. The developed model must be applied practically to the other integration models in future work. Furthermore, several observation techniques are required to thoroughly cover the integration by software development teams from diverse organizations. 
Robust and Automatic Algorithm for Palmprint ROI Extraction Yousif, Noor A.; Qassir, Samar Amil; George, Dena Nadir
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2801

Abstract

The ridges, creases, wrinkles, and minutiae on the palmprint region of interest (ROI) are important features. These features are employed to confirm or identify an individual. One inevitable issue in the realization of palmprint recognition systems is the extraction procedure of this region under unrestricted environments. The variety in palm sizes, postures, lighting conditions, and backgrounds, however, certainly presents a significant issue. Finding and extracting the palm's area of interest (ROI) will be our main goal. This research introduces a robust automated algorithm based on square construction and each YCbCr color space features. After reading the image of the colored hand, this algorithm goes through two stages. Firstly, convert to the YCbCr color space. This stage guarantees precise locating of the hand region in addition to deleting irrelevant information from the image. Secondly, determining ROI is based on applying three steps: locating three key references, utilizing these key references to construct the main line, and finally, constructing the ROI square. The total color hand images (230) were used to test and evaluate the newly introduced algorithm; 30 were collected from the internet; and 200 were chosen from the Birjand University Mobile Palmprint Database (BMPD). The hand images include two orientations, left and right, varying sizes and backgrounds, uneven illumination, shadows, and some hand images have items on the finger(s). The experimental findings demonstrate that the introduced algorithm effectively attained 100% and 99.565% sensitivity and accuracy, respectively.
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.
Adaptive Inertia Weight Particle Swarm Optimization for Augmentation Selection in Coral Reef Classification with Convolutional Neural Networks Prabowo, Dwi Puji; Rohman, Muhammad Syaifur; Megantara, Rama Aria; Pergiwati, Dewi; Saraswati, Galuh Wilujeng; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar; Andono, Pulung Nurtantio
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2726

Abstract

Indonesia possesses the world's largest aquatic resources, with 17,504 islands and 6.49 million square kilometers of sea. Located in the coral triangle, Indonesia is home to diverse marine life, including vital coral reefs. However, these reefs face threats from climate change, pollution, and human activities, endangering biodiversity and coastal communities. Therefore, monitoring and preservation are crucial. This study evaluates various augmentation methods for classifying underwater coral reef images using Convolutional Neural Networks (CNNs). Effective augmentation methods are essential due to the unique characteristics of these images. The methodology includes testing different augmentation methods, epoch parameters, and CNN parameters on a coral reef image dataset. Five optimization algorithms (AIWPSO, GA, GWO, PSO, and FOX) are compared. The highest accuracy, 95.64%, is achieved at the 10th epoch. AIWPSO and GA show the highest average accuracies, 93.44%, and 93.50%, respectively, with no significant performance differences among the algorithms. Statistical analysis using the Wilcoxon test indicates a significant difference between training and validation accuracy (p-value = 0.0020). These findings underscore the importance of selecting augmentation methods that align with the characteristics of each optimization algorithm to enhance classification performance. The results provide valuable insights into improving the quality and diversity of input data for classification algorithms in underwater image analysis. They highlight the necessity of matching augmentation methods to specific optimization algorithms to boost accuracy and effectiveness significantly. Future research should explore additional augmentation methods and optimization algorithms further to enhance the robustness and accuracy of underwater image classification.
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. 
Adaptive Deep Convolution Neural Network for Early Diagnosis of Autism through Combining Personal Characteristic with Eye Tracking Path Imaging Kesavan, Revathi; Palanichamy, Naveen; Haw, Su-Cheng; Ng, Kok-Why
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3046

Abstract

Autism is a large set of illnesses related to brain development, also referred to as autism spectrum disorder (ASD). According to WHO reports, 1 in 100 children is expected to have ASD. Numerous behavioral domains are affected, including linguistic, interpersonal skills, stereotypical and repetitive behaviors which represent an extreme instance of a neurodevelopmental abnormality. Identifying ASD can be difficult and exhausting because its symptoms are remarkably identical to those of many other disorders of the mind. Medical professionals can improve diagnosis efficiency by adapting deep learning practices. In clinics for autism spectrum disorders, eye-tracking scan pathways (ETSP) have become a more common instrument. This approach uses quantitative eye movement analysis to study attentional processes, and it exhibits promising results in the development of indicators that can be used in clinical studies for autism.   ASD can be identified by comparing the abnormal attention span patterns of children’s having the disorder to the children’s who are typically developing. The recommended model makes use of two publicly viable datasets, namely ABIDE and ETSP imaging. The proposed deep convolutional network consists of four hidden convolution layers and uses 5-fold cross-validation strategy. The performance of the proposed model is validated against multilayer perceptron (MLP) and conventional machine learning classifiers like decision tree (DT), k-nearest neighbor (KNN) and Random Forest (RF) using metrics like sensitivity, specificity and area under curve (AUC). The findings demonstrated that without the need for human assistance, the suggested model is capable of correctly identifying children with ASD.
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.

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