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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 542 Documents
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Cataract Classification Based on Fundus Images Using Convolutional Neural Network Richard Bina Jadi Simanjuntak; Yunendah Fu’adah; Rita Magdalena; Sofia Saidah; Abel Bima Wiratama; Ibnu Da’wan Salim Ubaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.856

Abstract

A cataract is a disease that attacks the eye's lens and makes it difficult to see. Cataracts can occur due to hydration of the lens (addition of fluid) or denaturation of proteins in the lens. Cataracts that are not treated properly can lead to blindness. Therefore, early detection needs to be done to provide appropriate treatment according to the level of cataracts experienced. In this study, a comparison of cataract classification based on fundus images using GoogleNet, MobileNet, ResNet, and the proposed Convolutional Neural Network was carried out. We compared four CNN architectures when implementing the Adam optimizer with a learning rate of 0.001. The data used are 399 datasets and augmented to 3200 data. This test's best and most stable results were obtained from the proposed CNN model with 92% accuracy, followed by MobileNet at 92%, ResNet at 93%, and GoogLeNet at 86%. We also make comparisons with previous research. Most of the previous studies only used two to three class categories. In this study, the system was improved by increasing system classifies into four categories: Normal, Immature, Mature, and Hypermature. In addition, the accuracy obtained is also quite good compared to previous studies using manual feature extraction. This study is expected to help medical staff to carry out early detection of cataracts to prevent the dangerous effect of cataracts and appropriate medical treatment. In the future, we want to expand the number of datasets to improve the classification accuracy of the cataract detection system.
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 : Politeknik Negeri Padang

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.
Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB Muhammad Pandu Dwi Cahyo; Fitri Utaminingrum
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.785

Abstract

The prediction of COVID-19 cases will continue to experience a surge, inseparable from the presence of a new variant of the coronavirus in the world. One of the best ways to prevent transmission of the virus is to avoid or limit contact with people showing symptoms of COVID-19 or any respiratory infection. The number of medical personnel infected when interacting with patients directly also needs to be an essential concern. Hence, an autonomous robot based on room nameplate recognition systems is a solution. It can be used as an intermediary medium for medical personnel with patients to reduce the intensity of direct contact primarily can be implemented in the hospital. It is expected to reduce the spread of the COVID-19 virus, especially among health workers. Each patient room in the hospital has its room nameplate to be used as a robot reference in navigating. This research aims to make a room nameplate recognition system using the YOLOv4 method on NVIDIA Jetson Nano 2GB that produces an output for 4-wheeled robot navigation control to move. This system is designed to detect rooms within a range of 1-3 meters using 5W and 10W power modes. The testing results based on recognition is obtained an average accuracy value of 95.34%. The system performance test results based on the power mode resulted in the best average computing time of 0.149 seconds. The average value of the accuracy of output integration with the system is 94.73%.
Design of a Low-area Digit Recognition Accelerator Using MNIST Database Joonyub Kwon; Sunhee Kim
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.855

Abstract

Deep neural networks, which is a field of artificial intelligence, have been used in various fields. Deep learning is processed on high-performance GPUs or TPUs. It requires high cost as much as its good performance. Recently, as the demand for edge computing increases, many studies have been conducted to perform complex deep learning operations in a low-computing processor. Among them, a typical study is to lighten the deep learning network. In this paper, we propose a handwritten digit recognition hardware accelerator suitable for edge computing using MNIST database. After setting the correction rate for MNIST to 94% and performing network lighting processes, a hardware structure that can reduce the area of hardware and minimize memory access is proposed. Basically, the network is set as a two-layer fully connected network. The network is modeled with Python and lighten while checking the performance. Network parameters, weighs and biases, are quantized. The pixel number and bit number of MNIST input data are also reduced. The number of MAC units and the processing order of the hardware accelerator are determined so that there is no not used MACs while performing the MAC operations in parallel. It is designed with Verilog HDL and its functions are checked in Modelsim. And then it is implemented in Xilinx Zynq ZC-702 to verify the operations. The designed number recognition accelerator is expected to be widely used in edge devices by reducing the area and memory access.
Immersive Applications in Museums: An Analysis of the Use of XR Technologies and the Provided Functionality Based on Systematic Literature Review Vasileios Komianos
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.708

Abstract

Immersive technologies (Virtual, Augmented, and Mixed Reality) are widely used in cultural heritage for communication, enhancing the visiting experience, and improving learning and understanding. Immersive technologies have found their way into museums and other cultural spaces in various forms and shapes. This work aims to recognize the main forms of immersive technologies and applications in museums and other cultural spaces and provide information on the employed methods, technologies, equipment, and software solutions by conducting a systematic literature review aligned with the PRISMA guidelines. The analyzed literature was collected through a focused search in scientific databases (Scopus, ACM, and IEEE). The relevance to the subject was assessed based on the main technological focus (VR/AR/MR or XR) and the employed technologies. Methods and approaches for realizing their applications were studied and discussed. Thirteen articles were found to meet the selection criteria, of which two focus on VR, six are on AR, two are on Audio-AR, and three are on MR. The results showed that Augmented Reality solutions are preferred for on-site use; Mixed Reality applications started to emerge as Mixed Reality hardware technology became available and Virtual Reality despite being criticized for isolating visitors. The findings cover the existing gap in recent literature and can reveal a set of good practices and innovative ideas for future applications.
Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network Agus Eko Minarno; Mochammad Hazmi Cokro Mandiri; Yufis Azhar; Fitri Bimantoro; Hanung Adi Nugroho; Zaidah Ibrahim
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.857

Abstract

Diabetic Retinopathy (DR) is a disease that causes visual impairment and blindness in patients with it. Diabetic Retinopathy disease appears characterized by a condition of swelling and leakage in the blood vessels located at the back of the retina of the eye. Early detection through the retinal fundus image of the eye could take time and requires an experienced ophthalmologist. This study proposed a deep learning method, the Efficientnet-b7 model to identify diabetic retinopathy disease automatically. This study applies three preprocessing techniques that could be implemented in the dataset "APTOS 2019 Blindness Detection". In preprocessing technique trial scenarios, Usuyama preprocessing technique obtained the best results with accuracy of 89% of train data and 84% in test data compared to Harikrishnan preprocessing technique which has 82% accuracy in test data, and Ben Graham preprocessing has 81% accuracy in test data. In this study, Hyperparameter tuning was conducted to find the best parameters for use on the EfficientNet-B7 Model. In this study, we tested the Efficientnet-B7 model with an augmentation process that can reduce the occurrence of overfitting compared to models without augmentation. Preprocessing techniques and augmentation techniques can influence the proposed EfficientNet-B7 model in terms of performance results and reduce the overfitting of models.
Design of a Big-data-Based Decision Support System for Rational Cultural Policy Establishment Youngseok Lee; Gimoon Cho; Jungwon Cho
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.937

Abstract

This paper proposes a technique for designing a decision-making system based on big data to support rational cultural policy decisions. To identify a rational cultural policy, it is necessary to extract a comparable index for cultural policy and analyze and process factors in terms of cultural supply and cultural consumption. Analyzed and processed supply indices and consumption indices become the basic input data for calculating additional cultural indices that can be measured at the cultural level of each region. Regional cultural indices are treated as independent variables in terms of cultural supply, and target variables are considered in terms of cultural demand. Two corresponding types of regression models are established. Based on the eXtreme gradient boosting and light gradient boosting machine algorithms, which are representative algorithms for calculating cultural indicators, we attempted to construct and analyze a model of the proposed system. The developed model is designed to predict the demand index according to the regional cultural supply index. It was confirmed that the demand side could be changed based on supply-side items by using the proposed technique to support decision-making. Due to the complexity of the policy environment of modern society, mixing various policy tools targeting multiple functions is accepted as a common basis for policy design, but institutional arrangements are needed to reflect the results of various data analyses in budget decision-making. This will be possible to produce data based on effectiveness and suggest appropriate rational policies and decisions.
Customization of Cost Allocation Monitoring Report for Improving Activity-Based Costing Process Risma Nur Damayanti; Muhardi Saputra; Tien Fabrianti Kusumasari
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.927

Abstract

In the age of a global competition environment, accurate costing measurement is essential for every company. The more accurate allocation process to final outputs indicates the potential impact a company's decision has on costs. Activity-based costing is a technique for allocating organizational costs to activities that utilize the organization's resources and then tracing the costs of these activities to products, consumers, or distribution channels that generate profits or losses for the business. With a large number of cost allocations in the business processes, it makes it difficult for companies to identify the number of costs that have been allocated, especially if the data that must be processed is in large quantities. To overcome this problem, it is necessary to require cost mapping for the business process from resource to cost center to compare the number of costs that have been allocated. This research discusses the application of monitoring reports by using ALV customization in XYZ Ltd. This report was created using an iterative and incremental model approach. The simulation results show a 50% reduction of the time to execute the customization monitoring report, and it only takes one step to generate reports and analyze data. The results of this research are expected to be used as a study to provide the right solution in facilitating the process of checking the cost allocation on ABC to objectively monitor and analyze each business process (resource, activity, and cost object) and support the decision-making process.
The Use of Image Processing and Sensor in Tomato Sorting Machine by Color, Size, and Weight Marlindia Ike Sari; Rizal Fajar; Tedi Gunawan; Rini Handayani
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.944

Abstract

Tomatoes are a popular vegetable in Indonesia, where production increases every year according to market demand. The large production requires proper post-harvest handling both in quality and time. It has been well-known that sorting and grading are the first and foremost processes in the post-harvest process of tomatoes. Sorting tomatoes can be conducted by color and adjusted to the target market. The automation process in the post-sorting and grading process can save time and resources. This research proposes a sorting system that sorts tomatoes based on color, size, and weight. Tomatoes are sorted by red, yellow, and green colors. The detection of color and size was carried out by image processing with the OpenCV library. The color detection was carried out by HSV's red, yellow, and green values. In comparison, the dimensional measurement was carried out by determining the outermost point of the detected object both vertically and horizontally. At the same time, tomatoes' weight was measured by a weight sensor. This system was implemented into a prototype sorting system with a webcam, Arduino, a conveyor, and motors. The final part was a storage box used to accommodate tomatoes based on grading. The implementation has the maximum results for detecting color with 100% accuracy and measuring weight with 95% accuracy. However, it still needs development for dimensional measurements. In this research, it has only 5% accuracy. This proposed system can be used both in software and hardware design as an inline tomato sorting tool.
An Assessment Algorithm for Indoor Evacuation Model Khyrina Airin Fariza Abu Samah; Amir Haikal Abdul Halim; Zaidah Ibrahim
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.933

Abstract

The public buildings increased significantly with the economy's growth and the population's advancement. The complexity of the indoor layout and the involvement of many people cause the indoor evacuation wayfinding to the nearest exit to be more challenging during emergencies such as fire. In order to overcome the problem, each building is compulsory to follow the standard evacuation preparedness required by Uniform Building By-Law (UBBL). Researchers have also developed evacuation models to help evacuees evacuate safely during the evacuation from a building. However, building owners do not know which evacuation model is suitable for implementing the chosen high-rise building. Two problems were identified in choosing a suitable evacuation model during the decision-making process. First, many developed evacuation models focus on studying different features of evacuation behavior and evacuation time. Second, the validation and comparison of the evacuation model is the missing process before applying the suitable evacuation model. Both validation and comparison procedures were made independently without any standard assessment that encapsulates the critical incident features during the indoor evacuation and virtual spatial elements. Therefore, this research proposed an indoor evacuation assessment algorithm to solve the problem. The assessment algorithm refers to the elements developed in our previous study. We determined attributes, executed simulations, and evaluated the cluster performance using the developed framework. The outcome can help the building owners assess which suitable existing evacuation model is the best to implement at the chosen building.

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