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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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Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model Aji Gautama Putrada; Nur Alamsyah; Mohamad Nurkamal Fauzan
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.7.4.01498

Abstract

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.
High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor Abdul Rashid, Raghdah Rasyidah; Milleana Shaharudin, Shazlyn; Filza Sulaiman, Nurul Ainina; Zainuddin, Nurul Hila; Mahdin, Hairulnizam; Mohd Najib, Summayah Aimi; Hidayat, Rahmat
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.8.2.2700

Abstract

Due to the discrepancy in resolution between existing global climate model output and the resolution required by decision-makers, there is a persistent need for climate downscaling. We conducted a study to determine the effectiveness of Relevant Vector Machine (RVM), one of the machine learning approaches, in outperforming existing statistical methods in downscaling historical rainfall data in the complex terrain of Selangor, Malaysia. While machine learning eliminates the requirement for manual feature selection when extracting significant information from predictor fields, considering multiple pivotal factors is essential. These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine learning model, and various imputation methods were utilized to manage missing data, such as mean imputation and the KNN algorithm. To assess the performance of the RVM-based rainfall model, we collected a dataset from the Department of Irrigation and Drainage Malaysia. We used Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) as evaluation metrics. This study concluded that Relevance Vector Machine (RVM) models are suitable for forecasting future rainfall since they can support large rainfall extremes and generate reliable daily rainfall estimates based on rainfall extremes. In this study, the RVM model was employed to determine a predictive association between predictand variables and predictors.
Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model iResNet-RS Chandranegara, Didih Rizki; Vitanti, Vizza Dwi; Suharso, Wildan; Wibowo, Hardianto; Arifianto, Sofyan
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 Nguyen, Tien Han; Paramasivam, Prabhu; Dong, Van Huong; Le, Huu Cuong; Nguyen, Duc Chuan
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.
The Evaluation of Entropy-based Algorithm towards the Production of Closed-Loop Edge Crysdian, Cahyo
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: 10.62527/joiv.7.4.1727

Abstract

This research concerns the common problem of edge detection that produces a disjointed and incomplete edge, leading to the misdetection of visual objects. The entropy-based algorithm can potentially solve this problem by classifying the pixel belonging to which objects in an image. Hence, the paper aims to evaluate the performance of entropy-based algorithm to produce the closed-loop edge representing the formation of object boundary. The research utilizes the concept of Entropy to sense the uncertainty of pixel membership to the existing objects to classify pixels as the edge or object. Six entropy-based algorithms are evaluated, i.e., the optimum Entropy based on Shannon formula, the optimum of relative-entropy based on Kullback-Leibler divergence, the maximum of optimum entropy neighbor, the minimum of optimum relative-entropy neighbor, the thinning of optimum entropy neighbor, and the thinning of optimum relative-entropy neighbor. The experiment is held to compare the developed algorithms against Canny as a benchmark by employing five performance parameters, i.e., the average number of detected objects, the average number of detected edge pixels, the average size of detected objects, the ratio of the number of edge pixel per object, and the average of ten biggest sizes. The experiment shows that the entropy-based algorithms significantly improve the production of closed-loop edges, and the optimum of relative-entropy neighbor based on Kullback-Leibler divergence becomes the most desired approach among others due to the production of more considerable closed-loop edge in the average. This finding suggests that the entropy-based algorithm is the best choice for edge-based segmentation. The effectiveness of Entropy in the segmentation task is addressed for further research. 
Measurement on University Websites: A Perspective of Effectiveness Palacios-Zamora, Kerly; Cordova-Morana, Jorge; Mendoza-Cabrera, Denis; Pacheco-Mendoza, Silvia
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.7.3-2.2369

Abstract

This paper highlights the importance of evaluating the performance of university websites and how this can affect the reputation of universities. Different quality evaluation models are analyzed and emphasized in the context of efficiency and how factors such as response time, processing capacity, efficient use of resources, scalability, data transfer rate, concurrency capacity, and fault tolerance can positively or negatively affect websites. In addition, the importance of applying specific techniques to increase efficiency in loading speed is pointed out, such as image optimization, responsiveness on desktop and mobile devices, and content caching, among others, which allow to improve the website's efficiency. To conduct this process, a case study was applied where the university websites were selected, efficiency metrics were defined, and the data provided by the performance measurement tools that provide metrics and quantitative data for the evaluation were collected and analyzed. from the website. The results of the study revealed that there is room for improvement in page load time and page size optimization. In addition, the need to upgrade the performance of mobile devices was identified, given the increasing use of smartphones and tablets to access websites. As a final recommendation, it is advised to implement a comprehensive strategy to improve website performance. This strategy should include optimization of page load time and page size as well as user experience considerations. By achieving optimal performance, universities can offer their users a more satisfying online experience, thus strengthening their reputation and their ability to attract new users.
Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models Mohd Yazed, Muhammad Syukri; Mohd Yunus, Mohd Amin; Ahmad Shaubari, Ezak Fadzrin; Abdul Hamid, Nor Aziati; Amzah, Azmale; Md Ali, Zulhelmi
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.8.2.2756

Abstract

Railway track defects in Malaysia pose significant risks of train derailments and accidents, underscoring the urgency for early and accurate defect detection and classification. This study presents a novel approach utilizing deep learning models, VGG16 and YOLOv5, for detecting and classifying railway track defects, explicitly focusing on corrugation and squat defects. The research's uniqueness lies in its application of these specific models and the composition of a dataset collected from extensive field measurements and inspections across various railway tracks within the Track Network Maintenance Ampang Line in Malaysia. The results demonstrate that these models achieve high precision in defect classification and detection of defects by more than 80%. The proposed methodology provides the railway industry with a powerful tool to streamline maintenance planning and prioritize defect remediation efficiently. Early defect detection can prevent potential accidents and improve safety and operational efficiency. Future studies can expand on these findings by exploring the extension of the proposed techniques to address other types of rail defects. Incorporating a diverse range of scenarios and operating conditions in the dataset could further enhance the models' performance and generalization. Real-time deployment and integration with existing maintenance systems are crucial for practical adoption. This research has strengths but acknowledges limitations. Additional evaluation metrics and a diverse dataset are essential for model performance. Leveraging deep learning models offers a reliable solution for railway maintenance, enhancing safety and efficiency. Addressing these limitations will drive proactive defect management, ensuring safe and reliable railway networks.
Design and Development of Sound and Rhythm Perception Assessment Application for Students with Hearing Impairment Damri, -; Safaruddin, -; Marlina, -; Efendi, Jon; Efrina, Elsa
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: 10.62527/joiv.7.4.2223

Abstract

Technology use is becoming increasingly popular in life, including in educational aspects. Some widely used applications in education include metaverse, blended learning, game learning, cloud-based learning, mobile applications, and social media learning. Apps are generally in the form of software applications or programs designed to run on smartphones. In this study, we propose using applications in assessing children with hearing impairments at school. Design and Development of the Sound and Rhythm Perception Assessment Application uses the ADDIE development model of Analysis, Design, Development, Implementation, and Evaluation. The test subjects in this study were validation test subjects consisting of 3 experts to test the feasibility of the application. Data was collected through a questionnaire in the form of a tool tested for validity and reliability with a score of 90.1% for learning design, 88.9% for layout, and 94.7% for software. Validation was carried out through focus group discussions. The application was tested on four teachers who teach students with hearing impairments. The results of the main field experiment show that teachers can use the application to help them assess students with hearing loss. With availability, the accuracy of the Design and Development of the Sound and Rhythm Perception Assessment Application can be further improved by conducting training with more teachers who teach children with hearing impairments at school.
Secure Agent-Oriented Modelling with Web-based Security Application Development Limpan, Macklin Ak; Wai Shiang, Cheah; Phang, Eaqerzilla; bin Khairuddin, Muhammad Asyraf; bt Jali, Nurfauza
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.
Multi-Temporal Factors to Analyze Indonesian Government Policies regarding Restrictions on Community Activities during COVID-19 Pandemic Fachri Pane, Syafrial; Adiwijaya, Adiwijaya; Dwi Sulistiyo, Mahmud; Akbar Gozali, Alfian
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: 10.62527/joiv.7.4.2415

Abstract

Concerning the implementation of the government policy regarding the Restriction of Community Activities (PPKM) during the COVID-19 pandemic era, there are still discrepancies in the economic sector and population mobility. This issue emerges due to irrelevant data and information in one region of Indonesia. The data differences should be carefully solved when implementing the PPKM policy. Besides, the PPKM must also pay attention to some specific factors related to the real conditions of a region, such as the data on the epidemiology of COVID-19, economic situations, and population mobility. These three are called Multi Factors. Then, based on the data, COVID-19 has a specific spreading period that cannot be repeated and thus is called temporal. Therefore, using the Multi-Temporal Factors approach to identify their correlation with the PPKM policy by applying Machine Learning, such as the Multiple Linear Regression model and Dynamic Factors, is essential. This research aims to analyze the characteristics and correlations of the COVID-19 pandemic data and the effectiveness of the government's policy on community activities (PPKM) based on the data quality. The results show that the accuracy of the multiple linear regression models is 84%. The Dynamic Factor shows that the five most important factors are idr_close, positive, retail_recreation, station, and healing. Based on the ANOVA test, all independent variables significantly influence the dependent one. The linear multiple regression models do not display any symptoms of heteroscedasticity. Thus, based on the data quality, the implementation of PPKM by the government has a practical impact.

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