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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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Design of Personal Mobility Safety System Using AI Park, Hyun Joo; Choi, Kang-Hyeon; Yu, Jong-Won
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.558

Abstract

In this paper, we propose the implementation of a safety device that generates an alarm sound or braking operation to reduce the risk of accidents. It reduces the exposure of risks due to non-wearing by supplementing the function of the helmet for safety. For machine learning, the safety state is learned by using two types of sensing data, and when an abnormal helmet use or speed or drinking driving is detected, an alarm sound is generated and motion is broken to maintain the safe state. By measuring data using a gas sensor, alcohol is checked and this is used as abnormal data. Users form a habit of wearing safety equipment with continuous safety alarm sound and speed braking and proper driving habit by driving in a normal state without drinking alcohol. In addition, the proposed system enables real-time monitoring, thereby reducing risks by continuously maintaining safe driving and wearing protective equipment. The proposed system uses artificial intelligence to discriminate data related to helmet wearing, speed, and drinking in making an electric kickboard for safety, and triggers an alarm or operates the brake to prevent abnormal driving. If the design and function are supplemented, it will become a basic function that can be applied to various equipment of transportation.
Data Mining Techniques for Pandemic Outbreak in Healthcare Nur Izyan Suraya Abdul Satar; Azlinah Mohamed; Azliza Mohd Ali
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.548

Abstract

Pandemic outbreaks such as SARS-CoV, MERS-CoV and Covid-19 have attracted worldwide attention since these viruses have affected many countries and become a global public health issue. In 2019, Covid-19 was announced as a pandemic disease and categorized as a public health emergency globally. It is ranked as the sixth most serious pandemic internationally. This pandemic tracking and analysis require an appropriate method that gives better performance in terms of accuracy, precision and recall that defines its pattern since it involves huge and complicated datasets from the pandemic. Pattern identification is currently applied in many instances due to the rapid growth of data besides having the   potential to generate a knowledge-rich environment which can help to significantly improve the quality of clinical decisions and identify the relationships between data items. Therefore, there is a need to review the techniques in data mining on the pandemic outbreak that focuses on healthcare. The goal of this study was to analyze the algorithms from the data mining method that had been implemented for pandemic outbreaks in past research such as SARS-CoV, MERS-CoV and Covid-19. The result shows that 2 main algorithms, namely Naïve Bayes and Decision Tree, from the classification method, are appropriate algorithms and give more than 90% accuracy in both the pandemic and healthcare. This will be further considered and investigated for future analysis on large datasets of Covid-19 which can help researchers and healthcare practitioners in controlling the infection of the coronavirus using the data mining technique discussed.
Indonesian Online News Extraction and Clustering Using Evolving Clustering Muhammad Alfian; Ali Ridho Barakbah; Idris Winarno
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.537

Abstract

43,000 online media outlets in Indonesia publish at least one to two stories every hour. The amount of information exceeds human processing capacity, resulting in several impacts for humans, such as confusion and psychological pressure. This study proposes the Evolving Clustering method that continually adapts existing model knowledge in the real, ever-evolving environment without re-clustering the data. This study also proposes feature extraction with vector space-based stemming features to improve Indonesian language stemming. The application of the system consists of seven stages, (1) Data Acquisition, (2) Data Pipeline, (3) Keyword Feature Extraction, (4) Data Aggregation, (5) Predefined Cluster using Automatic Clustering algorithm, (6) Evolving Clustering, and (7) News Clustering Result. The experimental results show that Automatic Clustering generated 388 clusters as predefined clusters from 3.000 news. One of them is the unknown cluster. Evolving clustering runs for two days to cluster the news by streaming, resulting in a total of 611 clusters. Evolving clustering goes well, both updating models and adding models. The performance of the Evolving Clustering algorithm is quite good, as evidenced by the cluster accuracy value of 88%. However, some clusters are not right. It should be re-evaluated in the keyword feature extraction process to extract the appropriate features for grouping. In the future, this method can be developed further by adding other functions, updating and adding to the model, and evaluating.
Implementation of an Integrated Online Class Model using Open-Source Technology and SNS Won Ho; Dae-Hyun Lee; Yong Kim
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.668

Abstract

Before Covid-19, the class model was divided into online, offline, and blended learning. Due to Covid-19, we have the only online class environment. We need a new online class model under the new circumstances. In the new model, all technology and educational methods need to be well-adapted, organized and harmonized to compensate for the absence of offline learning sessions. In this paper, we propose a new online class model. Because of the absence of offline sessions, the model emphasizes the integration of synchronous and asynchronous activities seamlessly and effectively. The model also emphasizes the instructor's role as a content prosumer because the instructor in the new model is either reusing other's and the one's own contents or supplying those contents for others. This model uses open-source solutions or free services like Moodle, OBS, Tubestory, and Snap Camera for both budget-saving and stability purposes. It actively uses Moodle's monitoring capability and adopts various learning technologies. It consists of three activity sessions: a pre-Zoom session, a Zoom session, and a post-Zoom session. Each session is composed of modules that describe the process and action for the class. The process, methods, and techniques for each module are explained in this paper. The official students survey for class evaluation held by Kongju national university showed that the new class model's application obtained a higher score than the same class of the previous year that is performed by conventional teaching.
Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities Shafik, Wasswa; Matinkhah, S. Mojtaba; Sanda, Mamman Nur; Shokoor, Fawad
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.373

Abstract

In recent years, the IoT) Internet of Things (IoT) allows devices to connect to the Internet that has become a promising research area mainly due to the constant emerging of the dynamic improvement of technologies and their associated challenges. In an approach to solve these challenges, fog computing came to play since it closely manages IoT connectivity. Fog-Enabled Smart Cities (IoT-ESC) portrays equitable energy consumption of a 7% reduction from 18.2% renewable energy contribution, which extends resource computation as a great advantage. The initialization of IoT-Enabled Smart Grids including (FESC) like fog nodes in fog computing, reduced workload in Terminal Nodes services (TNs) that are the sensors and actuators of the Internet of Things (IoT) set up. This paper proposes an integrated energy-efficiency model computation about the response time and delays service minimization delay in FESC. The FESC gives an impression of an auspicious computing model for location, time, and delay-sensitive applications supporting vertically -isolated, service delay, sensitive solicitations by providing abundant, ascendable, and scattered figuring stowage and system associativity. We first reviewed the persisting challenges in the proposed state-of-the models and based on them. We introduce a new model to address mainly energy efficiency about response time and the service delays in IoT-ESC. The iFogsim simulated results demonstrated that the proposed model minimized service delay and reduced energy consumption during computation. We employed IoT-ESC to decide autonomously or semi-autonomously whether the computation is to be made on Fog nodes or its transfer to the cloud.
Identifying the Requirements of Visually Impaired Users for Accessible Mobile E-book Applications Hashim, Nor Laily; Ba Matraf, Munya Saleh; Hussain, Azham
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.398

Abstract

Books are a medium for communicating information and in recent years have taken the electronic form called e-books. This shift has opened new opportunities for the visually impaired in overcoming their struggles with books in the traditional paper format. Yet, the National Federation of the Blind (NFB) claimed that many e-book applications do not meet needs of the visually impaired. Very few studies had investigated this subject matter hence paving the way for this current study to address the above research gap. As a result, equitable access to e-books for the visually impaired is still limited. Hence, there is now a necessity to design usable and accessible e-book interfaces for the visually impaired. To achieve this goal, it is important to identify the e-book requirements of the visually impaired into their e-book applications. An online survey was conducted involving seven visually impaired students at a local Malaysian university. The target participants’ ages are between 21 and 27 years old. The outcomes of this study identified ten requirements for accessible e-book applications for the visually impaired. Among these requirements are features that enable users to zoom, read aloud, and search for book contents. Besides that, screen reader strategy and text-to-speech are also mandatory. Other requirements include clear text and sound, ease of navigation, high contrast, and high brightness. These requirements will involve the field of Human-Computer Interaction design which is applied particularly in the development of usable and accessible mobile e-book applications for the visually impaired.
Agent-Oriented Modelling for Blockchain Application Development: Feasibility Study LiBin, Michelle Ten; WaiShiang, Cheah; Khairuddin, Muhammad Asyraf B; Mit, Edwin; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.670

Abstract

Blockchain application development has received much attention nowadays. As development is complex and challenging, a systematic approach is needed to improve the product, services, and process quality. Despite the introduction of techniques, there are still inadequate models for demonstrating the blockchain's internal architecture. Hence, there is a gap when developing the blockchain application, a gap in the modelling environment of a blockchain development application. This paper introduces a new insight into blockchain application development through Agent-Oriented Modelling (AOM). AOM is a methodology for complex socio-technical system development, and we believe that it can reduce the complexity of implementing the blockchain application. In this paper, the AOM is used to model a blockchain-based "win a fortune" system, which includes smart contract development. It showcases the feasibility of adopting AOM to model a blockchain enabling application. A usability survey among the novices has further validated the usability and benefits of AOM in the blockchain enabling application development.
AP-based CW Synchronization Scheme in IEEE 802.11 WLANs Lee, Jin-Lee; Kyung, Yeunwoong
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.553

Abstract

In this paper, an optimal CW (Contention Window) synchronization scheme is proposed in IEEE 802.11 WLANs. IEEE 802.11 WLANs operates with DCF (Distributed Coordination Function) mode for the MAC (Medium Access Control). In DCF, CW becomes the minimum CW according to the success of data transmissions and increases exponentially due to the collisions. In this situation, the smaller value of the minimum CW can increase the collision probability because stations have higher opportunity to access the medium. On the other hand, the higher value of the minimum CW will delay the transmission, which can result in the network performance degradation. In IEEE 802.11, since the base minimum CW value is a fixed value depending on the hardware or standard, it is difficult to provide the optimal network performance that can be determined by the flexible CW value according to the number of active stations. In addition, the synchronization of optimal CW is required among mobile stations to adapt the network parameters. Especially for the newly joined stations such as moving or turning on stations, they need to adapt the minimum CW value to get the optimal network performance. The shorter the adaptation time is, the better the network performance can maintain. Therefore, in this paper, AP (Access Point) calculates the optimal CW and shares it with mobile stations using beacon and probe response messages for the fast CW synchronization. Extensive simulation results show that the proposed scheme outperforms the previous schemes in terms of the network throughput and adaptation time.  
Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework Pranoto, Hady; Kusumawardani, Oktaria
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.480

Abstract

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.
Fast Clustering Environment Impact using Multi Soft Set Based on Multivariate Distribution Yanto, Iwan Tri Riyadi; Apriani, Ani; Hidayat, Rahmat; Mat Deris, Mustafa; Senan, Norhalina
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.628

Abstract

Every development activity is always related to human or community aspects. This can also lead to changes in the characteristics of the community. The community's increasing awareness and critical attitude need to be accommodated to avoid the emergence of social conflicts in the future. This research is to find out how the public perception about the impact of development on the environment. Two methods are used, i.e., MDA (Maximum Dependency Attribute) and MSMD (the Multi soft set multivariate distribution function). The MDA is to determine the most influential attribute and the Multi soft set multivariate distribution function (MSMD) is to group the selected data into classes with similar characteristics. This will help the police producer plan the right mediation and take quick activity to make strides in the quality of the social environment. The experiment conducted level of impact based on the clustering results with the greatest number of member clusters is cluster 1 (very low impact) with 32.25 % of total data following cluster 5 (Very High impact) with 24.25 % of total data. The experiment obtains the level of impact based on the clustering results. The greatest number of member clusters is cluster 1 (extremely low impact) with 32.25 % of total data following cluster 5 (Very High impact) with 24.25 % of total data. The scatter area impact is spread at districts 6, 7, 10, 11, the most of very high impact and districts 1,2,3,4,5,8 the lowest impact. 

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