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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 1,172 Documents
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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.
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.
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.
Detection of Oil Palm Fruit Ripeness through Image Feature Optimization using Convolutional Neural Network Algorithm Setiawan, Dedy; Eko Prasetyo Utomo, Pradita; Alfalah, Muksin
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.9.2.2687

Abstract

The increase in the need for raw materials for palm oil products in the form of food and non-food is felt by the people of Indonesia and other countries. For this reason, triggering oil palm farmers in Indonesia must be able to maximize their production. Currently, oil palm farmers in Indonesia still need help knowing the level of sustainability of oil palm fruit to maintain their production. This research was conducted to identify the maturity level of oil palm fruit using practical images for oil palm farmers in Indonesia. The Convolutional Neutral Network (CNN) algorithm is the research method used to identify pictures of oil palm fruit. The dataset collection comprised 400 images of oil palm fruits divided into three types of classes, namely images of raw, ripe, and rotten oil palm fruits. The dataset was taken from various internet sources, and photos were taken directly using a mobile phone camera according to a predetermined class. This study found that identifying the maturity level of oil palm fruit using the Convolutional Neural Network (CNN) algorithm obtained a high accuracy of 98% in the training process and 76% in the model testing process. The findings of this study can also inspire further research in optimizing image features and using the Convolutional Neural Network (CNN) algorithm more efficiently. This could include a reduction in model training time, the number of parameters, or the development of other techniques that improve algorithm performance.
Integrative Geospatial Analysis of Agricultural Land Resilience Using NDVI-Based Remote Sensing and GIS: Spatio-Temporal Impacts of Urbanization in Sleman Regency (2017–2022) Pamungkas, Guntur Bagus; Firmansyah, Muhammad Reffi; Tamara, Anindya Putri; Zainul, Rahadian
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2127

Abstract

This study delves into the dynamics of agricultural health in Sleman Regency, Yogyakarta Special Region Province, Indonesia, spanning the years 2017 to 2022. By integrating Geographic Information System (GIS) techniques with NDVI-based remote sensing using Landsat imagery from the USGS, our research aims to comprehend the spatio-temporal patterns and transformations in agricultural land health over the study period. The primary objectives encompass understanding these alterations and assessing how urbanization and land utilization impact the well-being of agricultural lands in Sleman Regency. The analytical framework incorporates geospatial processing using QGIS to classify and visualize vegetation health changes, enabling spatially explicit interpretation of land degradation trends. Throughout the 2017–2022 analysis period, a concerning and consistent decline in healthy agricultural lands was observed. By 2022, only 4581.56 hectares of agricultural land remained in a healthy state, constituting a mere 0.011% of the total region, while the expanse of unhealthy land surged from 1109.48 hectares in 2017 to 1160.8 hectares in 2022. This shift underscores a distressing deterioration in the health of agricultural plants due to diminished agricultural land. The geospatial analysis reveals a notable encroachment pattern from urban expansion zones into previously fertile areas, highlighting the urgency for integrated spatial planning. To counter this trend, proactive protection and effective regulation of designated agricultural zones by the Sleman Regency Government are imperative to ensure sustainable cultivation of essential food crops within the region and maintain the overall well-being of the agricultural landscape. The study contributes to advancing GIS-based land monitoring approaches and offers actionable insights for sustainable land use policy formulation in rapidly urbanizing regions. Strengthening policies for sustainable urban development in harmony with agricultural interests is pivotal to securing prosperous and balanced socio-economic growth.
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.
Chest X-Ray Images Clustering using Convolutional Autoencoder for Lung Disease Detection Syafira, Putri Amanda; Yudistira, Novanto; Kurnianingtyas, Diva
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.9.2.2478

Abstract

In healthcare, medical imaging is commonly used for health assessments. One of the most commonly used types of medical imaging is X-ray imaging. One area that often undergoes examination using this modality is the lungs, where healthcare professionals use X-ray images to interpret the results. However, prolonged interpretation of X-ray results by healthcare professionals and other work activities can lead to errors and potentially result in invalid disease identification. There is a need for a system that can classify the detection results from these images to assist healthcare professionals in their tasks. Various methods can be used for this purpose, such as classification, clustering, segmentation, etc. However, data labeling requires significant resources and costs, especially with large-scale datasets. One possible solution is to use an unsupervised learning approach to address this. One method under unsupervised learning is clustering, which allows the system to process and understand data patterns without needing external annotations or manual labeling. This research uses an autoencoder as a subcategory of unsupervised learning. This is because autoencoders can automatically extract relevant features from the data without needing external label guidance. The research utilizes a dataset consisting of 700 X-ray images of the chest, including 500 images showing disease and 200 normal X-ray images. This research aims to determine the effectiveness of clustering methods using an autoencoder model in grouping X-ray image results. The research conducted two experiments. In the first experiment, an autoencoder with 18 Layers was used, resulting in the best performance with a value of K=15 and a rand index of 76%. In the second experiment, an autoencoder with a reduced number of Layers (11 Layers) was used, and it achieved the best performance with a value of K=15 and a rand index of 87%.

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