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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.
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Articles 41 Documents
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Search results for , issue "Vol 8, No 1 (2024)" : 41 Documents clear
Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm Tamara Zhukabayeva; Aigul Adamova; Khu Ven-Tsen; Zhanserik Nurlan; Yerik Mardenov; Nurdaulet Karabayev
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.
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 Anand Singh Rajawat; S.B. Goyal; Ram Kumar Solanki; Amit Gadekar; Dipak Patil
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.
Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm Suthami Ariessaputra; Viviana Herlita Vidiasari; Sudi Mariyanto Al Sasongko; Budi Darmawan; Sabar Nababan
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.8.1.1386

Abstract

Sasambo batik is a traditional batik from the West Nusa Tenggara province. Sasambo itself is an abbreviation of three tribes, namely the Sasak (sa) in the Lombok Islands, the Samawa (sam), and the Mbojo (bo) tribes in Sumbawa Island. Classification of batik motifs can use image processing technology, one of which is the Convolution Neural Network (CNN) algorithm. Before entering the classification process, the batik image first undergoes image resizing. After that, proceed with the operation of the convolution, pooling, and fully connected layers. The sample image of Lombok songket motifs and Sasambo batik consists of 20 songket fabric data with the same motif and color and 14 songket data with the same motif but different colors. In addition, there are 10 data points on songket fabrics with other motifs and colors. In addition, there are 5 data points on Sasambo batik fabrics with the same motif and color and 5 data points on Sasambo batik fabrics with the same motif but different colors. The training data rotates the image by 150 as many as 20 photos. Testing with motifs with the same color shows that the system's success rate is 83.85%. The highest average recognition for Sasambo batik cloth is in testing motifs with the same color for data in the database at 93.66%. The CNN modeling classification results indicate that the Sasambo batik cloth can be a reference for developing songket categorization using a website platform or the Android system.
Text Mining for News Forecasting on The Turnback Hoax Website Rio Wirawan; Erly Krisnanik; Artika Arista
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.8.1.1939

Abstract

News has been disseminated swiftly via the internet due to the rapid growth of information technology. The rapid spreading of news often confuses because the truth cannot be ascertained. Additionally, online social media is becoming increasingly popular, making it an excellent environment for propagating false information, including misinformation, phony reviews, advertising, rumors, political remarks, innuendo, etc. This study's specific goal is to classify data using a data mining approach model called text mining so that a system can automatically do the classification. As a result, the study will produce a dataset, which can then be used to create an application using data mining's ability to predict breaking news. An application was produced by employing data mining to forecast recent news. This study was able to classify data using a naive Bayes data mining approach model so that a system can automatically do the classification. The study produced an accuracy of 77% obtained with training data of 82%. From 994 contents, the classification of misleading content reached 33.9%, false content as many as 24.85%, imitation content was 13.48%, fake content reached 11.07%, manipulated content was 9.86%, parody content was 3.22%, satire content was 2.31%, and connection content as many as 1.31%. This study then visualizes the results using bar charts and word clouds. This work also produced datasets with the naïve Bayes method of news data and news that has been valid. Afterward, the dataset will be used in making applications to produce prototypes of computer program applications.
COVID-19 Social Distancing Tracking and Monitoring System (SDMOS-19) Nurafrina Arrysya Binti Abdullah; Nur Diyana Binti Kamarudin; Siti Noormiza Makhtar; Ruzanna Mat Jusoh; Alde Alanda
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.8.1.1199

Abstract

The Coronavirus disease (COVID-19), stemming from the SARS-CoV-2 virus, has garnered global concern as a virulent infectious ailment. Recognized as an epidemic by the World Health Organization (WHO), the persistently mutating virus sustains its transmission within communities. Individuals have been advised to uphold a safe interpersonal distance, notably around five feet, to mitigate its spread during social interactions. Addressing this imperative, an innovative automated social distancing detection system is conceived, leveraging the Convolutional Neural Network (CNN) algorithm. This system operates on two distinct input modes: static images and recorded videos recorded on closed-circuit television (CCTV). Remarkably, the proposed automated system adeptly quantifies and surveils the extent of social distancing among individuals in densely populated settings. A sophisticated framework accurately discerns social distancing compliance, delineating between hazardous and secure intervals via distinct red and green bounding box indicators. The culmination of this endeavor reveals an impressive 90% detection accuracy for both input modes. Notably, this proposed system holds substantial promise for implementation within sprawling premises such as expansive shopping malls or recreational parks. Seamlessly enforcing automated safety distance assessment expedites real-time insights to security departments and other relevant authorities. Consequently, the efficacy of citizens in upholding safe interpersonal distances can be promptly evaluated and, if necessary, corrective measures can be expeditiously instituted. This automated system ensures public health and safety maintenance, particularly in difficult circumstances.
Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model iResNet-RS Didih Rizki Chandranegara; Vizza Dwi Vitanti; Wildan Suharso; Hardianto Wibowo; Sofyan Arifianto
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.8.1.1728

Abstract

Pneumonia, a prevalent inflammatory condition affecting lung tissue, poses a significant health threat across all age groups and remains a leading cause of infectious mortality among children worldwide. Early diagnosis is critical in preventing severe complications and potential fatality. Chest X-rays are a valuable diagnostic tool for pneumonia; however, their interpretation can be challenging due to unclear images, overlapping diagnoses, and various abnormalities. Consequently, expedient, and accurate analysis of medical images using computer-aided methods has become crucial. This research proposes a Convolutional Neural Network (CNN) model, specifically the ResNet-RS Model, to automate pneumonia identification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) technique enhances image contrast and highlights abnormalities in pneumonia images. Additionally, data augmentation techniques are applied to expand the image dataset while preserving the intrinsic characteristics of the original images. The proposed methodology is evaluated through three testing scenarios, employing chest X-ray images and pneumonia dataset. The third testing scenario, which incorporates the ResNet-RS model, CLAHE preprocessing, and data augmentation, achieves superior performance among these scenarios. The results show an accuracy of 92% and a training loss of 0.0526. Moreover, this approach effectively mitigates overfitting, a common challenge in deep learning models. By leveraging the power of the ResNet-RS model, along with CLAHE preprocessing and data augmentation techniques, this research demonstrates a promising methodology for accurately detecting pneumonia in chest X-ray images. Such advancements contribute to the early diagnosis and timely treatment of pneumonia, ultimately improving patient outcomes and reducing mortality rates.
Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy Tien Han Nguyen; Prabhu Paramasivam; Van Huong Dong; Huu Cuong Le; Duc Chuan Nguyen
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.8.1.2637

Abstract

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.
Secure Agent-Oriented Modelling with Web-based Security Application Development Macklin Ak Limpan; Cheah Wai Shiang; Eaqerzilla Phang; Muhammad Asyraf bin Khairuddin; Nurfauza bt Jali
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.8.1.2180

Abstract

Nowadays, privacy and security have become challenges in developing web-based applications. For example, e-commerce applications are threatened with security issues like scammers, SQL injection attacks, bots, DDOs, Server Security, and Phishing. Although various security requirement methodologies are introduced, it has been reported that security consideration is consistently ignored or treated as the lowest priority during the application development process. Hence, the application is being violated by various security attacks. This paper introduces an alternative methodology to secure a web-based application through an Agent-Oriented Modelling extension. The secure AOM starts with Context and Asset Identification. The models involved in this phase are the Goal Model and Secure Tropos model. The second phase is the Determination of Security Objective. The model that will be used is Secure Tropos. The third phase is Risk Analysis and Assessment. The model that will be used is Secure Tropos. The fourth phase is Risk Treatment. In this phase, there is no model, but we use the suggestion from Secure Tropos: to eliminate risk, transfer risk, retain risk, and reduce risk. The fifth phase is Security Requirements Definition. The models that will be used are the scenario model, interaction model, and knowledge model. The last phase is Control Selection and Implementation. The model that will be used is the Behavior Model. We conducted a reliability analysis to analyze the participants' understanding of Secure AOM. From the reliability test, we can conclude that Secure AOM can become the alternative methodology, as the percentage that agrees that Secure AOM can protect users against making errors and mistakes is 80.9%, and 71.9% agree that SAOM can help to prevent users from specifying incorrect model elements and the relation between the model. This result means that over 50% of the participants agree that Secure AOM can be an alternative methodology that supports security risk management.
Developing Compliant Audit Information System for Information Security Index: A Study on Enhancing Institutional and Organizational Audits using Web-based Technology and ISO 25010:2011 Total Quality of Use Evaluation Wahyu Adi Prabowo
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.8.1.1845

Abstract

This study aimed to develop the KAMI 4.1 Index system application based on web application technology to provide a platform for controlled audit implementation and improve data management. The primary goals were to independently assess organizations' ability to obtain ISO 27001:2013 and enhance the audit process's effectiveness and efficiency. The research utilized web application technologies as materials. It employed a systematic approach, focusing on developing a web-based application using the waterfall model's stages of communication, planning, modeling, construction, and deployment. The resulting KAMI 4.1 Index system application introduced a new and efficient platform for controlled audit implementation, featuring an improved user experience and enhanced ease of use by incorporating existing audit calculations from the KAMI 4.1 index. Evaluation based on the ISO 25010:2011 quality of use model yielded a high total quality of use rate of 81.45%, indicating a "very good" categorization. However, areas requiring further research and improvement were identified, including data security, content coverage, freedom from risk, and error tracking. The study also suggested exploring integration possibilities of the audit system with other ISO audit needs, such as a quality assurance system complying with ISO 9001. Further research is necessary to gather information about user criteria and needs in different organizational contexts, ensuring the audit application system meets their requirements. Overall, this research contributes to developing the KAMI 4.1 Index system application and highlights directions for further enhancement and exploration in controlled audit implementation and data management.

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