cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rahmat Hidayat
Contact Email
mr.rahmat@gmail.com
Phone
-
Journal Mail Official
rahmat@pnp.ac.id
Editorial Address
-
Location
Kota padang,
Sumatera barat
INDONESIA
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 52 Documents
 2   3   4   5   6 
Search results for , issue "Vol 9, No 1 (2025)" : 52 Documents clear
Visualization of Prediction Potential Hotspots in Multidimensional Datasets Dudáš, Adam; Modrovičová, Bianka
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.9.1.2477

Abstract

Correlation analysis and visual analysis of multidimensional datasets with the objective of identification of patterns and trends is an essential element of decision-making processes. Conventional visualization models in the considered area, such as correlation heatmaps, are used to visually represent the value of the correlation coefficient measured between pairs of attributes of the multidimensional dataset but are hard to read when working with a large number of attributes. This study concerns the design and implementation of a visualization model, which can be used to identify prediction potential hotspots in analysed datasets - parts of the dataset, which are strongly correlated with a high number of attributes in the dataset. The proposed model focuses on a graphical representation of such hotspots based on planar, multicomponent graphs, with the aim of meta-analysis of large, multidimensional datasets. The implemented approach is evaluated on a case study focused on the analysis of the original cubic graph property dataset where several prediction potential hotspots of different correlation types are constructed. Other than the construction of the hotspots themselves, this study shows a comparison of results gained by the graphical model to the conventional model used in the meta-analysis of multidimensional datasets – Shapley value explanations. The results presented in this study point to the need for a robust visualization framework for the analysis of correlation structures in multidimensional datasets and for models of visualization based on virtual and augmented reality.
Single Image Estimation Techniques for SEM Imaging System Lew, Kai Liang; Sim, Kok Swee; Tan, Shing Chiang
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.9.1.3505

Abstract

Estimating a single image's signal-to-noise ratio (SNR) is a critical challenge in Scanning Electron Microscopy (SEM), impacting image quality and analysis reliability. SEM images are essential for revealing structural details at the micro- or nanoscale, but noise often obscures these details, complicating interpretation. Traditional SNR estimation methods required two images to compare and assess the noise levels. SEM images are usually corrupted by noise through several operating conditions, such as dwell time, probe current, and specimen composition. This paper introduces a novel single-image SNR estimation technique, Quarsig SNR Estimation (QSE), for estimating SNR value in SEM images. This method differs from the traditional methods because it only uses a single image to obtain the SNR value without a reference image. This approach involves a single image with Gaussian noise and using the autocorrelation function (ACF) to calculate the peak value for both the original and noisy images. The peak value is the SNR value for the noisy image. QSE has outperformed the existing methods, such as Nearest Neighborhood (NN), Linear Interpolation (LI), and the combination of NN and LI by archiving the nearest SNR value to the reference measurements. This shows that QSE has significant potential for single-image SNR estimation under Gaussian noise. However, its performance under non-Gaussian noise remains a limitation. Despite this, QSE has showcased its reliability in the SEM imaging field by improving the analysis of structural details in noisy imaging conditions.
A Comprehensive Review of Cyber Hygiene Practices in the Workplace for Enhanced Digital Security Armoogum, Sheeba; Armoogum, Vinaye; Chandra, Anurag; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Bappoo, Soodeshna; Mohd Salikon, Mohd Zaki; Alanda, Alde
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.9.1.3787

Abstract

In today's digital age, cybercrime is increasing at an alarming rate, and it has become more critical than ever for organizations to prioritize adopting best practices in cyber hygiene to safeguard their personnel and resources from cyberattacks. As personal hygiene keeps one clean and healthy, cyber hygiene combines behaviors to enhance data privacy. This paper aims to explore the common cyber-attacks currently faced by organizations and how the different practices associated with good cyber hygiene can be used to mitigate those attacks. This paper also emphasizes the need for organizations to adopt good cyber hygiene techniques and, therefore, provides the top 10 effective cyber hygiene measures for organizations seeking to enhance their cybersecurity posture. To better evaluate the cyber hygiene techniques, a systematic literature approach was used, assessing the different models of cyber hygiene, thus distinguishing between good and bad cyber hygiene techniques and what are the cyber-attacks associated with bad cyber hygiene that can eventually affect any organization. Based on the case study and surveys done by the researchers, it has been deduced that good cyber hygiene techniques bring positive behavior among employees, thus contributing to a more secure organization. More importantly, it is the responsibility of both the organization and the employees to practice good cyber hygiene techniques. Suppose organizations fail to enforce good cyber hygiene techniques, such as a lack of security awareness programs. In that case, employees may have the misconception that it is not their responsibility to contribute to their security and that of the organization, which consequently opens doors to various cyber-attacks. There have not been many research papers on cyber hygiene, particularly when it comes to its application in the workplace, which is a fundamental aspect of our everyday life. This paper focuses on the cyber hygiene techniques that any small to larger organization should consider. It also highlights the existing challenges associated with the implementation of good cyber hygiene techniques and offers potential solutions to address them.
Multilayer Perceptron Model with Feature Extraction for Potassium Deficiency Identification of Cocoa Plants Basri, Basri; Karim, Harli A; Assidiq, Muhammad; Arafah, Muhammad; Rahmadani, Fitria
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.9.1.2829

Abstract

The development of Multilayer Perceptron (MLP) models for networked learning systems heavily relies on the specific application case study and the accurate parameterization aligned with the chosen computer vision feature extraction models. This study proposes an MLP model for identifying potassium deficiency in cocoa plants. The feature extraction methodology employs object feature extraction that commonly used in computer vision, including Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM), and Hue Saturation Value (HSV) models. These computer vision techniques aid in analyzing leaf characteristics classified into two categories: normal conditions and leaves identified with potassium deficiency. The dataset used in this research comprises two conditions: with a white background and without any specific background. The study evaluates various feature extraction techniques based on MLP parameters, incorporating network learning rates and optimizing solvers. Employing the ROC analysis method throughout the data collection, algorithm development, validation, and analysis phases reveals that the most effective classification performance, reaching up to 93.33% accuracy on the background dataset and 90.00% on the non-background dataset, is achieved using HSV-based color feature extraction with MLP parameters set at an initial learning rate of 10-3 and employing the Adam optimization solver. These outcomes underscore the suitability of HSV color feature extraction for identifying potassium deficiency in cocoa plant leaves. However, optimizing parameters remains crucial to maximize its application in real-time identification systems. Future research should refine these parameters to enhance the model's robustness and efficacy across broader agricultural contexts.
Design and Development of a System for Monitoring Student Attention and Concentration during Learning using CNN Model and Face Landmark Detection Arifin, Syamsul; Aisjaha, Aulia Siti; Fatima, Azzezza Nurul; Mahmudah, Haniah
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.9.1.2897

Abstract

Mobile learning media has been wide and provides a tendency for lecturers to identify students' concentration levels in online classes. To bring the class into active learning, efforts are needed from lecturers and educational institutions to return students' concentration to the ongoing learning process. In this paper, a monitoring and alarm system is designed to increase student concentration and combines two elements of statistical analysis to validate CNN models that recognize face emotions in real time while learning. The research was carried out by recording face data using a camera, extracting digital features, and analyzing facial features. The results of the analysis are used as data input for the decision-making system regarding the level of concentration. The concentration level will be used to activate alarms and send them via chat so that students can focus on learning.The system is created by merging facial expression recognition (FER) and decision-making with a convolutional neural network. The system using a face landmark via camera V2 and a Raspberry Pi 4 performed with the Haar-Cascade classifier, extracting facial features. Face detection via camera is performed using the Haar-Cascade classifier, which extracts facial features. The results of CNN model face detection with landmark features showed good results, with weighted average performance of precision, recall, and F1-score close to 0.99. According to the implementation results, the average number of facial expressions identified in drowsy and neutral states. The device can alert lecturers to how frequently drowsy detects students within a 10-minute interval.
Improving YoloPX using YoloP and Yolov8 for Panoptic Driving Perception Yumeng, Xie; Manshor, Noridayu Binti; Husin, Nor Azura; Chengzhi, Liu
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.9.1.3791

Abstract

Autonomous driving technology (ADS) has seen significant advancements over the past decade, with car manufacturers investing heavily in its development to meet the growing demand for safer, more efficient, and eco-friendly transportation solutions. The panoptic driving perception system is central to ADS, essential for accurately interpreting the driving environment. This system requires high precision, lightweight design, and real-time responsiveness to detect surrounding vehicles, lane lines, and drivable areas effectively. This study introduces an enhanced YOLOPX model that combines YOLOP and YOLOv8 to create an adaptive multi-task learning network capable of traffic object detection, drivable area segmentation, and lane detection. The model integrates YOLOP's detection head with YOLOPX's anchor-free detection head to improve generalization, incorporates YOLOv8's advanced backbone structure to enhance feature extraction accuracy, and retains YOLOP's three-neck architecture to optimize multi-task processing. The improved model employs a mode loss function for segmentation tasks, enhancing generalization and improving lane detection accuracy. Experiments conducted using the BDD100k dataset demonstrated the model's effectiveness: achieving 98.8% accuracy and 27.6% IoU for lane line detection, 90.4% mIoU for drivable area segmentation, and 85.9% recall and 76.9% mAP50 for traffic object detection. This model represents a significant advancement in ADS, enhancing both the safety and reliability of autonomous vehicles.
Music Recommendation Based on Facial Expression Using Deep Learning Kurniawan, -; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Saringat, Zainuri; Firosha, Ardian
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.9.1.3794

Abstract

Music's profound impact on human emotions is essential for creating personalized experiences in entertainment and therapeutic settings. This study introduces a cutting-edge music recommendation system that utilizes facial expression analysis to tailor music suggestions according to the user's emotional state. Our approach integrates a haar-cascade classifier for real-time face detection with a Convolutional Neural Network (CNN) that classifies emotions into seven distinct categories: happiness, sadness, anger, fear, disgust, surprise, and neutrality. This emotionally aware system recommends music tracks corresponding to the user's current emotional condition to enhance mood regulation and overall listener satisfaction. The effectiveness of our system was evaluated through rigorous testing, where the CNN model demonstrated a high degree of accuracy. Notably, the model achieved an overall accuracy of 84.44% in recognizing facial expressions. Precision, recall, and F1 scores consistently exceeded 84%, indicating robust performance across diverse emotional states. These results underscore the system's capability to accurately interpret and respond to complex emotional cues through tailored music suggestions. Integrating advanced deep learning techniques for face and emotion recognition enables our recommendation system to adapt dynamically to the user's emotional fluctuations. This responsiveness ensures a highly personalized music listening experience that reflects the user's feelings and potentially enhances their emotional well-being. By bridging the gap between static user profiles and the dynamic nature of human emotions, our system sets a new standard for personalized technology in music recommendation, promising significant improvements in user engagement and satisfaction.
Overview of Software Re-Engineering Concepts, Models and Approaches Lim, Fung Ji; Sian, Tan Bee
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.9.1.3034

Abstract

 Legacy systems face issues such as integrating new technology, fulfilling new requirements in the ever-changing environment, and meeting new user expectations. Due to the old complex system structure and technology, modification is hardly applied. Therefore, re-engineering is needed to change the system to meet new requirements and adapt to new technology. Software re-engineering generally refers to creating a new system from the existing one. Software re-engineering is divided into three (3) main phases: reverse engineering alteration and forward engineering. Reverse engineering examines, analyzes, and understands the legacy system in deriving the abstract representation of a legacy system; then, through necessary alterations such as restructuring, recording, and a series of forward engineering processes, a new system is built. This paper introduces the concepts of software re-engineering, including the challenges, benefits, and motivation for re-engineering. In addition, beginning with the traditional model of software re-engineering, this paper provides an overview of other models that provide different processes of software re-engineering. Each model has its unique set of processes for performing software re-engineering. Furthermore, re-engineering approaches show various ways of performing software re-engineering. Software re-engineering is a complex process that requires knowledge, tools, and techniques from different areas such as software design, programming, testing, et cetera. Therefore, monitoring the re-engineering process to meet the expectations is necessary.
Content and Network Feature in Attention-based Neural Network for Stance Detection on COVID-19 Vaccination Tweets Bimantara, I Made Satria; Irdayanti, Marina; Nisa, Chilyatun; Purwitasari, Diana
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.9.1.2671

Abstract

Stance detection in COVID-19 vaccination utilizing tweets is crucial for several reasons, such as public health communication, monitoring vaccine sentiment, and identifying misinformation. This research aims to explore the use of attention-based neural networks for stance detection in Indonesian COVID-19 vaccination tweets. The research focuses on enhancing accuracy by integrating content and network features. The content features represent the tweet's text, while network features define the user account's following or unfollowing. The primary contribution of this research is the development of an Attention Long Short-Term Memory (AttLSTM) model for stance detection in Indonesian tweets related to the COVID-19 vaccination. This model combines content and network features to improve accuracy in classifying user attitudes. We also highlight the performance differences between Word2Vec and FastText for numerical text representation in the AttLSTM model. The research used the Indonesian COVID-19 vaccination-related tweet dataset from prior research. The dataset is extracted using user metadata to obtain content and network features necessary to represent users' interest in tweets. Our research method involves data preparation, preprocessing, extraction of content and network features, and the development of an AttLSTM model. By integrating content and network features into the AttLSTM model with Word2Vec text representation, the study demonstrates superior performance compared to the LSTM baseline model and FastText. Adding attention mechanisms to the baseline LSTM model can capture crucial information, such as the minority class inside a tweet's text. Future research will involve exploring advanced data processing methods and ensemble learning techniques to further improve the model's performance.
A Data Pipeline Concept for Digitizing Services in Small and Medium-Sized Companies Chikhalkar, Akshay; Brünninghaus, Marc; Deppe, Sahar; Bicker, Eckard; Röcker, Carsten
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.9.1.3796

Abstract

Small and medium-sized enterprises face significant challenges in their digital transformation due to their limited resources compared to larger companies. In order to overcome these issues, this study proposes the idea of a data pipeline that is affordable and accessible for small and medium-sized enterprises. The suggested method conceptualizes an Extract, Transform and Load (ETL) procedure, which is a go-to approach for data engineering using open-source technologies. A case study of a mobile assistance system is used to illustrate this data flow and emphasizes its numerous advantages and practical uses. Small and medium-sized enterprises can use this data pipeline as a jumping-off point to create a cost-effective, efficient, and scalable data infrastructure. Because the pipeline’s components are modular and completely independent of one another, it is simple to expand, modify, or use individually to meet specific business needs. A basic dashboard prototype that can be modified for different applications is created to show the concept’s viability. Although pipeline design is provided by the concept, its successful execution necessitates technical know-how. To handle resource constraints and data anomalies, this research highlights the necessity of standardized procedures and careful tool selection. The data pipeline’s output may eventually be utilized for sophisticated analytical functions, giving small and medium-sized enterprises the competitive edge they need in the digital era by enabling them with data-driven solutions.

Page 5 of 6 | Total Record : 52

 2   3   4   5   6 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 9, No 6 (2025) Vol 9, No 5 (2025) Vol 9, No 4 (2025) Vol 9, No 3 (2025) Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow Vol 8, No 4 (2024) Vol 8, No 3 (2024) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien Vol 7, No 4 (2023) Vol 7, No 3 (2023) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2-2 (2022): A New Frontier in Informatics Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation Vol 6, No 4 (2022) Vol 6, No 3 (2022) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 4 (2021) Vol 5, No 3 (2021) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 4 (2020) Vol 4, No 3 (2020) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2-2 (2019): Internet of Things and Smart Environments Vol 3, No 4 (2019) Vol 3, No 3 (2019) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 4-2 (2018): Cyber Security and Information Assurance Vol 2, No 3-2 (2018): The Diversity in Information Systems Vol 2, No 4 (2018) Vol 2, No 3 (2018) Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 1, No 4-2 (2017): The Advancement of System and Applications Vol 1, No 4 (2017) Vol 1, No 3 (2017) Vol 1, No 2 (2017) Vol 1, No 1 (2017) More Issue