JOIV : International Journal on Informatics Visualization
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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<![CDATA[Predictive Algorithms Analysis to Improve Sustainable Mobility ]]>
<![CDATA[Oscar Dario León-Granizo]]>;
<![CDATA[Miguel Botto-Tobar]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022) ]]>
Publisher : <![CDATA[Politeknik Negeri Padang ]]>
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DOI: 10.30630/joiv.6.1.860
<![CDATA[The work is based on carrying out a comparative analysis of 3 prediction algorithms (Linear Regression, Neural Networks, and KNN), which allow the study of information on georeferential coordinates of moving objects, since through an exhaustive study it will be possible to know the predictions of each one. of them and then proceed to comply with the main objective that is to implement the algorithm with greater accuracy and effectiveness, making use of open Source tools that allow working with Machine Learning and thus be able to analyze the forecasts of traffic congestion that is formed in the surroundings of the University of Guayaquil, because this generates a great inconvenience for students and administrative personnel who belong to this institution and diminish an improvement in sustainable mobility. The methodology used is the Waterfall methodology, as it is a linear model of simple implementation, where each phase of the project was emphasized, allowing possible disorientation of the results to be managed and achieving the development of the proposed project without any inconvenience.]]>
<![CDATA[Batik Classification Using Convolutional Neural Network with Data Improvements ]]>
<![CDATA[Dewa Gede Trika Meranggi]]>;
<![CDATA[Novanto Yudistira]]>;
<![CDATA[Yuita Arum Sari]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.1.716
<![CDATA[Batik is one of the Indonesian cultures that UNESCO has recognized. Batik has a variety of unique and distinctive patterns that reflect the area of origin of the batik motif. Batik motifs usually have a 'core motif' printed repeatedly on the fabric. The entry of digitization makes batik motif designs more diverse and unique. However, with so many batik motifs spread on the internet, it is difficult for ordinary people to recognize the types of batik motifs. This makes an automatic classification of batik motifs must continue to be developed. Automation of batik motif classification can be assisted with artificial intelligence. Machine learning and deep learning have produced much good performance in image recognition. In this study, we use deep learning based on a Convolutional Neural Network (CNN) to automate the classification of batik motifs. There are two datasets used in this study. The old dataset comes from a public repository with 598 data with five types of motifs. Meanwhile, the new dataset updates the old dataset by replacing the anomalous data in the old dataset with 621 data with five types of motifs. The lereng motif is changed to pisanbali due to the difficulty of obtaining the lereng motif. Each dataset was divided into three ways: original, balance patch, and patch. We used ResNet-18 architecture, which used a pre-trained model to shorten the training time. The best test results were obtained in the new dataset with the patch way of 88.88 % ±0.88, and in the old dataset, the best accuracy was found in the patch way on the test data of 66.14 % ±3.7. The data augmentation in this study did not significantly affect the accuracy because the most significant increase in accuracy is only up to 1.22%.]]>
<![CDATA[Prototype of Integrated National Identity Storage Security System in Indonesia using Blockchain Technology ]]>
<![CDATA[Fathiyana, Rana Zaini]]>;
<![CDATA[Yutia, Syifa Nurgaida]]>;
<![CDATA[Hidayat, Dinda Jaelani]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.1.877
<![CDATA[Approximately 29 institutions in Indonesia issue were identifying numbers, such as ID cards, driving licenses, BPJS, etcetera. In general, the identity storage system is designed with a centralized system and managed by each government agency. However, this system has some disadvantages, like data replication and redundancy. Furthermore, the Indonesian government is now undertaking a program through the Ministry of Home Affairs to use population data for public services by providing access to organizations cooperating for population data use. With a centralized database managed by a single entity, data abuse can occur and rely on third parties, the sole authority of the national identity data. The blockchain-based solution described in this paper to integrate a national identity system can provide the advantages of a population data utilization program. The system designed can facilitate convenience in sharing and updating population data while also ensuring the security and integrity of the population data. The citizens do not have to worry about the possibility of data misuse by user institutions. Blockchain technology offers decentralization through the participation of members across a distributed network. There is no single point of failure, and no single user may alter the transaction record. Our proposed approach could help the government of Indonesia secure citizens' private information and increase transparency in information management.]]>
<![CDATA[Developing Fire Evacuation Simulation Through Emotion-based BDI Methodology ]]>
<![CDATA[Paschal, Celine Haren]]>;
<![CDATA[Shiang, Cheah Wai]]>;
<![CDATA[Wai, Sim Keng]]>;
<![CDATA[bin Khairuddin, Muhammad Asyraf]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.1.854
<![CDATA[Fire evacuation simulation is a tool to study human behavior in dealing with fire. It has been used for safety policy management studies, building safety analysis, and human safety understanding. To date, modeling the fire evacuation behavior is paying much attention in which works have been done to design and develop building model, fire model, human decision model, and human emotion decision model. As fire evacuation simulation is important, the BDI methodology is introduced by authors to ease modeling and simulation of human behavior in a fire evacuation. Continue the success of capturing and modeling the human behavior in a fire evacuation. This paper presents the influence of human emotion in fire evacuation simulation. In this paper, the emotion-based BDI methodology is presented with a walkthrough example of how emotion can influence the human decision in a fire spreading scenario. The OCEAN model of personality is used to handle the emotional properties in the methodology. Different people have different types of personalities, which can affect both decision-making and emotion in different situations. A fire evacuation simulation is developed by using the Unity3D game engine. The simulation is created based on the emotion-based BDI methodology presented. Hence, the emotion-based BDI methodology can be used to model human behavior and emotional states in a fire evacuation. Overall, the paper introduces a new insight into how to model human behavior in fire evacuation decision-making systematically.]]>
<![CDATA[Cataract Classification Based on Fundus Images Using Convolutional Neural Network ]]>
<![CDATA[Richard Bina Jadi Simanjuntak]]>;
<![CDATA[Yunendah Fu’adah]]>;
<![CDATA[Rita Magdalena]]>;
<![CDATA[Sofia Saidah]]>;
<![CDATA[Abel Bima Wiratama]]>;
<![CDATA[Ibnu Da’wan Salim Ubaidah]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.1.856
<![CDATA[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.]]>
<![CDATA[Classifying Vehicle Types from Video Streams for Traffic Flow Analysis Systems ]]>
<![CDATA[Imran B. Mu’azam]]>;
<![CDATA[Nor Fatihah Ismail]]>;
<![CDATA[Salama A. Mostafa]]>;
<![CDATA[Zirawani Baharum]]>;
<![CDATA[Taufik Gusman]]>;
<![CDATA[Dewi Nasien]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.1.739
<![CDATA[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.]]>
<![CDATA[Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB ]]>
<![CDATA[Cahyo, Muhammad Pandu Dwi]]>;
<![CDATA[Utaminingrum, Fitri]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.1.785
<![CDATA[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%.]]>
<![CDATA[Design of a Low-area Digit Recognition Accelerator Using MNIST Database ]]>
<![CDATA[Kwon, Joonyub]]>;
<![CDATA[Kim, Sunhee]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.1.855
<![CDATA[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.]]>
<![CDATA[Immersive Applications in Museums: An Analysis of the Use of XR Technologies and the Provided Functionality Based on Systematic Literature Review ]]>
<![CDATA[Komianos, Vasileios]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.1.708
<![CDATA[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.]]>
<![CDATA[Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network ]]>
<![CDATA[Minarno, Agus Eko]]>;
<![CDATA[Cokro Mandiri, Mochammad Hazmi]]>;
<![CDATA[Azhar, Yufis]]>;
<![CDATA[Bimantoro, Fitri]]>;
<![CDATA[Nugroho, Hanung Adi]]>;
<![CDATA[Ibrahim, Zaidah]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.1.857
<![CDATA[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.]]>
