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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 48 Documents
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Search results for , issue "Vol 9, No 2 (2025)" : 48 Documents clear
Indonesian Word Sound Recognition Using Convolutional Neural Network Method Kusuma, Mandahadi; Aunilbarr, Fayyadh
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2679

Abstract

Access to education, particularly in a university environment, is essential for deaf and hard-of-hearing students as more of them pursue higher education. At UIN Sunan Kalijaga the current challenges are a limited number of sign language interpreters and translating technical terminology in lectures. Many methods are available for speech recognition, but research on how well this method performs in Indonesian has not been published, especially in education-level recognizers. This experimental study aims to investigate if Indonesian words can be recognized through Convolutional Neural Networks (CNN) and to find out the Data Ratio for Training, Validation, and Testing set to get the best performance. The study used a dataset of 4 Indonesian words with the total voice sample, each with 50 voice samples from young adults aged 19-23. Audio data is preprocessed into spectrograms, inputs to the CNN model using TensorFlow. The CNN Model had a 90% accuracy with a 60:20:20 ratio between training, validation, and test data. The other ratios (70:15:15 and 80:10:10) provided accuracy ranges of between 80% to 90%. This study shows that CNNs are the best for Indonesian word recognition and that the data ratio of 60:20:20 is optimal. This result has valuable benefits, such as using voice-to-text over lectures to enhance the ease of learning and education in Indonesia. Further studies should be conducted using different neural network approaches; the denoise approach is also necessary to increase accuracy.
Context-Aware Job Recommender System Azri, Muhammad Haziq Fikri Bin; Haw, Su-Cheng; Ng, Kok-Why; Saad, Mohamad Firdaus Mat
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3021

Abstract

Context-aware recommendation systems have emerged as essential to interactive web content and online job search. Primarily, since so many job offers are published on different online platforms, it can make the users take some time to find good opportunities that match exactly what they are looking for, as well as countless qualified candidates and other characteristics within that context, such as temporality. This comes as no surprise, as many practitioners and researchers have resorted to machine learning to create context-aware job recommendation systems that cater not only to job seekers. In this comparative paper, we have analyzed various machine-learning models for job recommendation systems. Four fundamental pillars are considered: accuracy, scalability, interpretability, and computational efficiency. This paper also studies the extent to which these models are contextual (e.g., how well they can model factors due to user preferences, job requirements, location, industry evolution, and temporality.) and can be used as a recommendation system. This study uses real-world employment data from actual employment statistics (through fixed-period analysis), professional networking platforms, and online job market platforms. The study does so purposefully to be comprehensive because it believes the lessons from remote work are generalizable. Still, the data is from a wide variety of job sectors, job positions, and locations. The group created a test environment for constructing and testing machine learning algorithms. Collaborative filtering, content-based, matrix factorization, deep learning, and many other hybrid approaches have obtained better results. This study was performed on Python with sci-kit-learn, pandas, and NumPy. The proposed system is a context-aware job recommender system that employs many machine learning algorithms to personalize job recommendations concerning user preferences and contextual information such as job location, industry status, and temporal dynamics. The findings underscore the importance of choosing machine learning models that are well-suited for job recommendation systems on a case-by-case basis. This comparative study intends to add to the art by providing algorithmic proof and practical advice to properly leverage machine learning models proposed in a naturalistic, messy setting of context-aware job recommendation systems. 
Deep Learning Algorithms and Optimizers: Enhancing the Evaluation of Signature Authenticity Haris Rangkuti, Abdul; Tanuar, Evawaty; Kusuma, Verdiant Jonathan; Athala, Varyl Hasbi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2625

Abstract

Given the rapid technological advancements, security has become an essential human need that must be addressed. For example, a signature, which serves as a unique identifier or mark on a document, is vital in verifying and legalizing its contents. This study aims to utilize image processing techniques to identify patterns in signature images. Generally, a signature is a handwritten depiction used to authorize a document, indicating that the signing party acknowledges and agrees to its contents. However, this practice exposes signatures to the risk of forgery by dishonest individuals. Therefore, it is crucial to implement a security system for identity recognition using a biometric system for verification and identification. Verification involves determining whether the signature belongs to a previously identified individual and assessing its authenticity. This study employs deep learning algorithms, enhanced by optimizer methods, to improve accuracy performance in signature recognition for authenticity. Additionally, classification methods such as Linear SVM and RbfSVM are utilized. Several experiments were conducted, with VGG16 paired with the Adam optimizer yielding the highest accuracy of 0.9923. This was closely followed by VGG19 with Adagrad and Xception with RMSprop, achieving an accuracy of 0.9915. The training and validation accuracy processes revealed that the CNN VGG19 and VGG16 models with the Adam optimizer consistently achieved an accuracy exceeding 99%. Based on these experimental findings, the accuracy for detecting genuine and fake signatures can be clearly distinguished with an accuracy of over 99%, demonstrating the validity of this approach.
DeepForgery Images Detection Using Deep Learning Approaches and Error Level Analysis Nazrin, S.N.; Burhanuddin, Liyana Adilla binti; Jothi, Neesha; Zaman, Halimah Badioze; Rosnan, Muhammad Fahmi Bin
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3504

Abstract

The increasing of manipulated images, often shared on social media platforms, poses significant challenges for distinguishing authentic content from forgeries. This study aims to enhance the detection of tampered images by integrating Error Level Analysis (ELA) with Convolutional Neural Networks (CNNs). Specifically, the objectives are to evaluate the performance of two CNN architectures, VGG16 and MesoNet, combined with ELA preprocessing, and to identify potential avenues for future improvements in forgery detection. The dataset used comprises 7,492 authentic and 5,124 tampered images, sourced from the CASIA database, and is complemented with images from the Milborrow University of Cape Town (MUCT) dataset. Images were preprocessed using ELA to amplify discrepancies caused by tampering before being analyzed by the CNN models. The results indicate that the proposed ELA-VGG16 model achieved an accuracy of 86.786%, while the ELA-MesoNet model demonstrated superior performance, with an accuracy of 92.7%. These findings highlight the potential of combining ELA preprocessing with CNN architectures for robust image forgery detection. Despite fluctuations in training curves and instances of overfitting, the model effectively detects subtle manipulations in the majority of cases. However, challenges such as false positives and generalization to diverse datasets persist. Future research should explore enhancements such as expanded data augmentation, the integration of multi-model architectures,such as Xception or capsule networks, and advanced preprocessing techniques, which could further refine the model’s applicability and accuracy. These efforts would advance both the practical detection of forgeries and theoretical developments in informatics visualization, addressing critical challenges in digital forensics and media integrity.
Predicting the Next Day's Closing Price of Stock Indices Using Machine Learning and Deep Learning Algorithms Cayzer, Ahmad Firdaus; Bau, Yoon-Teck
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3501

Abstract

Share prices are a critical factor in a stock index’s worth but are never constant. Thus, an effective method of predicting share prices is needed. This is where machine learning comes in. This research discusses the applicability of machine learning algorithms, precisely long short-term memory, artificial neural networks, and linear regression in predicting share prices. Additionally, this research goes in-depth, explaining how each algorithm functions. These three algorithms were implemented using the financial dataset of the S&P 500, one of the more known stock indices out there. Data was collected from Yahoo Finance for 34 years, from 1990 to 2023. Then, the algorithms mentioned were used to train a model using the collected dataset. All three algorithms were measured using three performance metrics: mean absolute error, R-squared score, and mean absolute percentage error. The final implementation involved training them by only using 1-day lagged features to create a model that can predict the next day's closing price. All the algorithms performed considerably well, with linear regression being the best, followed by artificial neural networks and long short-term memory being the worst. Finally, the implemented algorithms were used to predict the closing prices of other stock indices, NASDAQ and Hang Seng Index. All algorithms performed well and followed the same trend, wherein linear regression performed the best and long- and short-term memory the worst. Future research should be conducted to explore the possibilities of utilizing lagged features along with external features like GDP growth rate, political trends, etc.
Object Detection with YOLOv8 and Enhanced Distance Estimation Using OpenCV for Visually Impaired Accessibility Syahrudin, Erwin; Utami, Ema; Hartanto, Anggit Dwi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2826

Abstract

Accessibility challenges for the visually impaired are getting more serious yearly. To address this issue, this study presents an advanced object detection system that utilizes YOLOv8, enhanced with OpenCV for distance estimation. The methodology involves data preparation with diverse scenarios to test system accuracy, including environments like busy streets and indoor settings. Precision, recall, and F1-score metrics evaluate performance under varying lighting conditions. Results show a decrease in performance during low-light conditions, emphasizing the need for adequate lighting for effective detection. The system also includes a real-time implementation with a panic button feature, allowing immediate activation of object detection and distance estimation processes. The results are translated into Indonesian using a translation service and converted to speech, making the information accessible to users. By integrating YOLOv8 and OpenCV, the research achieves an average object detection accuracy of 91% with a low error rate of about 3.6%. Rigorous testing and evaluation under various conditions ensure reliability and effectiveness. The implications of this research extend to real-time applications like navigation assistance for the visually impaired, highlighting the potential for improved quality of life and independence. Future work will focus on optimizing detection in low-light conditions, incorporating additional sensors like infrared cameras, and enhancing real-time text translation services for accurate information delivery to visually impaired users. Additionally, continuous training with diverse datasets will be conducted further to improve the robustness and accuracy of the detection system.
Student's Attitudes and Motivation Towards the Effectiveness of Open Distance Learning (ODL) in Malaysian Universities Law, Kyra Ley Sy; Nesamalar Tharumaraj, Judith; Si, Josh Chong En
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3404

Abstract

Online distance learning (ODL) has transformed the educational environment and ensured educational continuity during global crises. As ODL becomes a permanent mode of education, continuous research into its effectiveness is imperative. This study investigates the ARCS model as a mediator between student attitudes, learning platforms, and ODL effectiveness, focusing on student experiences and outcomes. Utilizing a quantitative approach, an online survey comprising 31 items across five domains, including the ARCS model, was administered to 123 participants currently or previously engaged in ODL. The findings reveal that ODL effectiveness is significantly enhanced by positive motivational factors supported by psychological and emotional attitudes. Contrary to initial assumptions, platform availability, and accessibility do not independently influence ODL effectiveness; instead, motivation positively mediates effectiveness. This study provides institutions with the flexibility to improve learning platforms and offers insights to boost student motivation. Additionally, the study underscores the importance of fostering supportive attitudes to maximize ODL benefits. Recommendations for future research include exploring other mediating factors that may impact ODL effectiveness and examining diverse student populations to generalize the findings further. By addressing these areas, educational institutions can better understand the dynamics of ODL and implement strategies to enhance student experiences and outcomes. This study contributes to the growing knowledge of ODL, highlighting critical areas for institutional improvement and student support. It emphasizes the need for a holistic approach to educational technology, where motivational and attitudinal factors are integral to achieving effective and impactful online learning.
Agent-Oriented Modelling and Simulation for Robotic Based Predator Control Andy, Yee Wee Chieh; Shiang, Cheah Wai; Paschal, Celine Haren
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3347

Abstract

Agent-oriented modeling (AOM) is a methodology that can develop complex tasks that involve multi-agent system development, such as robotic systems. There are still insufficient studies on how Agent-Oriented Modelling benefits robotic development. There is little reference to using Agent-Oriented Modelling to develop complex systems, especially robotic applications. This study aims to investigate the adoption of AOM for robotic surveillance modeling and simulation for predator control in the farming sector and to conduct qualitative comparisons on robotic models and simulation methods. A case study of robotic-based predator control is used to develop the system using the AOM model. Later, this is incorporated into a Netlogo simulation to illustrate the suggested methodology in the model simulation stage. A qualitative analysis of the model is performed to validate the model.  SUS analysis for AOM usability at the score of 68.35 shows AOM is at the average usability level for beginner users in software development. Qualitative analysis shows that beginner users prefer to use AOM for complex adaptive and distributed robotic systems. AOM is introduced to create robotic-based predator control in a structured manner to prove that AOM can be used to develop complex systems. Introducing Agent-Oriented Modelling in various domains leads to higher confidence in the industry player's adoption of this model across multiple system developments.
Recent Advances on Meta-heuristic Algorithms for Training Multilayer Perceptron Neural Network Al-Asaady, Maher Talal; Aris, Teh Noranis Mohd; Sharef, Nurfadhlina Mohd; Hamdan, Hazlina
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3109

Abstract

Artificial Neural Networks (ANNs) have demonstrated applicability and effectiveness in several domains, including classification tasks. Significant emphasis has been given to the training techniques of ANNs in identifying appropriate weights and biases. Conventional training techniques such as Gradient Descent (GD) and Backpropagation (BP), while thorough, have several disadvantages such as early convergence, being highly dependent on the initial parameters, and quickly getting stuck in local optima. Conversely, meta-heuristic algorithms show great potential as effective approaches for training ANNs with high computational efficiency, high quality, and global search capabilities. The literature has proposed several such techniques; hence, this paper offers a thorough examination of current advancements in training a Multilayer Perceptron (MLP) neural network using meta-heuristic algorithms, with a focus on classification benchmark datasets. The study was conducted over a period of ten years, from the year 2014 to 2024. The research papers were specifically chosen from four widely used databases: ScienceDirect, Scopus, Springer, and IEEE Xplore. Through the use of a research methodology that incorporates specific criteria for including and excluding articles, and by thorough examination of more than 53 publications, we present a comprehensive study of meta-heuristic methods for training MLPs. Our main focus is on discovering trends across these tools. The analysis has been conducted utilizing relevant factors such as evaluation metrics for classification models, fitness functions, comparing approaches, datasets, and observed outcomes. The present work serves as a significant asset for researchers, facilitating the identification of suitable optimization methodologies for various application areas. 
Mapping The Relationship Between Virtual Reality and Bullying Prevention: An Analysis Of Bibliographic Coupling Maryaeni, Maryaeni; Nastiti, Vinna Rahmayanti Setyaning; Oktaviani, Chintya Tria Diana; Reikisyifa, Clarissa Sanindita; Kenanga, Larynt Sawfa; Kusuma, Wahyu Andhyka; Wahyuni, Evi Dwi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2183

Abstract

Bullying prevention has become a significant topic in contemporary society. The bibliographic analysis method employed is bibliographic coupling, which allows for identifying relationships among relevant scientific articles to understand the role of technology in combating bullying. The research methodology involves identifying previous publications on virtual reality and bullying prevention from Scopus. Bibliographic analysis quantifies relationships between publications based on shared references. The bibliographic data collected focuses on VR-related literature and bullying prevention from various academic sources. This article discusses the interconnections among the reviewed articles regarding the impact of these findings, such as the development of VR applications that can enhance social skills and empathy, which are crucial factors in preventing bullying. Research results indicate a significant correlation between virtual reality and bullying prevention. Relevant prior studies include topics such as using virtual reality to avoid bullying through bystanders and victims, as well as simulating bullying to enhance bystander empathy. These studies provide information on the role of virtual reality technology in effectively combating bullying. Research findings are presented as descriptive and visual analyses using VOS viewer software. Additionally, the article underscores the importance of interdisciplinary collaboration in integrating VR into effective bullying prevention strategies, making us all part of a larger community working towards a common goal. Hopefully, this article will provide a foundation for future research and the development of technology applications with the potential to combat bullying.

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