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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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Max Feature Map CNN with Support Vector Guided Softmax for Face Recognition Herdianti Darwis; Zahrizhal Ali; Yulita Salim; Poetri Lestari Lokapitasari Belluano
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1751

Abstract

Face recognition has made significant progress because of advances in deep convolutional neural networks (CNNs) in addressing face verification in large amounts of data variation. When image data comes from different sources and devices, the identifiability of other classes and the presence of profile face data can lead to inaccurate and ambiguous classification because other classes lack discriminatory power. Furthermore, using a complex architecture with many deep convolutional layers can become very slow in the training process due to a huge amount of Random Access Memory (RAM) usage during the reverse pass of backpropagation. In this paper, we design a light CNN architecture that addresses these challenges. Specifically, we implemented Max-feature-map (MFM) into each convolutional layer to improve the accuracy and efficiency of the CNN. The strength of the support vector-guided SoftMax (SV-SoftMax) is also used in the proposed method to emphasize misclassified points and adaptively guide feature learning. Experimental results show that the 9-Layers CNN with MFM layer and SV-SoftMax outperform VGG-19 with 96.22% validation accuracy and the second rank below FaceNet tested on the same dataset with fewer parameters. Moreover, the model performed well on data that is obtained from various capture devices such as webcam, CCTVs, phone cameras, and DSLR cameras. The implications of this research could extend to scenarios requiring face recognition technology implementation with light size, such as surveillance and authentication systems
3D Scanner Using Infrared for Small Object Marlindia Ike Sari; Anang Sularsa; Rini Handayani; Surya Badrudin Alamsyah; Siswandi Riki Rizaldi
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2050

Abstract

Three-Dimensional scanning is a method to convert various distances set into object visualization in 3-dimensional form. Developing a 3D scanner has various methods and techniques depending on the 3d scanner's purpose and the size of the object target. This research aims to build a prototype of a 3D scanner scanning small objects with dimensions maximum(10x7x23)cm. The study applied an a-three dimensional(3D) scanner using infrared and a motor to move the infrared upward to get Z-ordinate. The infrared is used to scan an object and visualize the result based on distance measurement by infrared. At the same time, the motor for rotating objects gets the (X, Y) ordinates. The object was placed in the center of the scanner, and the maximum distance of the object from infrared was 20cm. The model uses infrared to measure the object's distance, collect the result for each object's height, and visualize it in the graphic user interface. In this research, we tested the scanner with the distance between the object and infrared were 7 cm, 10 cm, 15 cm, and 20 cm. The best result was 80% accurate, with the distance between the object and the infrared being 10cm. The best result was obtained when the scanner was used on a cylindrical object and an object made of a non-glossy material. The design of this study is not recommended for objects with edge points and metal material.
Understanding User Engagement Strategies for Podcasts Videos on Youtube in Indonesia: A Study on Content Creation Kurniawan, Yohannes; Halim, Enrico; Jennifer, Elisa; Pribadi, Fazha Aqsa; Bhutkar, Ganesh; Anwar, Norizan
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2123

Abstract

COVID-19 has transformed human life by utilizing technology to obtain information. Based on Katadata.com, Indonesia ranks second in the world's highest number of podcast listeners in the third quarter of 2021, accounting for 35.6% of the total internet users. Based on YouTube user statistics from Global Media Insight, Indonesia also ranks fourth globally for the highest number of YouTube users in 2023, totaling 139 million. Thus, this study aims to examine the factors that can influence the strategy to attract the right audience in building podcast content and provide recommendations for appropriate user engagement by comparing the genres of current issues and business & finance podcasts on YouTube Indonesia. The research method used is descriptive analytics, using the open-source Netlytics tool to analyze text and automatically summarize and visualize public online conversations on YouTube. The results of this study indicate that current issue genres are more prevalent in Indonesian society, with one of the most influential factors being the topic and guests to currently viral podcasts. This study also analyzes other factors that influence user engagement. Therefore, the findings of this research can be utilized as an opportunity for companies/institutions to enhance their branding/promotion through YouTube video podcasts. This research can also serve as a reference for other podcast content creators in building and improving user engagement on their YouTube channels to attract more interest from Indonesian society.
The Analysis of Organizational Changes using Structural Equation Modelling with Mediating Readiness to Change in Higher Education Rindang Ayu; Nurul-Azza Abdullah; Wan Shahrazad Wan Sulaiman; Mohd Nasir Bin Selamat
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.02252

Abstract

This research study demonstrates that the readiness to change moderates the association between supervisor support and commitment to organizational change. The variable "readiness to change" fulfills this moderating function. The State University in West Sumatra, a prestigious institution of higher education in Indonesia, boasts a faculty comprising 260 committed teaching personnel. The formulation of the questionnaire was grounded upon a predetermined set of criteria. The data analysis process involved utilizing Structural Equation Modelling (SEM) and SmartPLS 3.0 software. The results obtained from using structural equation modeling (SEM) were consistent with recognized metrics such as Cronbach alpha, composite reliability, mean-variance extracted, and evaluation criteria for both measurement and structural models. Furthermore, it also showcased the soundness and reliability of the measurement instruments. The study suggests that the mediating factor of preparation for change plays a role in the association between the provision of high-quality support and the level of commitment towards organizational change. This study contributes to the field of management and organizational leadership by providing insights on how to develop robust change strategies through enhancing employees' readiness for transition. This study makes a valuable contribution to the field of change management by emphasizing the role of readiness to change as a mediating factor in the relationship between supervisor support and organizational change commitment. This research additionally aids organizations in developing grooming and training programs aimed at equipping employees with the necessary skills and knowledge to adapt to change.
Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm Zhukabayeva, Tamara; Adamova, Aigul; Ven-Tsen, Khu; Nurlan, Zhanserik; Mardenov, Yerik; Karabayev, Nurdaulet
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2220

Abstract

The imperfection of existing intrusion detection methods and the changing nature of malicious actions on the attacker's part led to the Internet of Things (IoT) network interaction in an unsafe state. The actual problem of improving the technology of the IOT is counteracting malicious network impacts. In this regard, research and development aimed at creating effective tools for solving applied problems within the framework of this problem are becoming increasingly important.  This study seeks to develop tools for detecting anomalous network conditions resulting from malicious attacks. In particular, the accuracy of the identification of DoS and DDoS attacks is sufficient for operational use. This study analyzes various multi-level architectures, relevant communication protocols, and different types of network attacks. The presented research was conducted on open datasets TON_IOT DATASETS, which include multiple data sources collected from IoT sensors. The modified HyperNEAT algorithm was used as the basis for the development. The NEAT methodology used in the study allows you to combine various network nodes. Results of the study: a neuro-evolutionary algorithm for identifying DoS and DDoS attacks was implemented, integrated, and real-tested based on a multi-level analysis of network traffic combined with various adaptive modules. The accuracy of identifying DoS and DDoS attacks is 0.9242 in the Accuracy metric. The study implies that the proposed approach can be recommended for network intrusion detection, ensuring security when interacting with the IoT.
A Layered Architecture and Taxonomy for Blockchain-empowered Reputation-based Reward Systems Jitendra Singh Yadav; Narendra Singh Yadav; Akhilesh Kumar Sharma
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.01692

Abstract

Blockchain based rating and review systems have changed the operational structure of the traditional market by introducing characteristics like immutability, security, anonymity etc. to liberate users from potential malicious acts of sellers such as altering and hiding ratings or reviews, collusion with users or service providers. The lack of standardization for developing decentralized applications does not depict flow of information and cataloguing of specific functions and roles for a particular set of tasks. The development of decentralized applications for e-commerce systems is in its immature age of progress and has lack of interoperable sharing of data and workflows for new innate systems. Thus, it is significant to catalogue blockchain-based rating and review systems by identifying key parameters to generate a taxonomy and develop a conceptual layered framework for identifying core components and their interaction. This manuscript presents a substantial analysis of existing blockchain-empowered reputation-based reward systems. It uses an iterative approach following observed to rational and rational to observed for taxonomy development. The analysis results identify 11 key parameters for categorizing systems and propose a 4 layered architecture to signify IPFS, P2P network, Blockchain and DApps. The proposed model identifies underlying subsystems, their services, and their interaction. The new taxonomy identifies natural roadmaps in system development process. This study is key because it allows developers to design new reputation-based reward framework in different dimensions by following an open workflow with a common understanding of underlying core entities.
In-Air Hand Gesture Signature Recognition Using Multi-Scale Convolutional Neural Networks Alvin Lim Fang Chuen; Khoh Wee How; Pang Ying Han; Yap Hui Yen
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2359

Abstract

The hand signature is a unique handwritten name or symbol that serves as a proof of identity. Due to its practicality and widespread use, hand signature is still used by financial institutions as a means of verifying and validating the identity of their customers. The emergence of the COVID-19 global pandemic has raised hygiene concerns regarding the conventional touch-based hand signature recognition system, which often requires sharing the acquisition devices among the public. This paper presents in-air hand gesture signature recognition using convolutional neural networks to address this concern. We designed a shallow multi-scale convolutional neural network using 3x3 and 5x5 kernel filter sizes to extract features on different scales. The feature maps from these two filters are then concatenated to provide more robust features, which improve the model’s performance. The experiment results show that the proposed architecture outperforms other architectures, which obtained the highest accuracy of 93.00%. On the other hand, our architecture consumed significantly fewer computational resources, requiring only an average of 3 minutes and 33 seconds to train. Additionally, the performance of the proposed architecture could be further enhanced by integrating it with recurrent neural networks (RNN). This integrated architecture of convolutional recurrent neural networks (C-RNN) can capture spatio-temporal features simultaneously.
Utilization of WebGIS for Visualization of the Distribution of Tourist Destination Religious Objects in Nagari Batuhampar of Lima Puluh Kota Regency, West Sumatera Province Susetyo, Bigharta Bekti; Purwaningsih, Endah; Sutriani, Widia; Purnamasari, Eva; Bagus, Muhamad Ikhwan
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2281

Abstract

Nagari Batuhampar has several tourist attractions planned as object tourist destinations in the strategic plan. However, publication and presentation on social media are less effective in promoting the unique characteristics of tourist attractions. This research aims to identify the distribution of tourist destination objects in   Nagari Batuhampar, followed by comprehensive information. The type of research used is descriptive survey research with the waterfall method, which consists of requirement analysis, system analysis, system implementation, system testing, system evaluation, operation, and maintenance. Data collecting techniques include observation using GPS and documentation, interviews to obtain information for web development, and questionnaires. Furthermore, the built-in data application QGIS 3.32.3” Lima” is open source. WebGIS, built using the Database Management System (DBMS) approach, is designed as software to manage big data. Big data is meant to be a collection of lots of data tailored to the project being carried out, such as mapping the distribution of public facilities and village potential. In this research, DBMS focuses on spatial data and religious and supporting tourism attributes. This is focused on data on religious and supporting tourism attributes. The result found that historical religious tourist attractions dominated the distribution of attractions in Nagari Batuhampar. The WebGIS of Tourist Destination Object was constructed using a waterfall method that was effectively created. This development was conducted through system evaluation tests, resulting in most respondents being satisfied with the process's performance. 
Clustering Defensive Shariah-compliant Stocks Using Financial Performance as the Indicator Nur Sara Zainudin; Choo-Yee Ting; Kok-Chin Khor; Keng-Hoong Ng; Gee-Kok Tong; Suraya Nurain Kalid
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2269

Abstract

Malaysian stocks, including Shariah-compliant stocks, have experienced turbulence last year. Although there are defensive stocks, the well-performing ones are not easily identified. Researchers have proposed various metrics to identify defensive stocks. However, most of the approaches require human intervention. In this study, we focus on Shariah-compliant stocks and propose to automate the labeling of stocks in terms of their financial performance via clustering. The study aims to identify the optimal clustering method to label the clusters. This was achieved by first employing k-Means, Agglomerative, and Mean Shift clustering to group similar stocks before labeling. When labeling, the criteria to distinguish well-performing defensive Shariah-compliant stocks were high dividend yield, low price-earnings ratio, low Beta value, and low price-to-book value. After labelling each stock with its financial performance (Low, Medium, High), we performed classification using Logistic Regression, k-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest to verify the credibility of the labels. Based on the results, the clusters created by k-Means clustering outperformed the rest in matching accuracy. Further investigation was conducted on the k-Means data set by dividing the data according to sector and classifying each sector’s data separately. Logistic Regression outperformed other classification algorithms with an accuracy of 71.5%. The findings also suggested accuracy increased when stocks were classified according to sectors. Further considerations include performing outlier analysis on the data to select well-performing stocks.
Dark Web Financial Fraud Identification Using Mathematical Models in Healthcare Domain Rajawat, Anand Singh; Goyal, S.B.; Solanki, Ram Kumar; Gadekar, Amit; Patil, Dipak
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2600

Abstract

The so-called "dark web" has emerged as the most trustworthy platform for thieves to launch their enterprises. The healthcare industry has become a haven for illegal activities such as the sale of medical gadgets, trafficking in human beings, and the purchase of organs. This is because the sector provides a high level of privacy, which makes it an ideal location for engaging in unlawful operations. In this field of research, linear regression is utilized to uncover previously unknown patterns in customer demand. A vector will be created using a time series of medical equipment purchases to do this. When we look at the data the case firm gave us, we notice that people tend to desire to purchase products in one of three ways. After that, we sort the hospitals into groups according to the course of the trend vector by employing a technique known as "hierarchical clustering," which we apply to the data. According to the research findings, the trend-based clustering method is an excellent way to partition hospitals into subgroups that share similar tendencies. According to our model evaluations, no one model can reliably produce the most accurate forecasts for each cluster when used by itself. Some models can be utilized to make accurate predictions, and these models apply to a wide variety of time series that exhibit various patterns.

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