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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 1,172 Documents
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Development of a Decision Support System Based on New Approach Respond to Criteria Weighting Method and Grey Relational Analysis: Case Study of Employee Recruitment Selection Megawaty, Dyah Ayu; Damayanti, Damayanti; Sumanto, Sumanto; Permata, Permata; Setiawan, Dandi; Setiawansyah, Setiawansyah
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2744

Abstract

The purpose of this research is to propose a new approach in the criteria weighting method using the RECA method, the RECA method can help provide a systematic and structured framework for determining criteria weights in multi-criteria decision making. The determination of weights using the RECA method is to increase objectivity and accuracy in the candidate assessment and selection process by determining the appropriate weight for each criterion based on responses and assessments from experts or stakeholders. Testing the RECA Method with Multi Attribute Decision Making (MADM) techniques is an important step in measuring the effectiveness of the RECA Method in the context of multi-criteria decision making. Ranking tests using Spearman correlation between the RECA method and other methods such as SAW with a correlation value of 1, MOORA with a correlation value of 0.9636, MAUT with a correlation value of 0.9515, WP with a correlation value of 0.891, SMART with a correlation value of 0.9636, and TOPSIS with a correlation value of 0.8788 show a high level of rank consistency between the RECA method and these methods. This indicates that the RECA Method has a strong ability to generate similar candidate rankings with other methods, validating its reliability and consistency in the context of multi-criteria decision making. Implications for further research include exploring the application of the RECA method in different decision-making contexts other than recruitment, such as performance evaluation, project selection, or supplier selection. Further research could investigate the integration of the RECA method with other decision-making methods or algorithms to improve its performance and applicability in complex decision environments. Comparative studies with larger sample sizes and diverse datasets can provide deeper insights into the effectiveness and reliability of the RECA method compared to other methods.
Datasets for Artificial Intelligence-based Spine Analysis: A Scoping Review Zaim Ahmad, Muhammad Shahrul; Ab. Aziz, Nor Azlina; Siong, Lim Heng; Ghazali, Anith Khairunnisa
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3042

Abstract

The advancement of artificial intelligence (AI) and intense learning is key to automating the diagnosis and inspection of spinal-related pathologies. This automation reduces the need for human manual analysis. Reducing the burden on the healthcare system and the risk of human error. Spine medical images have several modalities, such as X-rays, computed tomography (CT), and magnetic resonance imaging (MRI). Each modality captured the vertebral features differently. The choice of modality affects the performance of the applied algorithms. It is also important to note that a large amount of data is better for training AI algorithms, profound learning algorithms. However, medical images are often limited owing to privacy concerns and the lack of open-source databases. Therefore, it is essential to identify data sources to ensure the success of AI projects for spine analysis. This review discusses available datasets and their characteristics, such as modality, size, and labels.  Additionally, the demographics and applications of the data were also discussed. The platform utilized to obtain related literature in this study is Lens. A scoping review was used in this study to extract information from related literature. The number of literature included in this study is 39. A total of 43 datasets, which include 32 private and 11 public datasets, are discussed in this review. This work will benefit researchers and developers developing an AI-based spinal analysis system.
EEG Power Analysis of Children with Autism Spectrum Disorders (ASD) Based on EIBI Curriculum Levels Rahmahtrisilvia, Rahmahtrisilvia; Setiawan, Rudi; Sopandi, Asep Ahmad; Efrina, Elsa; Kusumastuti, Grahita
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2211

Abstract

Early Intervention Behavioral Therapy as a method has been shown to aid children diagnosed with Autism in adjusting behavior through Applied Behavior Analysis. While there are three levels of ABA, EIBI does not provide a concrete metric of what separates between the individual levels. The current study focuses on differentiating the electrical patterns found in EEG in children and plans to explore how EIBI can serve across the ABA spectrum. The electrodes F3, F4, C3, C4, P3, P4, O1, and O2 were used to capture the EEG signals and were utilized in estimating the power, spectral density using the Welch method. It was observed during the statistical examination that there existed differences in the results of power across the frequency band amongst the groups. The higher levels of Alpha lead us to believe that there was better emotional management. The chronic group was shown to have more prominent Delta power reflecting weakened control. Comparatively, beginning level’s theta power was found to be higher across all groups showcasing change in attention requiring tasks. Due to greater focus being placed on the lower range frequency activity there existed no noteworthy changes in the Beta and Gamma portions. These findings highlight the role of EIBI in neuromodulation in the Alpha and Delta bands, and its application in the enhancement of emotional and neurological stability. EEG is an effective measure as it quantifies EIBI outcomes. Further studies should examine the long-term effects and enhance curriculum concepts to increase the efficacy of the interventions.
Why People Benefit from Online Learning: Empirical Assessment from Jordan Gharaibeh, Malik Khlaif; Gharaibeh, Natheer Khleaf
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2613

Abstract

Most countries have imposed online learning on universities and schools due to the COVID-19 pandemic. These days, despite the end of the impact of the COVID-19 pandemic on the educational sector, many countries in the world are still adopting this type of education and trying to develop its methods due to the many benefits it provides. The main objective of conducting this study is to determine the main factors affecting the acceptance of online learning in Jordan. The data were analyzed using SmartPLS 4. 940 questionnaires were distributed in Irbid and Amman. The study's results supported the hypotheses, as it was found that the acceptance of e-learning is statistically and positively associated with the four variables. This study provides essential guidelines for decision-makers and those in charge of the educational process, as it supports the body of knowledge with new variables that were not used in previous studies. Online learning is considered inevitable for adoption in universities and schools, especially when looking at the benefits that institutions derive from its adoption. Saving time, effort, and costs are the most important benefits when applying online learning. This study attempted to determine the main factors affecting the acceptance of online learning in Jordan. The study's results aligned with the hypotheses that technological development, women's empowerment, disabilities, and environmental benefits significantly affect the acceptance of online learning. This study presents a new model and theoretical framework that researchers in this field can build upon.
Enhanced U-Net Architecture for Glottis Segmentation with VGG-16 Aldi, Febri; Yuhandri, Yuhandri; Tajuddin, Muhammad
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3088

Abstract

Laryngeal endoscopic image analysis with segmentation techniques has great potential in detecting various diseases in the glottic area, which is essential for early diagnosis and proper treatment. This study proposes developing the U-Net architecture by integrating the VGG-16 model, aiming to improve the accuracy in detecting glottic areas. VGG-16 is applied to the encoder and bridge sections so that the model can take advantage of previously learned knowledge. This modification is expected to improve segmentation performance compared to standard U-Net, especially in handling variations in laryngeal image complexity. The dataset used consisted of 1,200 images taken randomly from the BAGLS website, a collection of laryngeal endoscopic image data rich in variation. The training results show that the standard U-Net produces an accuracy of 0.9995, IoU 0.6744, and DSC 0.7814. The improved U-Net showed a significant performance improvement, with an accuracy of 0.9998, an IoU of 0.8223, and a DSC of 0.9153. This improvement confirms that modifying the U-Net architecture using VGG-16 provides superior results in detecting glottic areas precisely. VGG-16 also helps model performance in overcoming the problem of smaller datasets. In addition, both models were tested using relevant evaluation metrics, and the test results showed that the improved U-Net consistently outperformed other CNN-based segmentation methods. These advantages show that the proposed approach improves accuracy and contributes significantly to developing glottic disease detection methods through laryngeal endoscopic image analysis, which can ultimately support clinical practice in detecting abnormalities in glottis more effectively.
Using Artificial Neural Networks to Forecasting Carbon Dioxide Emissions in Iraq Ahmed, Shaymaa Mohammed; Sheab, Gheada Ibrahim; Hasan, Arshad Hameed; Hanon, Muammel M.
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2456

Abstract

This paper explores the application of ANN (artificial neural networks) to forecast CO2 emissions in Iraq until 2028. ANNs are able to model non-linear dynamics of time series data which eventually leads to accurate forecasts without any statistical assumption about the features of a dataset. The authors developed a simple single-input feedforward ANN model with the yearly CO2 emission data from 1991 to 2023 as the input to project the future emissions using the year. Levenberg-Marquardt algorithm was used for the network training. The model performed well on the training, validation, and testing datasets with minimal error rates and R-squared values of 1, which implied that the regression demonstrated a good fit between targets and outputs. The performance of ANNs in forecasting was evaluated. The mean squared error (MSE=0.1325) and root mean squared error (RMSE = 0.3641) values were low, highly predictive of small forecasting errors. R2 is quite high (0.946), indicating the model could explain as much as 94.6% of the variances in the actual data. The mean absolute percentage error equalled 8.01%, which signifies a good forecast with less than 10% error. The forecast of 2028 shows per capita emissions reaching 3.649 tons, which may be affected by population growth, economic development, or infrastructure changes that will be put into place. Despite renewables, efficiency, and emissions control or policies the growth curve can be replaced. This model serves as a data-driven instrument for future Iraqi CO2 emissions forecasting in order to develop climate change mitigation policies which are not time series statistical assumptions. It could also be extended to other greenhouse gases and countries, which is possible. This paper shows that ANNs can predict emissions that are accurate and reliable for decision-making which helps to reduce the country's carbon footprint and climate change.
Multi Task Deep Learning with Transformer Encoder Decoder for Semantic Segmentation Indah, Komang Ayu Triana; Darma Putra, I Ketut Gede; Sudarma, Made; Hartati, Rukmi Sari
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.1978

Abstract

Visual understanding is one of the core elements of computer vision consisting of image classification, object detection, and segmentation. The system applies a multilayer process to obtain complex image and video understanding using deep learning methods to convert the images to text. Therefore, this study aimed to extract video in the form of frames followed by the application of Transformer and Inception V3 architectures to the image captioning process. The synchronization was based on Multi-task Deep Learning method developed by combining Convolutional Neural Network (CNN) system in the image area, Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) in the sentence area, Caption Content Network (CCN), and Relational Network Context (RCN). Moreover, Transformer Encoder-Decoder architecture was used in the process of labeling and determining the relationships between objects. The results of the image-to-text conversion process were determined by comparing prospective translated text with one or more references. This was achieved using accuracy and loss validation tables to provide graphical comparisons between the number of epochs and losses. The test results showed that the validation data accuracy was 70.166% while the loss was 22,648% and this showed more epoch iterations led to greater validation accuracy.Keywords— Visual Understanding, Transformer, Encoder, Decoder
A Novel Network Optimization Framework Based on Software-Defined Networking (SDN) and Deep Learning (DL) Approach Osman, Muhammad Fendi; Mohd Isa, Mohd Rizal; Khairuddin, Mohammad Adib; Mohd Shukran, Mohd ‘Afizi; Mat Razali, Noor Afiza
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2169

Abstract

Access to networks and the Internet has multiplied, and data traffic is growing exponentially and quickly. High network utilization, along with varied traffic types in the network, poses a considerable challenge and impact on the ICT Infrastructure, particularly affecting the performance and responsiveness of real-time application users who will experience slowness and poor performance. Conventional/traditional Quality of Service (QoS) mechanisms, designed to ensure reliable and efficient data transmission, are increasingly insufficient due to their static nature and inability to adapt to the dynamic demands of modern networks.  As such, this study introduces a Novel Network Optimization Framework leveraging the combined strengths of Software-Defined Networking (SDN) and Deep Learning (DL) to dynamically manage multiple QoS of network devices in enterprise and campus network environments. The proposed system is a dynamic QoS that utilizes SDN's global monitoring and centralized management control capabilities to programmatically control network devices, ensuring that sensitive traffic is allocated with appropriate bandwidth and minimized latency. Concurrently, DL algorithms enhance the framework's decision-making process by proposing an accurate preferred configuration for the best adequate bandwidth for sensitive traffic transmission. This integration facilitates real-time adjustments to network conditions and improves overall network performance by ensuring high-priority applications receive the bandwidth they require without manual/human intervention. By providing a dynamic, intelligent solution to QoS management, this framework represents a significant step forward in developing more adaptable, resilient, and efficient networks capable of supporting the demands of contemporary and future digital ecosystems.
An Improved Hybrid GRU and CNN Models for News Text Classification Khudhair, Inteasar Yaseen; Majeed, Sundus Hatem; Ahmed, Ali Mohammed Saleh; Kadhim Alsaeedi, Mokhalad Abdulameer; Aswad, Firas Mohammed
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2658

Abstract

 Due to the continuous growth and advancement of technology, an enormous volume of text data is generated daily across various sources including social media platforms, websites, search engines, healthcare records, and news articles. Extracting meaningful patterns from text data, such as viewpoints, related theories, journal distribution, facts, and the development of online news text, is a challenging task due to the varying lengths of the texts. One issue arises from the length of the text data itself, and another challenge lies in extracting valuable features, especially in news articles. In the deep learning models, the convolutional neural networks (CNNs) are capable of capturing local features in text data, but unable to capture the structural information or semantic relationships between words. Consequently, a sole CNN network often yields poor performance in text classification tasks, whereas the Gated Recurrent Unit (GRU) is adept at effectively extracting semantic information and understanding the global structural relationships present in textual data. This paper presents a solution to the problem by introducing a new text classification that integrates the strengths of CNN and GRU. The proposed hybrid models incorporate word vectorization and word dispersion in parallel. Initially, the model trains word vectors using the Word2vec model and then leverages the GRU model to capture semantic information from text sentences. Subsequently, the CNN method is employed to capture crucial semantic features, leading to classification using the SoftMax layer. Experimental findings demonstrated that the proposed hybrid GRU_CNN model outperformed and achieved accuracy 97.73% as compared to individual CNN, LSTM, and GRU models in terms of classification effectiveness and accuracy.
Analyzing Course Selection by MBTI Personality Types Goo, Cui-Ling; Leow, Meng-Chew; Ong, Lee-Yeng
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2937

Abstract

This research project explores the relationship between course selection and Myers-Briggs Type Indicator (MBTI) personality types. It focuses on a private university’s IT Faculty students pursuing AI, BIA, BIO, DCN, and ST courses. In higher education, there is a limited understanding of the influence of personality types on course selection. This research aims to determine the statistically significant differences between courses with personality profiles. To achieve this, data collected from the survey is systematically analyzed to provide useful insights into the distribution of course selection among various personality types through descriptive analysis and inferential statistics tests, such as the Kruskal-Wallis Test. These assessments help examine the statistically significant difference between courses for each personality profile, supported by a p-value < 0.05. Descriptive analysis shows INFJ typically occurred in every course, showing the wide distribution of this personality type among students. Besides, the result shows INF_ types predominantly appear in median personalities across all courses among the participants. The majority of the participants have INTP personality types. The inferential statistical results show statistically significant differences in the distribution of courses for 8 MBTI personality types, while the remaining MBTI is not statistically significant. The results also show statistically significant differences between courses for each personality dimension. These results can be used to provide suggestions to students on course selection. Future research could expand this study by including a more diverse range of universities and courses and incorporating additional personality assessments.

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