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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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Machine Learning-Based Fire Detection: A Comprehensive Review and Evaluation of Classification Models Secilmis, Adildabay; Aksu, Nurullah; Dael, Fares A.; Shayea, Ibraheem; El-Saleh, Ayman A.
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.7.3-2.2332

Abstract

Fires, regardless of their origin being natural events or human-induced, provide substantial economic and environmental hazards. Therefore, the development of efficient fire detection systems is of utmost importance. This study provides a comprehensive examination of the extant body of literature about studies on fire detection utilizing machine learning techniques. Significantly, the studies employed three distinct categories of datasets: pictures, data derived from Wireless Sensor Networks (WSNs), or a hybrid amalgamation of both. Our work mainly aims to categorize fire-related data utilizing four distinct classification models: Support Vector Machines (SVMs), Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP). The model with the highest accuracy and ROC curve performance was identified through experimental analysis. The results of our study indicate that the MLP model exhibits the highest overall accuracy, achieving a score of 0.997. In this study, we analyze the learning curves to showcase the positive training dynamics of our model. Additionally, we explore the scalability of our model to ensure its suitability in real-world situations. In general, our research underscores the possibility of employing machine learning methodologies for fire detection, specifically emphasizing the effectiveness of the Multilayer Perceptron (MLP) model. This study contributes to the existing literature by offering valuable insights into the performance of several categorization models and conducting a comprehensive investigation of the Multilayer Perceptron (MLP) architecture. The results of our study have the potential to contribute to the advancement of fire detection systems, leading to enhanced accuracy and efficiency. This, in turn, may mitigate the adverse impacts of fires on both society and the environment.
Coordination of The Apprenticeship Industrial Program with The Siakama Application Yustisia, Henny; Andreas, Laras Oktavia; Apdeni, Risma; Heriyadi, Bambang; Weriza, Jusmita
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.8.1.2245

Abstract

This research aims to examine the implementation of the SIAKAMA application in the Apprenticeship Industrial Program. This program was created as a SIAKAMA application to overcome hurdles during the monitoring and evaluation stages. At the monitoring stage, supervising lecturers and field supervisors can use the SIAKAMA application to monitor all Apprenticeship Industrial program student activities in the field, resulting in a good and smooth communication and coordination system. At the evaluation stage, the supervising lecturer and field supervisors in the SIAKAMA application can conduct assessments based on student activities in the field, including daily evaluations and final assessments after the Apprenticeship Industrial Program has been finished. This study employs a quantitative descriptive technique, the Research & Development method, and the 4D development model. A sample of Apprenticeship Industrial Program students from five departments of the Faculty of Engineering, Padang State University, was used in this study. The SIAKAMA application was found to be valid with a value of 0.876, practical with a value of 78.67, and effective with a value of 81.22% after data analysis using SPSS 25. This suggests that implementing the SIAKAMA application to enhance the work competency of Apprenticeship Industrial Program students is viable. The Apprenticeship Industrial Program model represents a modification of the Three Set of Actor development model, yet it hasn't been incorporated with the Industrial Revolution 4.0. Engaging in this Program enables students to acquire 4C skills, including Creativity and Innovation, Critical Thinking and Problem Solving, Communication, and Collaboration.
Batik Image Representation using Multi Texton Co-occurrence Histogram Minarno, Agus Eko; Soesanti, Indah; Nugroho, Hanung Adi
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3095

Abstract

This paper introduces a novel approach to batik image representation using the texton-based and statistical Multi Texton Co-occurrence Histogram (MTCH). The MTCH framework is leveraged as a robust batik image descriptor, capable of encapsulating a comprehensive range of visual features, including the intricate interplay of color, texture, shape, and statistical attributes. The research extensively evaluates the effectiveness of MTCH through its application on two well-established public batik datasets, namely Batik 300 and Batik Nitik 960. These datasets serve as benchmarks for assessing the performance of MTCH in both classification and image retrieval tasks. In the classification domain, four distinct scenarios were explored, employing various classifiers: the K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB). Each classifier was rigorously tested to determine its efficacy in correctly identifying batik patterns based on the MTCH descriptors. On the other hand, the image retrieval tasks were conducted using several distance metrics, including the Euclidean distance, City Block, Bray Curtis, and Canberra, to gauge the retrieval accuracy and the robustness of the MTCH framework in matching similar batik images. The empirical results derived from this study underscore the superior performance of the MTCH descriptor across all tested scenarios. The evaluation metrics, including accuracy, precision, and recall, indicate that MTCH not only achieves high classification performance but also excels in retrieving images with high similarity to the query. These findings suggest that MTCH is a highly effective tool for batik image analysis, offering significant potential for applications in cultural heritage preservation, textile pattern recognition, and automated batik classification systems.
Conceptualizing Digital Readiness, Strategic Foresight, and Strategic Flexibility as Drivers of Digitalization and Performance of Small and Medium Enterprises Alqam, Hanin S; Razzak, Mohammad; Al-Busaidi, Adil; Al-Riyami, Said
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.8.2.2230

Abstract

The drivers of digitalization and small and medium enterprises (SMEs) performance have been primarily examined through resource-based theories. Hence, this study presents an alternative perspective based on such organizations' readiness and dynamic capabilities through a conceptual framework. A conceptual framework is developed by drawing upon the digital readiness theory (DRT) along with the dynamic capabilities view (DCV) to propose an integrated framework that posits a set of propositions linking constructs that reflect both digital readiness as well as the dynamic capabilities of an organization as possible drivers of business process digitalization (BPD) and performance. The empirical literature based on the DRT suggests that digital readiness will likely drive BPD and performance. Whereas leveraging the premise of the DCV indicates that the ability to sense opportunities and threats is reflected by strategic foresight. In contrast, the ability to seize and transform is reflected through strategic flexibility. The propositions posit that all three factors influence performance directly and through the mediating effect of BPD. The framework developed in this study may provide clues to practitioners and policymakers related to SME development regarding potential drivers of digitalization and performance. Growing scholarly publications on antecedents of digitalization and the performance of SMEs have focused primarily on resources. The current study offers an alternate perspective by integrating the two theories based on such organizations' readiness and dynamic capabilities.   
A Robust License Plate Detection System Using Smart Device Bin Mohamad Azhar, Muhammad Darwish; Goh, Kah Ong Michael; Check Yee, Law; Connie, Tee
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.8.2.2287

Abstract

The license plate recognition (LPR) system is widely employed in various applications. However, most research studies have used a fixed camera rather than a moving one. This is because the location of the vehicle plate is nearly static and easily estimated, making the use of a static camera simple for locating and detecting the scanned license plate. Images obtained with a moving camera are highly complex due to frequent background changes. Additionally, a challenge with car plates in Malaysia is their non-standardized nature. Car owners are permitted to use any font type for their license plate number, rendering existing license plate recognition systems from other countries incapable of effectively detecting license plates on Malaysian car plates. A traditional LPR system typically requires a high-quality camera and a powerful computer for costly and bulky processing. Nowadays, many smartphones come equipped with powerful processors and cameras. Android smartphones include various libraries for modifying hardware configurations such as the camera. This paper presents a robust method for detecting Malaysia's license plate number using a convolutional neural network (CNN). The CNN model from the pre-training process is imported to the Android device and tested in real-time in an on-road driving environment, resulting in an average recognition rate of 89.37%. A comprehensive Character Recognition Analysis is also presented to demonstrate the accuracy of each character. However, there is still room for improvement in recognizing the character Q.
Preliminary Development of Vircadia Virtual Reality Platform for Monitoring Water Quality Powered by Solar Panels Rante, Hestiasari; Achmad, Zacky Maulana; Suaib, Norhaida Mohd; Prasetya, Sonki; Avianto, Tiyo; Hermanu, Adhi Indra; Alfarezi, Fitratama; Wijaya, Ray Yusra
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3188

Abstract

Climate change is a global issue that significantly challenges water resources, especially in regions with limited public awareness about water conservation. It manifests through rising global temperatures, shifting weather patterns, more frequent and intense natural disasters, and instability in water availability. These problems are worsened by low public awareness and the reliance on steam power plants for water pumps. Addressing these challenges requires educational media that raises awareness about the causes and impacts of climate change. This study introduces the early development of a Virtual Reality (VR) platform utilizing Vircadia, focused on creating a 3D world and monitoring water quality with the support of solar power. Vircadia, an open-source platform, offers developers the flexibility to build and host virtual worlds on their servers, providing greater control over the environment, scalability, and customization. With Vircadia, we can rapidly implement a VR platform that integrates custom assets from Blender and personalized avatars from Ready Player Me. Vircadia can seamlessly connect to IoT platforms via weblink, allowing for real-time monitoring of water quality parameters and enabling users to interact directly with and oversee IoT devices within the VR environment. This paper discusses why we chose Ready Player Me and Blender as platforms for building 3D avatars and assets, and Vircadia as the VR Platform. Additionally, it addresses challenges encountered when using Vircadia, such as asset optimization and IoT device integration. Future research will focus on optimizing asset quality, enhancing IoT integration, and implementing carbon emissions monitoring within the VR platform.
Continuous Training of Recommendation System for Airbnb Listings Using Graph Learning Chan, Yun Hong; Ng, Kok Why; Haw, Su Cheng; Palanichamy, Naveen
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.8.1.2315

Abstract

Recommender systems are getting increasingly important nowadays as they can boost user engagement and benefit businesses. However, there remain some unsolved problems. This paper will address two key performance issues. First, the limited ability to identify and leverage intrinsic relationships between data points. Second, the inability to adapt to new data. The first issue is proposed to be addressed through a Graph Neural Network (GNN) to curate better recommendations. GNN will be trained with Airbnb’s review data to utilize its outstanding expressive power to represent complex user-listing interactions at scale, followed by generating embeddings to compute the relevant recommendations to the users. With the generated embeddings, the recommender system will compute a recommendation list to every user based on the embedding similarity between the user and listings or the user’s first-ever reviewed listing and listings. The second issue is proposed to be resolved by incorporating Continuous Training. The proposed recommender system employs GraphSAGE with a customized Rating-Weighted Triplet Ranking Loss function, which outperformed unsupervised GraphSAGE. Offline simulation validated the recommender system's ability to learn from the latest data and improve over time. Overall, the proposed user-to-item (U2I) recommendation rating-weighted GraphSAGE substantially increased by 99.88% in hit-rate@5 and 98.15% in coverage. This offers an effective solution for enhancing the recommender system for Airbnb listings. This research validates the efficacy of GNN-based recommendations in capturing user-item relationships to aid in predicting relevant recommendations, thus significantly driving up the adoption of GNN-based recommender systems.
Skin Lesion Classification: A Deep Learning Approach with Local Interpretable Model-Agnostic Explanations (LIME) for Explainable Artificial Intelligence (XAI) Hong, Sin Yi; Lin, Lih Poh
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3022

Abstract

The classification of skin cancer is crucial as the chance of survival increases significantly with timely and accurate treatment. Convolution Neural Networks (CNNs) have proven effective in classifying skin cancer. However, CNN models are often regarded as "black boxes”, due to the lack of transparency in the decision-making. Therefore, explainable artificial intelligence (XAI) has emerged as a tool for understanding AI decisions. This study employed a CNN model, VGG16, to classify five skin lesion classes. The hyperparameters were adjusted to optimize its classification performance. The best hyperparameter settings were 50 epochs, a 0.1 dropout rate, and the Adam optimizer with a 0.001 learning rate. The VGG16 model demonstrated satisfactory classification performance. The Local Interpretable Model-Agnostic Explanations (LIME) method was implemented as the XAI tool to justify the predictions made by VGG16. The LIME explanation revealed that the correct predictions made by VGG16 were owing to its truthful extraction of the cancer or lesion area, especially for the “vascular lesion” class. Meanwhile, inaccurate classifications were attributed to VGG16 extraction of the background and insignificant parts of the skin as core features. In conclusion, The LIME model allowed visual inspection of the features selected by VGG16, paving the way for improving the CNN model for better feature extraction and classification of skin lesions, offering a promising direction for future research. 
Personalized Learning Models Using Decision Tree and Random Forest Algorithms in Telecommunication Company Wiratman, Alexander Bryan; Wella, Wella
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.8.1.1905

Abstract

In response to the rising popularity of online training, this study addresses the crucial need for effective assessment methods at PT XYZ. The research focuses on developing a comprehensive solution through a data visualization dashboard and a machine learning model. The data visualization dashboard, created using Tableau, provides an interactive platform for exploring training data. It offers valuable insights into employees learning progress and needs, empowering them to monitor their advancement and identify areas for improvement effectively. Simultaneously, a machine learning model was developed using Python and Google Collab, employing decision trees and random forest algorithms. The model exhibited promising results with an accuracy rate of 69% for decision trees and 70% for random forests, indicating its proficiency in predicting skill groups. Furthermore, the study rigorously evaluated the dashboard and machine learning model using a 20% holdout dataset, affirming their effectiveness. The dashboard, deployed on a web server, ensures accessibility to all PT XYZ employees, enhancing user experience and engagement. Notably, the dashboard's user-friendly interface allows employees to actively participate in their learning journey, while the machine learning model generates personalized training recommendations based on their progress and needs. In summary, this research provides a practical and innovative solution to the challenge of online training assessment at PT XYZ. By combining data visualization techniques and machine learning algorithms, the developed tools significantly enhance the efficiency and effectiveness of training programs. These findings contribute valuable insights into online training assessment methodologies and pave the way for improved learning experiences in the digital age.
Comparative Study of CNN Architectures for Real-Time Audio-Based Car Accident Detection on Edge Devices Ilahi, Ahmada Haiz Zakiyil; Irwansyah, Arif; Oktavianto, Hary
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2985

Abstract

Traffic accidents often result in fatalities for both drivers and bystanders. Traditionally, accident information relies heavily on community reports, which can delay the provision of victim assistance. To address this issue, a system capable of detecting accidents responsively in various weather conditions and traffic densities is necessary. One approach involved using audio analysis techniques to evaluate collision sounds. Thus, this study proposed an audio classification system for detecting car accidents using Convolutional Neural Networks (CNNs). The system’s performance was evaluated on personal computers and edge devices, such as the Raspberry Pi 4 and NVIDIA Jetson Nano, to compare inference times and power consumption. To enhance the dataset, segmentation and augmentation techniques were applied before converting the audio data into a 2D Mel-spectrogram. The dataset was then trained and assessed with four CNN architectures: custom sequential, custom with shared input layer, transfer learning EfficientNetB0, and transfer learning MobileNetV2. Both original and Lite models were deployed on experimental devices. Results showed that the custom CNN model had faster inference times across devices in both original and lite forms, though it had a 4% increase in the false positive rate. The Lite MobileNetV2 model recorded the fastest inference time on edge devices at 86 ms. Jetson Nano exhibited faster inference times compared to Raspberry Pi 4. However, Raspberry Pi 4 showed a minor increase in power consumption of 0.6 watts during inference. In future work, this system can be tested in real-time environments using embedded systems to evaluate its robustness against noise and varying environmental conditions.

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