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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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Evaluation of the Performance of Kernel Non-parametric Regression and Ordinary Least Squares Regression Sadek, Amjed Mohammed; Mohammed, Lekaa Ali
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2430

Abstract

Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson kernel estimator method with the proposed kernel function (AMS), the Gaussian kernel, and the ordinary least squares (OLS) method. Additionally, it determines which method yields the most accurate results when analyzing nonparametric regression models and provides valuable insights for practitioners looking to apply these techniques in real-world scenarios. However, criteria such as generalized cross-validation (GCV), mean square error (MSE), and coefficient determination are used to select the most efficient estimated model. Simulated data was used to evaluate the performance and efficiency of estimators using different sample sizes. The results favorable the simulation illustrate that the Nadaraya-Watson kernel estimator using the proposed kernel function (AMS) exhibited favorable and superior performance compared to other methods. The coefficients of determination indicate that the highest values attained were 98%, 99%, and 99%. The proposed function (AMS) yielded the lowest MSE and GCV values across all samples. Therefore, this suggests that the model can generate precise predictions and enhance the performance of the focused data.
Real-Time Digital Assistance for Exercise: Exercise Tracking System with MediaPipe Angle Directive Rules Sim, Kok Swee; wong, Shun Wei; Low, Alex; Yunus, Andi Prademon; Lim, Chee Peng
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.8.4.2993

Abstract

This paper focuses on developing an exercise tracking system capable of recognizing simple exercises, such as push-ups, pull-ups, and sit-ups, with high accuracy, leveraging human pose estimation techniques to enhance prediction performance. Exercise tracking can help users to perform workouts correctly and improve overall physical and mental health. The system utilizes the HSiPu2 dataset for training and evaluation, employing MediaPipe as the human pose estimation input and a Multi-Layer Perceptron (MLP) model for exercise recognition. Initially, a baseline MLP with three layers was implemented, followed by an improved expand-shrink MLP architecture designed to enhance model performance. The results demonstrate that the expand-shrink MLP model has achieved a 16% higher accuracy than the baseline, showcasing its effectiveness in accurately recognizing simple exercises based on pose estimation data. This advancement highlights the potential of the model to support a broader range of exercise types, offering a robust solution for monitoring workouts. The system provides meaningful feedback to users by ensuring accurate exercise recognition and promoting safe and effective physical activity. Future research can explore integrating this system with real-time feedback mechanisms, enabling users to receive immediate corrections during workouts. Expanding the dataset to include diverse exercise routines, including complex and dynamic movements, could enhance the system’s applicability. These developments would pave the way for more comprehensive and practical exercise-tracking solutions, supporting individuals to maintain a healthy lifestyle and improving the accessibility of fitness technologies.
Optimizing Quadrotor Stability: RBF Neural Network Control with Performance Bound for Center of Gravity Uncertainty Yani, Mohamad; Ardilla, Fernando; Anom Besari, Adnan Rahmat; Saputra, Azhar Aulia; Kubota, Naoyuki; Ismail, Zool H
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.9.3.2918

Abstract

The Radial Basis Function (RBF) neural network has been widely applied for approximating nonlinear systems and improving control robustness, particularly in uncertain conditions such as dynamic shifts in the quadrotor’s Center of Gravity (COG). However, initial weight estimation errors can degrade transient responses, reducing tracking performance. This study proposes a novel RBF-based control scheme integrated with a performance-bound mechanism to enhance quadrotor stability under COG uncertainty. The performance bound ensures that the quadrotor’s motion remains within a defined region around the reference trajectory, thereby minimizing steady-state and transient errors. The RBF network is trained online to estimate the system’s dynamic changes, and the controller is designed using a Lyapunov-like function to ensure stability. Simulation results show that the proposed controller achieves better tracking accuracy and significantly lower energy usage, with total force and moment values reduced compared to the standard RBF controller. Specifically, the proposed controller uses 3010.7 N of force and 2.2427 Nm of moment, while the standard controller requires 3150.2 N and 15.197 Nm. These results confirm that the proposed method provides improved performance and energy efficiency. This research highlights the potential of integrating performance bounds in neural network control for robust quadrotor navigation. Future work includes real-world experiments to validate performance under varying COG perturbations.
Enhancing Novice Developer Efficacy through UX Journey: Integrating User Experience and User Requirement to Develop Developer Skills Kusuma, Wahyu Andhyka; Jantan, Azrul Hazri; Admodisastro, Novia Indriaty; Norowi, Noris Mohd
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1848

Abstract

User experience and user requirements are two different approaches to software development. User requirements focus on meeting customer expectations and demands for software solutions, while user experience covers all aspects of software interaction with users. To increase the value of the software, the software must have usable and easy-to-use features with an attractive design or work environment that fits the user's behavior. Integrating software requirements and user experience can increase developer productivity by focusing on features that meet user requirements and expectations. This integration can also increase software development efficiency by addressing issues arising during development. This article addresses developers' challenges when addressing user needs and provides practical solutions widely accepted in industry and academia. Combining user experience and user needs into the UX Journey approach can increase developer productivity and confidence in software development. The design of the UX Journey is carried out by evaluating several existing design solution methods such as Design Thinking, IDEO, HPI, and Double Diamond to determine the existing conditions and needs for the problems faced. Then, by mapping the user, context, and domain, the model is obtained. appropriate. The proposed model comprises Discover, Explore, Test, and Listen activities. A trial was carried out on the respondents to test the method, and a feasibility test and an implementation schedule were obtained based on the statistical analysis of the initial user. It took 980-1500 minutes to complete the design solution. Focusing on features that align with user needs and improve problem-solving efficiency throughout development gives developers greater confidence in producing high-quality software.
E-commerce Product Review Classification using Neural Network-Based Approach Ihtada, Fahrendra Khoirul; Abidin, Zainal; Crysdian, Cahyo
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.2845

Abstract

E-commerce has become an integral part of how people shop, with the rise of customer reviews on various platforms. These reviews provide important insights into product, customer service, and delivery. The growing volume of e-commerce reviews makes manual sorting time-consuming and error-prone for business owners. This study aims to classify e-commerce reviews into three categories: product, customer service, and delivery. The data was collected from e-commerce customer reviews on Tokopedia and labeled using crowdsourcing for ground truth. To classify the reviews, a Neural Network is performed with various numbers of node and learning rate. TF-IDF is also used for feature extraction to capture important features from the review data. From nine test scenarios, model B3 with 50 nodes in the first hidden layer and a learning rate of 0.1 provided the best performance with an accuracy of 65.85%, precision of 62.27%, recall of 58.61%, and f1-score of 59.71%. Validation using K-Fold Cross Validation shows an average accuracy of 64.17% at k=10. Word analysis with TF-IDF identified dominant words in each category. The B3 model is not yet able to classify reviews perfectly, due to the large and unbalanced dataset, less complex model architecture, and less effective TF-IDF preprocessing. However, this study shows potential for better classification in the future. With optimization, this model can be very useful for e-commerce business owners to gain insight from customer reviews and can help them to identify aspects that will lead to customer satisfaction and trust.
Batik Recognition and Classification Using Transfer Learning and MobileNet Approach Sastypratiwi, Helen; Muhardi, Hafiz; Yulianti, Yulianti
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.8.4.2407

Abstract

In the vibrant tapestry of Indonesian culture, Batik motifs stand out as a testament to its enduring artistic heritage. Yet, adapting these intricate patterns, particularly the mesmerizing "insang" featuring fish-like forms, presents a unique challenge for modern applications. Limited datasets and the need for efficient mobile solutions create a bottleneck in accurate motif classification. This research boldly tackles this challenge by proposing a groundbreaking approach: marrying the power of MobileNet architecture, specifically designed for mobile devices, with transfer learning techniques. Transfer learning acts as a bridge, leveraging knowledge from a vast dataset to compensate for limited data specific to Batik. This synergy unlocks remarkable accuracy, with our method achieving a stunning 98% classification rate in under a second on mobile devices. The implications of this breakthrough are far-reaching. It safeguards Batik's legacy by enabling its digital preservation and paves the way for its seamless integration into contemporary design. It is predicted that Batik motifs can adorn digital interfaces, enrich user experiences, and inspire innovative fashion trends. This research is a beacon illuminating the path for Batik to evolve and thrive in the digital age. By empowering mobile devices to recognize and interpret these intricate patterns, it aims to unlock many possibilities. Batik's rich history can be woven into the fabric of modern life, enriching our digital landscapes and fostering a deeper appreciation for this cultural gem. This is not merely a technological feat; it is a celebration of tradition, a bridge between generations, and a testament to the enduring power of creativity.
Comparative Analysis of Homomorphic and Morphological Filters Using Inception V3 for Thermal Facial Image Classification of Autistic Children Catur Andryani, Nur Afny; Melinda, Melinda; Tariliani, Cut Dara; Oktiana, Maulisa; Junidar, Junidar
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.9.3.2885

Abstract

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder characterized by varying degrees of difficulty in social interaction and communication and repetitive behaviors. Early confirmation of the diagnosis of ASD leads to early appropriate treatment. However, confirming ASD diagnosis is challenging due to its wide spectrum and challenging behavior assessment. This research proposes a technology-based ASD diagnosis on children utilizing thermal facial analysis. This is conducted subject to the uniqueness of facial expression that is typically applied to children with ASD. A modified Inception V3 architecture did the intended thermal facial analysis for ASD diagnosis. Homomorphic filters and morphological filters are applied to the data pre-processing to improve the classification ability. The proposed identification method shows better sensitivity to the false-positive aspect. It is indicated by better performance in terms of precision, with a rate of 90% to 91%. This research is expected to support medical experts in confirming early diagnosis in children with ASD.
Strategic Recommendations in Increasing Gen Z User Engagement towards Gamification Elements with Fuzzy AHP and Octalysis Approaches Marisa, Fitri; Istiadi, -; Ahmad, Sharifah Sakinah Syed; Handajani, Endah Tri Esti; NoerTjahyana, Agustinus; Maukar, Anastasia L
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3324

Abstract

Generation Z (Gen Z), often referred to as the "digital native" generation, interacts extensively with digital technology and social media. E-commerce companies need to adopt the right strategies, such as gamification, to increase user engagement among Gen Z. However, there is limited research evaluating which gamification elements are most effective in engaging Gen Z users. This study addresses this gap by identifying the most impactful gamification elements that enhance Gen Z user engagement and providing strategic recommendations for e-commerce designers and developers. Using the Fuzzy AHP method and Octalysis approach, this study evaluates five gamification elements: Point, Reward, Referral, Leaderboard, and Level across four key parameters: Motivation, Engagement, User Experience, and Retention. The Fuzzy AHP results indicate that the "Reward" element ranks highest with a score of 1.0, followed by "Level" with a score of 0.829. "Leaderboard" comes in third with a score of 0.669, while "Point" and "Referral" score 0.606 and 0.220, respectively. The low score of "Referral" suggests its limited effectiveness in fostering social connectedness among Gen Z users. The Octalysis analysis reveals that "Reward" has the most significant influence on core drives such as "Development and Accomplishment" and "Scarcity and Impatience," with an average score of 7.25, followed by "Level" with a score of 7.125. These findings underscore the importance of prioritizing "Reward" and "Level" to optimize user engagement for Gen Z. The practical implications of this study suggest that e-commerce platforms should integrate these gamification elements to create more engaging and interactive shopping experiences for Gen Z users, aligning with their preferences and motivations.
A Better Performance of GAN Fake Face Image Detection Using Error Level Analysis-CNN Siregar, Maria Ulfah; Nurochman, Nurochman; Setianingrum, Anif Hanifa; Larasati, Dwi; Santoso, William; Stefany, Meisia Dhea
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.9.2.2698

Abstract

The use of face images has been widely established in various fields, including security, finance, education, social security, and others. Meanwhile, modern scientific and technological advances make it easier for individuals to manipulate images, including those of faces. In one of these advancements, the Generative Adversarial Network method creates a fake image similar to the real one. An error-level analysis algorithm and a convolutional neural network are proposed to detect manipulated images generated by generative adversarial networks. There are two scenarios: a stand-alone convolutional neural network and a combination of error-level analysis and a convolutional neural network. Furthermore, the combined scenario has three sub-scenarios regarding the compression levels of the error-level analysis algorithm: 10%, 50%, and 90%. After training the data obtained from a public source, it becomes evident that using a convolutional neural network combined with compression of error level analysis can improve the model’s overall performance: accuracy, precision, recall, and other parameters. Based on the evaluation results, it was found that the highest quality convolutional neural network training was obtained when using 50% error level analysis compression because it could achieve 94% accuracy, 93.3% precision, 94.9% recall, 94.1% F1 Score, 98.7% ROC-AUC Score, and 98.8% AP Score. This research is expected to be a reference for implementing image detection processes between real and fake images from generative adversarial networks.
Systematic Literature Review: An Early Detection for Schizophrenia Classification Using Machine Learning Algorithms Azizi, Ainin Sofiya; Kamal, Marnisha Mustafa; Azizan, Nurzarifah; Zawawi, Rohaizaazira Mohd; Zakaria, Noor Hidayah; Salamat, Mohamad Aizi; Yulherniwati, -
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.8.3-2.2446

Abstract

Schizophrenia is a complex mental health disorder that poses significant challenges in diagnosis and treatment due to its multifaceted symptoms, such as hallucinations, delusions, and cognitive impairments. Early detection is crucial for effective intervention, yet traditional diagnostic methods often fail in precision and scalability. This systematic literature review investigates the application of machine learning (ML) algorithms in the early detection and classification of schizophrenia. By synthesizing findings from 40 primary studies, the review highlights the effectiveness of diverse ML models, including Random Forests, Support Vector Machines (SVM), and advanced deep learning techniques like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Key datasets such as clinical records, EEG signals, and neuroimaging data were analyzed to evaluate model performance across metrics like accuracy, precision, and sensitivity. Studies demonstrated that hybrid approaches, integrating multiple data sources and deep learning architectures, achieved classification accuracies exceeding 90%, with notable advancements in early-stage diagnosis. However, the review identifies critical challenges, including data quality issues, biases, and limited external validation, which hinder the widespread clinical application of these models. Through a comparative analysis of ML methods and traditional supervised approaches, the study underscores the transformative potential of ML in enhancing diagnostic accuracy and facilitating personalized treatment plans. Addressing current limitations, such as expanding data diversity and improving model interpretability, is essential for translating these findings into practical healthcare solutions. This research contributes to the growing knowledge in ML-driven diagnostics, advocating for its integration into clinical workflows to optimize schizophrenia management.

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