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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 59 Documents
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Search results for , issue "Vol 8, No 4 (2024)" : 59 Documents clear
An Investigation of the Student Learning Satisfaction Model for User Story Learning in Software Engineering Course Zul, Muhammad Ihsan; Mohd. Yasin, Suhaila; Sahid, Dadang Syarif Sihabudin
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.8.4.3089

Abstract

Software engineering courses are essential for students to become professional software engineers. These courses expose them to their first user stories (US). Despite extensive studies on US-related issues, quality remains the most prominently discussed topic. Therefore, it is essential to investigate US education in higher education to produce qualified software practitioners. In the educational context, such investigations are typically measured using the learning satisfaction approach. This study aims to investigate the suitability of the learning satisfaction model in software engineering courses, specifically in the US context. Subsequently, the study will identify opportunities for improving US teaching methods. The applied learning satisfaction model consists of four factors: perceived ease of use, perceived usefulness, learning motivation, and learning satisfaction. These factors are derived by combining the Technology Acceptance Model (TAM) and Learning Motivation Theory. The study employs Confirmatory Factor Analysis (CFA) using the partial least squares structural equation modelling (PLS-SEM). The measurement model and model evaluation fit stages are used to assess the suitability of the implemented learning satisfaction model. The structural model examines opportunities for improving the US teaching method based on the identified factors. The study involves 142 software engineering students as respondents. The results indicate that the current model requires adjustments in indicators and model fit, particularly SRMR and NFI, to align with the study. Regarding learning enhancement, the factors of perceived ease of use and perceived usefulness suggest that improvements in US teaching methods are necessary to increase student learning satisfaction in US learning.
Toward Adoption User Experience Variables for Solo Software Development in Academic and Industry Kusuma, Wahyu Andhyka; Jantan, Azrul Hazri; Admodisastro, Novia Indriaty; binti Mohd Norowi, Noris
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.8.4.2172

Abstract

User experience (UX) is frequently mentioned as a rapidly expanding profession. It generates prospects in almost every industry for individuals with expertise in applying design principles and techniques centered around user needs. Many universities specializing in informatics and computer science have acknowledged this demand by integrating more coursework focused on UX into their educational programs. However, the widespread adoption of agile software development and efficient product design has prompted businesses to seek experienced candidates for UX positions, even at entry level. Consequently, individuals aspiring to work in this position need UX experience before acquiring it. Materials: We conducted a pilot study on 235 respondents who voluntarily participated in the research to examine the effect of UX on students' ability to identify a problem in software requirements. Method: This article evaluates the endeavors of a particular institution to bridge this experience gap. The article offers insights and recommended practices for effectively integrating. This research involved industries from two developed countries and one developing country, as well as implementing these aspects on students at one of the institutions with a General Self Efficacy (GSE) scale. Results: The results show the dominant user experience quality aspects. In addition, we provide recommendations for applying to several courses and competencies to enhance student self-efficacy. Implication for Further Research: In this paper, we used a combination of developing a conceptual framework and identifying the industry. With these two methods, we solve the UX gap between industry and academia from the perspective of the UX attribute. Some standards are commonly used in academics to construct the curriculum for their student.
Application of Artificial Intelligence in Detecting SQL Injection Attacks Augustine, Nwabudike; Md. Sultan, Abu Bakar; Osman, Mohd Hafeez; Sharif, Khaironi Yatim
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.8.4.3631

Abstract

SQL injection attacks rank among the most significant threats to data security. While AI and machine learning have advanced considerably, their application in cybersecurity remains relatively undeveloped. This work mainly aims to solve the IT-related challenge of insufficient knowledge bases and tools for security practitioners to monitor and mitigate SQL Injection attacks with AI/ML techniques. The study uses a mixed-methods approach to evaluate how well different AI and ML algorithms identify SQL injection attacks by combining algorithmic evaluation with empirical investigation. Datasets of well-known SQL injection attack patterns and AI/ML models intended for cybersecurity anomaly detection are among the resources underexplored; these findings show the potential for boosting detection capabilities by deploying ML and AI-based security solutions; specific algorithms have demonstrated success rates of up to 80% in detecting SQL injections. Despite this promising performance, around 75% of survey participants acknowledged a decrease in harmful content, with a similar number highlighting increased efficiency in their roles as security researchers or incident responders. Nevertheless, the tool’s adoption among cybersecurity professionals remains under 30%. This underscores a gap between the capabilities these technologies offer and their current level of adoption among professionals. This will help lay the groundwork for future work in identifying the best solutions and providing potential approaches to incorporating AI/ML into cybersecurity frameworks. The implications of this study indicate that adopting robust defenses against SQL injection and other cyber threats could increase many folds if we continue to research and implement AI ML. technologies.
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.
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.
Enhancing Contactless Respiratory Rate Measurement Accuracy: Integration of 24GHz FMCW Radar and XGBoost Machine Learning Arisandy, -; Erfianto, Bayu; Setyorini, -
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.8.4.2654

Abstract

Advancements in non-contact vital sign monitoring are crucial for enhancing patient measurements' accuracy and overall patient experiences. This research explores the integration of 24GHz Frequency-Modulated Continuous-Wave (FMCW) radar with the XGBoost machine learning algorithm to improve the detection of respiratory rate (RR). This innovative approach offers a promising alternative to traditional contact-based methods. The study utilizes FMCW radar to detect respiratory motion, while signal patterns are analyzed using XGBoost to ensure accuracy across various healthcare environments. The method involves collecting signals, pre-processing to remove noise and irrelevant data, and extracting features to be analyzed by the XGBoost algorithm. The collected dataset, which includes controlled and randomized respiratory rates from a diverse subject pool, establishes a solid basis for the algorithm's training and validation, ensuring extensive adaptability and precision. Empirical results show that XGBoost surpasses other machine learning models' accuracy and reliability. Importantly, this method significantly reduces error margins compared to established benchmarks, leading to substantial improvements in RR measurement. The implications of this study are wide-ranging, indicating that such a system could significantly enhance patient care standards by providing continuous, accurate, and non-intrusive monitoring, especially in settings where traditional methods are impractical or uncomfortable. Future research should aim to refine the system's real-world applicability, assess long-term reliability, and optimize the technology for integration into existing healthcare frameworks, thereby further transforming the landscape of patient monitoring technologies.
Assessing the Multifaceted Determinants of Collaborative Competence Among Students in the Digital Learning: A Comprehensive Analysis Saputra, Indra; Sari, Resti Elma; Mahniza, Melda; Hayatunnufus, Hayatunnufus; Rahmiati, Rahmiati; Yanita, Merita; Yupelmi, Mimi
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.8.4.2202

Abstract

The study background related to the students' collaborative competence in digital learning is still relatively low. The objective of this study is to examine the elements influencing collaborative competence. The study involved 107 cosmetology and beauty education students. The method used was a survey with data collection using questionnaires developed based on predetermined variable indicators. The analysis of data employed Structural Equation Model-Partial Least Square (SEM-PLS) with Smart PLS 4.0 software. The SEM results describe the Convergent Validity (Loading Factor and Average Variance Extracted) and Discriminant Validity (Fornell Larcker Criterion and Cross Loading), which states that the measurement model is valid. Furthermore, the Composite reliability and Cronbach's Alpha conclude that the measurement model is reliable. The analysis results indicate a positive and significant correlation of predictor variables, including project-based learning, social media, instructional approach, and material relevance to collaborative competence. Based on the variable analysis, material relevance becomes the highest aspect, followed by project-based learning, which increases the collaborative competence of students. Conversely, social media as a mediator variable weakens the level of correlation of the predictor variables to collaborative competence. This study contributes to understanding factors affecting students' collaborative competence in digital learning environments, with significant implications for educators, institutions, and policymakers in shaping digital learning frameworks enhancing collaborative competence. Future research, including longitudinal studies, could investigate the lasting impact of digital learning environments on developing collaborative competence over time.
2.5D Face Recognition System using EfficientNet with Various Optimizers Teo, Min-Er; Chong, Lee-Ying; Chong, Siew-Chin; Goh, Pey-Yun
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.8.4.3030

Abstract

Face recognition has emerged as the most common biometric technique for checking a person's authenticity in various applications. The depth characteristic that exists in 2.5D data, also known as depth image, is utilized by the 2.5D facial recognition algorithm to supply additional details, strengthening the system's precision and durability. A deep learning approach-based 2.5D facial recognition system is proposed in this research. The accuracy of 2.5D face recognition could be enhanced by integrating depth data with deep learning approaches. Besides, optimizers in the deep learning approach act as a function for adjusting the properties, like learning rates and weights in the neural network, which can minimize the overall loss of the system and further enhance performance. In this paper, several experiments have been conducted in two versions of EfficientNet architectures, such as EfficientNetB1 and EfficientNetB4, using different optimizers, including Adam, Nadam, Adamax, RMSProp, etc. Various optimizers are compared to find the most suitable optimizer for the system. The Face Recognition Grand Challenge version 2 (FRGC v2.0) database was utilized in this research. This research aims to increase the 2.5D face recognition system’s effectiveness and efficiency by implementing deep learning approaches. Based on the experimental result, a deep learning algorithm enhances the system's accuracy rate. It also proves that the EffifientNetB4, using Adam optimizer, gained the highest accuracy rate at 97.93%.
Comparison of Noise Using Reduction Method for Repairing Digital Image Masa, Amin Padmo Azam; Fajri, Muhamad Mushfa Hikmatal; Septiarini, Anindita; Winarno, Edy
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.8.4.2032

Abstract

Digital images are used to become a visual bridge of information. The information data must be precise so that the information can be adequately conveyed, but in the process, digital images sometimes experience a change in quality. One of the causes of this change is noise, where the image affected by noise is of poor quality, so misinformation can occur. This problem can be solved using filtering methods, but there are so many filtering methods. In this study, five filtering methods were used, including the Gaussian filter, mean filter, median filter, wiener filter, and conservative filter, to be compared with two types of noise, such as salt and pepper and speckle, so that the best method for noise reduction in digital images is known based on the criteria that have been set determined. The research results were determined based on the value of the measurement parameters Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The results show that the conservative method is the best based on the parameter values of MSE 3.21 and PSNR = 37.99. However, when viewed visually, the median method is superior for reducing noise in digital images that have been carried out. The results of the research can be used as information to develop future research, especially in the field of digital image processing.

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