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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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Security System for Door Locks Using YOLO-Based Face Recognition Putri, Hasanah; Hadiyoso, Sugondo; Putri Fatoni, Salwa Berliana; Octaviany, Vany; Wulandari, Astri; Aprilina, Riska; Rosmiati, Mia
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2410

Abstract

Di era kemajuan teknologi dan algoritma canggih yang memudahkan hidup manusia, kunci pintar pengenalan wajah merupakan sistem yang menggunakan salah satu algoritma tersebut dan mengatasi masalah keamanan dalam teknologi rumah pintar. Kunci pintar ini dapat dipasang di dekat pintu untuk memantau rumah, perusahaan, dan universitas. Masalah dengan solusi kunci pintar pengenalan wajah saat ini adalah bahwa kunci pintar tersebut kurang cepat dan tepat. Pintu merupakan salah satu komponen bangunan yang perlu diperhatikan keamanannya untuk mencegah upaya pencurian. Bangunan yang memiliki banyak ruang harus memiliki pintu dengan sistem keamanan yang kuat, salah satunya adalah hotel. Alat yang sering digunakan untuk mengakses kamar hotel adalah RFID. Mobil RFID memiliki banyak kekurangan, antara lain tamu sering meninggalkan kartu RFID mereka di kamar sehingga mereka tidak dapat lagi memasuki kamar dan harus melapor ke resepsionis terlebih dahulu, kartu RFID juga mudah hilang sehingga tamu yang kehilangan kartu RFID akan didenda sebagai biaya penggantian kartu. Oleh karena itu, dibuatlah sistem keamanan pintu menggunakan pengenalan wajah dengan algoritma YOLO. Algoritma YOLO digunakan untuk mendeteksi wajah siapa saja yang ingin mengakses pintu. Hasil pengujiannya adalah sistem dapat mendeteksi wajah dengan tingkat akurasi 94,4%.
Systematic Literature Review on Persuasive System Design Framework for Managing Curriculum Performance Saifunnizam, Syamir Thaqif; Md Fudzee, Mohd Farhan; Hanif Jofri, Muhamad; Kasim, Shahreen; Arrova Dewi, Deshinta; Arshad, Mohamad Safwan; Yulherniwati, -
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3663

Abstract

Integrating digital resources into educational assessment has led to the widespread adoption of e-portfolios as tools for documenting and evaluating student achievement, thereby transforming traditional evaluation methods. However, the existing frameworks primarily focus on assessing academic performance, often neglecting the comprehensive monitoring of student’s co-curricular activities. To overcome current gaps in comprehensive student evaluation, this study introduces a conceptual framework incorporating persuasive system design (PSD) into an e-portfolio to facilitate efficient co-curricular performance monitoring in Malaysian secondary schools. To ensure a thorough approach to educational evaluation, it is essential to effectively monitor and manage academic and extracurricular performance to understand student progress comprehensively. By adding Physical Activity, Sports, and Co-curriculum Assessment (PAJSK) – specific categories and key PSD elements- primary task support, dialogue support, system credibility support, and social support- that are all designed to improve user engagement and system dependability in an educational environment, the framework builds on the Oinas-Kukkonen and Harijumaa PSD Model. This study adapts and discusses the persuasive design elements to meet the goals of educational assessment frameworks by comparing PSD implementation in e-health, e-tourism, e-commerce, and e-learning. The results offer an overview of developing a practical, engaging e-portfolio framework that facilitates comprehensive student evaluation, especially in educational environments focusing on co-curricular achievement.
Development of a Life Story-Based Digital Counseling Model to Detect Student Depression Using LSTM Jiwa Permana, Agus Aan; Sudarma, Made; Sukarsa, I Made; Hartati, Rukmi Sari
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.9.2.2642

Abstract

This research aims to develop an LSTM-based model to help counselors analyze depressive symptoms in students based on their life stories. Depression often occurs among students, which can affect their lives. However, counselling can overcome these mental problems. In order to support the Indonesian government's programs in the field of mental health, concrete steps are needed. One concrete effort is to prevent children from experiencing depression. Depression can be recognized early through a counselling approach. Currently, counselling can be done using digital counselling technology. Therefore, a reliable model is needed to help counsellors. This research used 2,551 tweets about someone's life story from 2,581 datasets. ANN method with LSTM (Long Short-Term Memory) architecture. This counselling is effective in helping individuals resolve psychological and emotional problems, especially depression. The advantage of LSTM is that it can delete data that is no longer relevant. This method effectively processes, predicts, and classifies data based on a certain time sequence. The dataset was taken from Twitter(X) and then validated by experts before being trained with the model. As a result, the model can recognize the depression levels with a test accuracy of 86%. This research has implications in psychology regarding cases of student mental health in realizing the vision of Indonesia in 2045.
Challenges of Agile Software Development in the Banking Sector: A Systematic Literature Review Letelay, Kornelis; Mola, Sebastianus A. S; Go, Ratna Yulika
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2300

Abstract

The banking industry is expected to thrive, generate profits, and contribute to national development and societal welfare. However, this sector is susceptible to volatility caused by global and domestic economic fluctuations. This research aims to identify and address challenges related explicitly to implementing agile methodologies within the banking sector. The study utilized a Systematic Literature Review (SLR) approach based on the guidelines provided by Kitchenham. A substantial number of academic journals (1,933) were analyzed during this review. Among the vast pool of literature, 28 relevant studies were extracted. These studies were chosen because they provided insights into the challenges of implementing agile practices in the banking domain. The analysis and categorization of these studies were structured according to the Project Management Body of Knowledge (PMBOK) 6th edition framework. This framework was employed to organize and understand the identified challenges systematically. The study's primary finding is that the most prevalent challenge encountered in the context of agile development within the banking sector is "Project Resource Management." In essence, effectively managing and allocating resources is a significant hurdle banks face when adopting agile methodologies. The challenges related to resource management are not confined to a single aspect. Instead, they encompass various dimensions, including human resources, technological resources, and organizational factors. This suggests that challenges in agile banking are multifaceted, involving issues related to people, technology, and the structure and processes within banking organizations.
Boosting Performance of SVM in Koi Classification Using Direct Methods-Based Optimization Arkananta, Muhammad Hafizh; Fawwaz Al Maki, Wikky
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.8.4.2474

Abstract

Many koi fish enthusiasts keep or buy them just for their attractive colors without knowing what type of koi fish they are. The manual classification of koi fish species is still frequently incorrect. As a result, it is critical to apply a machine learning technique to identify various koi fish species. This research implemented a computer vision algorithm to classify koi fish species using the Support Vector Machine (SVM) as the classifier. However, the maximum accuracy SVM can achieve in our koi fish classification system is 79%.  To achieve better performance, the SVM was optimized by applying various optimization methods from the Direct Method group, i.e., the Generalized Pattern Search (GPS), the Powell method, and the Nelder-Mead method. Three optimization methods from the Direct Method group have successfully improved the performance of SVM in this task. Experimental results demonstrated that using the Generalized Pattern Search (GPS) in our classification system can increase the accuracy to 98%. Also, implementing the Powell and the Nelder-Mead method can make the koi classification system obtain a better accuracy of 99%. These results indicate that the proposed approach is a viable solution to overcome the limitations of the SVM algorithm.
Face Recognition for Logging in Using Deep Learning for Liveness Detection on Healthcare Kiosks Ryando, Catoer; Sigit, Riyanto; Setiawardhana, Setiawardhana; Sena Bayu Dewantara, Bima
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2759

Abstract

This study explores the enhancement of healthcare kiosks by integrating facial recognition and liveness detection technologies to address the limitations of healthcare service accessibility for a growing population. Healthcare kiosks increase efficiency, lessen the strain on conventional institutions, and promote accessibility. However, there are issues with conventional authentication methods like passwords and RFID, such as the possibility of them being lost, stolen, or hacked, which raises privacy and data security problems. Although it is more secure, face recognition is susceptible to spoofing attacks. In order to improve security, this study integrates liveness detection with face recognition. Data preparation is done using deep learning algorithms, namely FaceNet and Multi-task Cascaded Convolutional Neural Networks (MTCNN). Real-time authentication of persons is verified by the system, which provides correct identification of them. Techniques for enhancing data help the model become more accurate and robust. The system's usefulness is shown by the outcomes of the experiments. The VGG16 model outperforms alternative designs like MobileNet V2, ResNet-50, and DenseNet-121, achieving 100% accuracy in liveness detection. Face recognition and liveness detection together greatly improve security, which makes it a dependable option for real-world healthcare applications. Through the ability to differentiate between genuine and fake faces and foil spoofing efforts, facial liveness detection may boost security. This study offers insights into building biometric systems for safe and effective identity verification in the healthcare industry.
Batiknet: Batik Classification-based Management Application for Inexperienced User Putra, Muhammad Taufik Dwi; Pradana, Hilmil; Munawir, Munawir; Pradeka, Deden; Yuniarti, Ana Rahma; Sadik, Jafar; Andhika R, Muhammad
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.8.4.3086

Abstract

Batik has significantly contributed to the Indonesian economy, is diverse, and is spread throughout cities. Currently, batik patterns are very diverse and spread from Sabang to Merauke. Each batik pattern holds distinct meanings, philosophies of life, and ancestral heritage and reflects the region where it was crafted. We introduce a new batik dataset containing five patterns: Kawung, Megamendung, Parang, Sekarjagad, and Truntum. The Convolutional Neural Network (CNN) method is an effective Deep Learning method for extracting image information. CNNs have become the state of the art for various image processing tasks, such as classification, segmentation, and object recognition. This study used several state-of-the-art architectures, including Xception, ResNet50V2, MobileNetV2, and DenseNet169. However, we chose EfficientNetV2 as the primary feature extractor due to its superior performance. Our results show that EfficientNetV2 outperformed other architectures in training, validation, and testing accuracy, making it the best choice for classifying batik patterns. The training process resulted in an accuracy of 98% for training, 97% for validation, and 96% for testing. To ensure the accessibility and practical application of this research, we developed a user-friendly, web-based interface with a RESTful API, making the tool accessible to a broader audience. The application is named "BatikNet," a name chosen to reflect the blend of traditional batik culture ("Batik") with neural network technology ("Net"). This research contributes a valuable dataset and a practical tool for future studies and applications in batik pattern recognition and supports the preservation and understanding of Indonesian cultural heritage
A scoping review and bibliometric analysis (ScoRBA) on dengue infection and machine learning research Zahiruddin, Haikal; Zukarnain, Zuriani Ahmad; Wijaya, Adi
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.8.4.2249

Abstract

Dengue, a fast-spreading vector-borne infectious disease, requires early prediction and prompt decision-making for effective control. To address this issue, we present a comprehensive scoping review and bibliometric analysis (ScoRBA) that aims to map the current literature landscape, identify main research themes, and offer valuable insights into advancements and challenges in dengue infection and machine learning research. Materials for this analysis consist of scholarly articles related to dengue and machine learning research retrieved from the Scopus database. Our method involves a rigorous literature examination, utilizing keyword co-occurrence analysis. Our study reveals a growing interest in dengue and machine learning research, reflected in an increasing number of publications. Through keyword co-occurrence analysis, we identify four major research themes: Data mining using machine learning for dengue prediction, Deep learning approach for dengue prediction models, Neural network optimization for dengue diagnostic systems, and Climate-driven dengue prediction with IoT & remote sensing. Advancements include substantial improvements in prediction models through machine learning and IoT integration, albeit with identified limitations, necessitating ongoing research and refinement. Our findings hold direct implications for public health professionals, academics, and decision-makers, offering data-driven strategies for dengue outbreak control. The identified research themes act as a roadmap for future investigations, guiding the development of more robust tools for early prediction and decision-making in the battle against dengue. This study contributes to understanding the evolving landscape of dengue research, facilitating informed actions to mitigate the impact of this infectious disease. 
Information Behavior Model of e-Health Literacy for Online Health Information-seeking Effectiveness Xuewen, Wang; Azmi Murad, Masrah Azrifah; ZhangLi, Wu; Ismail, Ismi Arif; Mohamed Shaffril, Hayrol Azril
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.8.4.3642

Abstract

This study examines the growing imbalance between the availability and demand for medical resources, rising healthcare costs, and the critical role of accessible health information in disease prevention and public health. The rapid advancement of information technology has established the Internet as a primary source of health information, leading to an overload that surpasses users' processing capacity and causes significant cognitive and emotional challenges. This phenomenon profoundly affects users' health information behavior and decision-making, particularly in self-health management. To address these challenges, eHealth literacy must incorporate an understanding of users' information behavior. This research analyzed the literature on eHealth literacy through a systematic review, identifying key components and categorizing them using Squiers' method. The findings reveal that current definitions fail to address the variability in online health information quality and lack a comprehensive model for understanding information behavior in an overloaded environment. As a solution, this study proposes a new definition of eHealth literacy: the capacity to efficiently search for, access, evaluate, and apply relevant information based on physiological, emotional, and cognitive needs when using electronic health resources. This new definition emphasizes discernment, proactive engagement, personalized use, and practical application of information in health management. The Information Behavior Model of eHealth Literacy (IBeHL) highlights eHealth literacy's multifaceted and dynamic nature, influenced by environmental factors, and recognizes both active information seeking and passive information exposure. Future research should focus on refining this model and exploring its potential to enhance health information behavior and decision-making.
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.

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