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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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Technology and Language: Improving Speaking Skills through Cybergogy-Based Learning Satria, Dadi; Zamzani, Zamzani; Nurhadi, Nurhadi; Arief, Ermawati
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.8.2.2215

Abstract

Language learning in the Industrial Revolution 4.0 and Society 5.0 era is required to produce students with 21st-century skills by increasing the capacity and capability of using technology in learning. Technology-based learning, known as cybergogy, is a continuity of learning paradigms that previously applied the principles of pedagogy and andragogy in the learning process. As a new concept in learning, cybergogy is essential in improving 21st-century skills in the form of the 6Cs (Citizenship, Character, Critical Thinking and Problem Solving, Communication, Creativity, and Collaboration). Enhancing communication skills through cybergogy-based learning is a novelty that has not been done much and has become the focal point of research. This research, part of development research using the ADDIE model, employed a quasi-experimental design conducted in 3 senior high schools in Yogyakarta, representing one school per category, namely the lower, medium, and high categories, based on UTBK scores. A non-equivalent control group design involving an experimental and control class was applied. The results of this study, which showed a significant improvement in students' communication skills, especially speaking aspects, through blended learning, are of great significance. Therefore, it can be concluded that cybergogy-based language learning has proven effective in improving students' communication skills through blended learning.
Offline Handwriting Writer Identification using Depth-wise Separable Convolution with Siamese Network Suteddy, Wirmanto; Agustini, Devi Aprianti Rimadhani; Atmanto, Dastin Aryo
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.8.1.2148

Abstract

Offline handwriting writer identification has significant implications for forensic investigations and biometric authentication. Handwriting, as a distinctive biometric trait, provides insights into individual identity. Despite advancements in handcrafted algorithms and deep learning techniques, the persistent challenges related to intra-variability and inter-writer similarity continue to drive research efforts. In this study, we build on well-separated convolution architectures like the Xception architecture, which has proven to be robust in our previous research comparing various deep learning architectures such as MobileNet, EfficientNet, ResNet50, and VGG16, where Xception demonstrated minimal training-validation disparities for writer identification. Expanding on this, we use a model based on similarity or dissimilarity approaches to identify offline writers' handwriting, known as the Siamese Network, that incorporates the Xception architecture. Similarity or dissimilarity measurements are based on the Manhattan or L1 distance between representation vectors of each input pair. We train publicly available IAM and CVL datasets; our approach achieves accuracy rates of 99.81% for IAM and 99.88% for CVL. The model was evaluated using evaluation metrics, which revealed only two error predictions in the IAM dataset, resulting in 99.75% accuracy, and five error predictions for CVL, resulting in 99.57% accuracy. These findings modestly surpass existing achievements, highlighting the potential inherent in our methodology to enhance writer identification accuracy. This study underscores the effectiveness of integrating the Siamese Network with depth-wise separable convolution, emphasizing the practical implications for supporting writer identification in real-world applications.
A New Approach of Steganography on Image Metadata Fernando, Yusra; Darwis, Dedi; Mehta, Abhishek R; Wamiliana, Wamiliana; Wantoro, Agus
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.8.2.2110

Abstract

In this paper, we introduce a novel method, Steganography on Image Metadata (SIM), to tackle the problem of robustness modification in steganography.  The SIM method works by embedding messages into the metadata storage space of digital media. Metadata is information embedded in a file that explains the file's content. The advantage of this method is that it does not alter the pixel values in the image, ensuring no degradation in media quality, and the secret message remains secure even when robustness manipulations are applied to the stego-image. To enhance data security, this paper also suggests using Fernet cryptography for message encryption during the embedding process into the cover-image. According to experimental evaluations, the SIM technique can attain a maximum PSNR value of 100 dB and an outstanding MSE value of 0. All robustness manipulation issues in steganography can be effectively addressed using the SIM method. Test results demonstrate that the SIM method can withstand symmetric and asymmetric cropping manipulations down to a pixel size of 1x1, and the message can still be extracted. Testing with image rotation manipulation also proves that the message can be successfully extracted even when the stego-image is rotated up to 180 degrees. Experiments with image resizing manipulation also confirm that the message can be recovered even when the stego-image undergoes up to 90% compression. Testing with color effects applied to the image also does not affect message extraction results.
Curriculum Management Systems for Blended Learning Support Asrini, Hari Windu; Wicaksono, Galih Wasis; Budiono, Budiono
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.7.4.1306

Abstract

The ongoing COVID-19 has left some serious impacts on education, bringing academic activities to a halt, and restrictions have been in place to hamper the proliferation of the virus. The utilization of technology these days plays a vital role in assisting students in attending education online or blended learning and keeping academic activities running. However, limited learning management systems present a new problem in online learning, especially in higher education. Thus, a new information system is required to resolve this issue. This study aims to develop the Bauran information system to assist lecturers in higher education with curriculum design and semester lesson plans and to evaluate the effectiveness of Bauran's implementation using the ISO 25010 model. The material used during the research process included the Bauran application, Guidelines for Developing Higher Education Curriculum, and some data from relevant users to test the application. Meanwhile, during the development stage of the research, the prototype method was used to adjust the development to the feedback given by stakeholders. This application received positive feedback from relevant users regarding the curriculum development flow in line with the Guidelines of Curriculum Drafting for Higher Education. Using the ISO 25010 model during the testing process, the results of user evaluations demonstrated its effectiveness with an average score of 4.84 out of 5. Future research is expected to evaluate the long-term effectiveness of Bauran using a larger sample size and a different software evaluation model.
Practical Evaluation of Federated Learning in Edge AI for IoT Pal, Sauryadeep; Umair, Muhammad; Tan, Wooi-Haw; Foo, Yee-Loo
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.7.3-2.2329

Abstract

AI running locally on IoT Edge devices is called Edge AI. Federated Learning (FL) is a Machine Learning (ML) technique that builds upon the concept of distributed computing and preserves data privacy while still supporting trainable AI models. This paper evaluates the FL regarding practical CPU usage and training time. Additionally, the paper presents how biased IoT Edge clients affect the performance of an AI model. Existing literature on the performance of FL indicates that it is sensitive to imbalanced data distributions and does not easily converge in the presence of heterogeneous data. Furthermore, model training uses significant on-device resources, and low-power IoT devices cannot train complex ML models. This paper investigates optimal training parameters to make FL more performant and researches the use of model compression to make FL more accessible to IoT Edge devices. First, a flexible test environment is created that can emulate clients with biased data samples. Each compressed version of the ML model is used for FL. Evaluation is done regarding resources used and the overall ML model performance. Our current study shows an accuracy improvement of 1.16% from modifying training parameters, but a balance is needed to prevent overfitting. Model compression can reduce resource usage by 5.42% but tends to accelerate overfitting and increase model loss by 9.35%.
Designing an Information Technology Platform for Imparting Entrepreneurship Values in Social-Emotional Learning for Kindergarten Children Using EFA and CFA Muji, Anggarda Paramita; Bentri, Alwen; Jamaris, Jamaris; Rakimahwati, Rakimahwati; Hidayati, Abna
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.8.2.2536

Abstract

Kindergarten is crucial for today's education. Kindergarten helps kids develop in all areas. Students work in groups on a simple project task in this study. The work includes topic-related activities. Kids should learn early on that being an entrepreneur will shape their identity & future. This condition makes someone realize that their long-term desire to be an entrepreneur comes from their academic success. This study investigated how the 35 questions are assembled & what improves them. 195 kindergarten teachers participated in the study. The sample was analyzed using EFA & CFA. Exploratory factor analysis revealed twelve unknown variables. The above variables explained 80% of the variation. Various factors explained the remaining 20%. All Cronbach's alpha values exceed requirements. CR >.7 & AVE >.5, indicating credible & tested constructs. The EFA showed that 195 research samples were sufficient because the KMO was above 0.50. This allowed more research. Six-factor solutions explained over 80.71% of the variation, so the EFA liked them. These factors kept the results consistent with those of previous studies. These traits facilitate legislator-educator dialogue rather than kindergarten teacher business observation. Researchers can use these properties for cluster analysis or multivariate linear regression. This subject requires more research because students develop a structured approach. Furthermore, experts should examine the research on what is making kindergarten entrepreneurship instruction popular.
Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices Nguyen, Hoang Phuong; Nguyen, Cao Thao Uyen; Tran, Thi Men; Dang, Quoc Hai; Pham, Nguyen Dang Khoa
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.8.1.2581

Abstract

This paper aims to investigate the role that artificial intelligence (AI) plays in promoting sustainability in the marine industry. The report demonstrates the potential of AI-driven technology to improve vessel operations, decrease emissions, and promote environmental stewardship. This potential is shown by detailed examination of existing trends, problems, and possibilities. Several vital studies highlight the significance of policy interventions that encourage the use of artificial intelligence. These interventions include financial incentives, legal frameworks, and programs to increase capability. Throughout this work, the importance of the role that artificial intelligence plays in driving efficiency, safety, and sustainability is emphasized. This work also highlights the urgent need for action to address climate change and environmental degradation in the marine sector. The marine industry can lessen its carbon footprint, decrease pollution, and improve ecosystem health if it shifts to various alternative fuels, renewable energy sources, and technologies powered by artificial intelligence. At the end of this work, an appeal is made to policymakers, industry stakeholders, and technology providers, urging them to prioritize investments in artificial intelligence research and development and to create collaboration to speed up the transition to a marine sector that is more sustainable and resilient.
Machine Learning-Driven Stroke Prediction Using Independent Dataset Zahari, Fatin Natasha Binti; Ramakrishnan, Kannan
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.8.2.2689

Abstract

The incidence of stroke cases has witnessed a rapid global rise, affecting not only the elderly but also individuals across all age groups. Accurate prediction of stroke occurrence demands the utilization of extensive data pre-processing techniques. Moreover, the automation of early stroke forecasting is crucial to prevent its onset at the initial stage. In this study, stroke prediction models are evaluated to estimate the likelihood of stroke based on various symptoms such as age, gender, pre-existing medical conditions, and social variables. The machine learning techniques employed include Linear Support Vector Classifier, Extreme Gradient Boosting Classifier, Multilayer Perceptron, Adaptive Boosting Classifier, Bootstrap Aggregating Classifier, and Light Gradient-Boosting Machine. The purpose of this paper is to optimize the hyperparameters of machine learning approaches in developing stroke prediction models. The goal was achieved through a comprehensive comparison of three different sampling techniques for handling imbalanced datasets and evaluating their performance by using various metrics. The most effective model is identified, which is the Adaptive Boosting Classifier utilizing the Tomek Links, with a cross-dataset accuracy of 99% which demonstrated a reliable performance and generalization as evidenced by high cross-validation scores and accuracy on an independent dataset. The next stage of this endeavor entails looking into multiple ways to forecast the development of new dangerous diseases such as breast cancer and skin disorders. In the long run, the aim of subsequent work is to build a powerful toolset that is obtainable to all medical practitioners, allowing for the pre-emptive diagnosis of all potentially hazardous illnesses.
Batik Classification using Microstructure Co-occurrence Histogram Minarno, Agus Eko; Soesanti, Indah; Nugroho, Hanung Adi
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.8.1.2152

Abstract

Batik Nitik is a distinctive form of batik originating from the culturally rich region of Yogyakarta, Indonesia. What sets it apart from other batik styles is its remarkable motif similarity, a characteristic that often poses a considerable challenge when attempting to distinguish one design from another. To address this challenge, extensive research has been conducted with the primary objective of classifying Batik Nitik, and this research leverages an innovative approach combining the microstructure histogram and gray level co-occurrence matrix (GLCM) techniques, collectively referred to as the Microstructure Co-occurrence Histogram (MCH).The MCH method offers a multi-faceted approach to feature extraction, simultaneously capturing color, texture, and shape attributes, thereby generating a set of local features that faithfully represent the intricate details found in Batik Nitik imagery. In parallel, the GLCM method excels at extracting robust texture features by employing statistical measures to portray the subtle nuances within these batik patterns. Nevertheless, the mere fusion of microstructure and GLCM features doesn't inherently guarantee superior classification performance. This research paper has meticulously examined many feature fusion scenarios between microstructure and GLCM to pinpoint the optimal configuration that would yield the most accurate results. The dataset used consists of 960 Batik Nitik samples, comprising 60 categories. The classifiers employed in this study are K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Linear Discriminant Analysis (LDA). Based on the experimental results, the fusion of microstructure and GLCM features with the (LDA) classifier yields the best performance compared to other scenarios and classifiers.
Implementation of Ensemble Machine Learning Classifier and Synthetic Minority Oversampling Technique for Sentiment Analysis of Sustainable Development Goals in Indonesia Gufroni, Acep Irham; Hoeronis, Irani; Fajar, Nur; Rachman, Andi Nur; Sidik Ramdani, Cecep Muhamad; Sulastri, Heni
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.8.2.1949

Abstract

As part of the Sustainable Development Goals (SDGs), governments worldwide have committed to improving people's lives to improve the quality of life for all, including the 17 such goals that were agreed upon in 2015 to benefit the human race as a whole. It would be interesting to see how society responds to the SDGs after approximately half of them have been achieved. This public response was analyzed in terms of sentiment. Within the total number of internet users in Indonesia, there are 18.45 million Twitter users. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. To model the data collected, the researchers used Ensemble Machine Learning Classifiers (EMLC) to model the data by using a machine learning classifier that uses machine learning techniques. The best model in this study is EMLC-Stacking with a data splitting of 80:20 and using SMOTE, which obtains an accuracy of 91%. This accuracy results from a 5% increase compared to when not using SMOTE. From 15,698 tweets, this research found that 47% were positive sentiments, 28% were negative sentiments, and 25% were neutral sentiments. The results that we measured offer hope that there will be a positive trend in the journey of the SDGs until 2030 if these findings are true.

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