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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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Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes Binti Muhammad Zahruddin, Nursyuhadah Alghazali; Kamarudin, Nur Diyana; Mat Jusoh, Ruzanna; Abdul Fataf, Nur Aisyah; Hidayat, Rahmat
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.7.4.2454

Abstract

This study predicts student performance at Universiti Pertahanan Nasional Malaysia (UPNM) based on their socio-demographic profile; it also determines how a prediction algorithm can be used to classify the student data for the most significant demographic attributes. The analytical pattern in academic results per batch has been identified using demographic attributes and the student's grades to improve short-term and long-term learning and teaching plans. Understanding the likely outcome of the education process based on predictions can help UPNM lecturers enhance the achievements of the subsequent batch of students by modifying the factors contributing to the prior success. This study identifies and predicts student performance using data mining and classification techniques such as decision trees, neural networks, and k-nearest neighbors. This frequently adopted method comprises data selection and preparation, cleansing, incorporating previous knowledge datasets, and interpreting precise solutions. This study presents the simplified output from each data mining method to facilitate a better understanding of the result and determine the best data mining method. The results show that the critical attributes influencing student performance are gender, age, and student status. The Neural Networks method has the lowest Root of the Mean of the Square of Errors (RMSE) for accuracy measurement. In contrast, the decision tree method has the highest RMSE, which indicates that the decision tree method has a lower performance accuracy. Moreover, the correlation coefficient for the k-nearest neighbor has been recorded as less than one.
Improved Image Classification Task Using Enhanced Visual Geometry Group of Convolution Neural Networks Zakaria, Nurzarinah; Mohmad Hassim, Yana Mazwin
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.7.4.1752

Abstract

Convolutional Neural Networks (CNNs) have become essential to solving image classification tasks. One of the most frequent models of CNNs for image classification is the Visual Geometry Group (VGG). The VGG architecture is made up of multiple layers of convolution and pooling processes followed by fully connected layers. Among the various VGG models, the VGG16 architecture has gained great attention due to its remarkable performance and simplicity. However, the VGG16 architecture is still prone to have many parameters contributing to its complexity. Moreover, the complexity of VGG16 may cause a longer execution time. The complexity of VGG16 architecture is also more highly prone to overfitting and may affect the classification accuracy. This study proposes an enhancement of VGG16 architecture to overcome such drawbacks. The enhancement involved the reduction of the convolution blocks, implementing batch normalization (B.N.) layers, and integrating global average pooling (GAP) layers with the addition of dense and dropout layers in the architecture. The experiment was carried out with six benchmark datasets for image classification tasks. The results from the experiment show that the network parameters are 79% less complex than the standard VGG16. The proposed model also yields better classification accuracy and shorter execution time. Reducing the parameters in the proposed improved VGG architecture allows for more efficient computation and memory usage. Overall, the proposed improved VGG architecture offers a promising solution to the challenges of long execution times and excessive memory usage in VGG16 architecture. 
E-Nose for Piston Ring and Cylinder Block Condition Detection of Motorcycle Engine Based on MyRIO LabVIEW Programming Andrizal, -; Antonisfia, Yul; Alfitri, Nadia; Junaldi, -
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.8.1.2221

Abstract

This study has created a system capable of identifying the condition of the piston ring and cylinder block of a 4-stroke motorcycle engine using petrol or similar through exhaust emissions. Multisensory gas, sensitive to changes in CO, CO2, NOx, and HC gas elements and compounds, is installed as an input to the exhaust channel and integrated using LabVIEW programming on the NI myRIO module. Multisensory data is processed using the FFT and the backpropagation method to classify whether the piston rings and engine cylinder block are in good or damaged condition. Tests have been carried out on motorbikes with piston rings and engine cylinder blocks that are in good, damaged, or unknown condition. During the test, the target error value for motorcycles with piston rings and engine cylinder blocks in good or damaged condition is less than 1%. The system can distinguish the condition of the piston ring and cylinder block of a motorcycle engine that is 100% optimal and 100% damaged with an error of 0% compared to the compression test method, and the maximum error is 20% Compared to the technician's manual method. Ten motorcycles were randomly tested in unknown conditions; 50% were in good condition, and 50% were damaged. For further development, an electronic nose system can detect engine combustion conditions and damage to cylinder rings and 4-stroke motorbike engine blocks based on exhaust emissions.
Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method Pratama, I Putu Agus Eka; Raharja, I Made Sunia
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.7.4.1762

Abstract

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.
Detecting Need-Attention Patients using Machine Learning Law, Theng Jia; Ting, Choo-Yee; Zakariah, Helmi
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.8.2.2277

Abstract

In healthcare, detecting patients who need immediate attention is difficult. Identifying the critical variables is challenging in patient detection because human intervention in variable selection is required. Consequently, patients who need immediate attention often experience prolonged waiting times. Researchers have investigated various approaches to identify those who require attention. One of the techniques is leveraging Artificial Intelligence (AI). However, identifying the optimal feature set and predictive model is complex. Therefore, this study has attempted to (i) identify the critical features and (ii) develop and evaluate predictive models in detecting those who need attention. The dataset is collected from one of the healthcare companies. The dataset collected contains 67 variables and 51102 records. It consists of patient information and questionnaires answered by each participant registered in the Selangor Saring Program. Important features were identified in detecting those who need attention on treated data. Multiple classifiers were developed due to their simplicity. The models were evaluated before and after hyperparameter tuning based on accuracy, precision, recall, F1-score, Geometric Mean, and Area Under the Curve. The findings showed that the Stacking Classifier produced the highest accuracy (69.9%) when using the blood dataset. In contrast, Extreme Gradient Boosting achieved the highest accuracy (81.7%) when the urine dataset was used. This work can be extended to explore the incorporation of Points of Interest and geographical data near patients’ residences and study other ensemble models to enhance the performance of detecting those who need attention.
Forum Text Processing and Summarization Mak, Yen-Wei; Goh, Hui-Ngo; Lim, Amy Hui-Lan
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.8.1.2279

Abstract

Frequently Asked Questions (FAQs) are extensively studied in general domains like the medical field, but such frameworks are lacking in domains such as software engineering and open-source communities. This research aims to bridge this gap by establishing the foundations of an automated FAQ Generation and Retrieval framework specifically tailored to the software engineering domain. The framework involves analyzing, ranking, performing sentiment analysis, and summarization techniques on open forums like StackOverflow and GitHub issues. A corpus of Stack Overflow post data is collected to evaluate the proposed framework and the selected models. Integrating state-of-the-art models of string-matching models, sentiment analysis models, summarization models, and the proprietary ranking formula proposed in this paper forms a robust Automatic FAQ Generation and Retrieval framework to facilitate developers' work. String matching, sentiment analysis, and summarization models are evaluated, and F1 scores of 71.31%, 74.90%, and 53.4% were achieved. Given the subjective nature of evaluations in this context, a human review is used to further validate the effectiveness of the overall framework, with assessments made on relevancy, preferred ranking, and preferred summarization. Future work includes improving summarization models by incorporating text classification and summarizing them individually (Kou et al, 2023), as well as proposing feedback loop systems based on human reinforcement learning. Furthermore, efforts will be made to optimize the framework by utilizing knowledge graphs for dimension reduction, enabling it to handle larger corpora effectively
Classification of Dermoscopic Images Using CNN-SVM Minarno, Agus Eko; Fadhlan, Muhammad; Munarko, Yuda; Chandranegara, Didih Rizki
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.8.2.2153

Abstract

Traditional machine learning methods like GLCM and ABCD rules have long been employed for image classification tasks. However, they come with inherent limitations, primarily the need for manual feature extraction. This manual feature extraction process is time-consuming and relies on expert domain knowledge, making it challenging for non-experts to use effectively. Deep learning methods, specifically Convolutional Neural Networks (CNN), have revolutionized image classification by automating the feature extraction. CNNs can learn hierarchical features directly from the raw pixel values, eliminating the need for manual feature engineering. Despite their powerful capabilities, CNNs have limitations, mainly when working with small image datasets. They may overfit the data or struggle to generalize effectively. In light of these considerations, this study adopts a hybrid approach that leverages the strengths of both deep learning and traditional machine learning. CNNs are automatic feature extractors, allowing the model to capture meaningful image patterns. These extracted features are then fed into a Support Vector Machine (SVM) classifier, known for its efficiency and effectiveness in handling small datasets. The results of this study are encouraging, with an accuracy of 0.94 and an AUC score of 0.94. Notably, these metrics outperform Abbas' previous research by a significant margin, underscoring the effectiveness of the hybrid CNN-SVM approach. This research reinforces that SVM classifiers are well-suited for tasks involving limited image data, yielding improved classification accuracy and highlighting the potential for broader applications in image analysis.
Decoding Innocence: Advancing Forensic Facial Discrimination through Comparative Analysis of Conventional CNN and Advanced Architectures Ahmed, Mirza Jamal; Abdullah, Nurul Azma
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.8.2.2755

Abstract

The field of Digital Image Forensics (DIF) faces a critical issue in accurately identifying children in digital images, notably in cases involving the proliferation of child sexual abuse content. Existing techniques face hurdles due to model architecture limitations, dataset suitability concerns, and classification imbalance, impeding their ability to recognize children to deter pornographic images. Addressing this challenge, this study introduces Implicit Feature Extraction (IFE), a specialized approach for distinguishing child and adult images in object detection. Leveraging Convolutional Neural Networks (CNNs), the IFE method automates the extraction of discriminative facial features, surpassing the constraints of Explicit Feature Extraction (EFE) methods, which achieve an accuracy of around 70%. The research focuses on three core objectives introducing IFE for detailed face feature detection in DIF's child and adult image identification, implementing IFE with CNNs to enhance image classification, and conducting a thorough evaluation of the proposed technique's performance using key metrics like accuracy and balanced classification results and comparing the result with a basic CNN model’s performance. This research's significance lies in its notable contributions to digital image forensics, particularly in combatting child exploitation. The fusion of IFE with CNNs showcases 92% accuracy in distinguishing child and adult images, promising advancements with practical implications in child protection and forensic investigations. The comprehensive evaluation using the UTKFace dataset underscores the proposed technique's efficacy, marking a substantial improvement in child image identification within digital image forensics.
Denoising Ambulatory Electrocardiogram Signal Using Interval Dedependent Thresholds based Stationary Wavelet Transform Hermawan, Indra; Sevani, Nina; F. Abka, Achmad; Jatmiko, Wisnu
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.8.2.2428

Abstract

Noise contamination in electrocardiogram (ECG) monitoring systems can lead to errors in analysis and diagnosis, resulting in a high false alarm rate (FAR). Various studies have been conducted to reduce or eliminate noise in ECG signals. However, some noise characteristics overlap with the frequency range of ECG signals, which occur randomly and are transient. This results in shape alteration and amplitude reduction in P and R waves. The author proposed a framework for eliminating noise in ECG signals using the stationary wavelet transform method and interval-dependent thresholds (IDT) based on the change point detection method to address these challenges. The proposed framework decomposes the input electrocardiogram (ECG) signal at a specific level using the Stationary Wavelet Transform method, resulting in detail and approximation coefficients. Interval detection focuses on the initial detailed coefficient, d1, chosen due to its significant content of noise coefficients, especially high-frequency noise. Subsequently, threshold values are computed for each interval. Hard and soft thresholding processes are then applied individually to each interval. Finally, reconstruction occurs using the inverse stationary wavelet transform method on the threshold coefficient outcomes. Two measurement matrices, root mean square error (RMSE) and percentage root mean squared difference (PRD), were used to measure the performance of the proposed framework. In addition, the proposed framework was compared to stationary wavelet transform (SWT) and discrete wavelet transform (DWT). The test results showed that the proposed method outperforms DWT and SWT. The proposed framework obtained an average increase in RMSE scores of 18% and 45% compared to the SWT and DWT methods, respectively, and PRD values of 17% and 37% compared to the SWT and DWT methods, respectively. So, using IDT in the stationary wavelet transform method can improve the denoising performance. With the development of this new framework for denoising ECG signals, we hope it can become an alternative method for other researchers to utilize in denoising ECG signals.
SCOV-CNN: A Simple CNN Architecture for COVID-19 Identification Based on the CT Images Haryanto, Toto; Suhartanto, Heru; Murni, Aniati; Kusmardi, Kusmardi; Yusoff, Marina; Zain, Jasni Mohammad
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.8.1.1750

Abstract

Since the coronavirus was first discovered in Wuhan, it has widely spread and was finally declared a global pandemic by the WHO. Image processing plays an essential role in examining the lungs of affected patients. Computed Tomography (CT) and X-ray images have been popularly used to examine the lungs of COVID-19 patients. This research aims to design a simple Convolution Neural Network (CNN) architecture called SCOV-CNN for the classification of the virus based on CT images and implementation on the web-based application. The data used in this work were CT images of 120 patients from hospitals in Brazil. SCOV-CNN was inspired by the LeNet architecture, but it has a deeper convolution and pooling layer structure. Combining seven and five kernel sizes for convolution and padding schemes can preserve the feature information from the images.  Furthermore, it has three fully connected (FC) layers with a dropout of 0.3 on each. In addition, the model was evaluated using the sensitivity, specificity, precision, F1 score, and ROC curve values. The results showed that the architecture we proposed was comparable to some prominent deep learning techniques in terms of accuracy (0.96), precision (0.98), and F1 score (0.95). The best model was integrated into a website-based system to help and facilitate the users' activities. We use Python Flask Pam tools as a web server on the server side and JavaScript for the User Interface (UI) Design

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