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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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Improved Fuzzy Possibilistic C-Means using Artificial Bee Colony for Clustering New Student’s Financial Capability to Determine Tuition Level Satriyanto, Edi; Surya Wardhani, Ni Wayan; Anam, Syaiful; Mahmudy, Wayan Firdaus
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.8.4.3087

Abstract

Outliers in the dataset will affect the quality of the cluster, so a good clustering method is needed. Based on the Mahalanobis distance method, it is known that the research dataset has outliers. Clustering methods that are often used for this type of data are Fuzzy C-means (FCM), Possibilistic C-means (PCM), and Fuzzy Possibilistic C-means (FPCM). This study aims to develop a clustering method that is more robust to outliers by using the Artificial Bee Colony (ABC) algorithm to minimize the objective function of FPCM. This study produces a new algorithm called Artificial Bee Colony Fuzzy Possibilistic C-Means (ABCFPCM) so that the resulting clusters are not easily trapped in the local optimum. This study also provides cluster centroid initialization using K-Means++ to improve cluster quality. ABCFPCM performs best because it significantly increases the Silhouette value and the Between Sum Squares (BSS) and Total Sum Squares (TSS) ratio. ABCFPCM performance provides the best cluster quality of 72.16% based on the BSS/TSS ratio, FPCM of 70.71%, and FCM K-Means++ of 68.14%. K-Means++ in the cluster method does not affect cluster performance except for FCM, where cluster quality is slightly increased. The centroid results of 8 clusters as the best performance of ABCFPCM are used to determine the tuition rate level. The impact of this study is to improve the quality of FPCM performance because it is no longer trapped in a local optimum at the cluster centroid.
Classification of Arabic Documents with Five Classifier Models Using Machine Learning Najjar, Esraa; A. Alkhaykanee, Nibras.; Breesam, Aqeel Majeed
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.9.1.2539

Abstract

Automated document classification is becoming important and highly required for many applications, particularly in light of the exponential increase of Arabic-language internet documents. Text classification is a big data issue and an essential aspect of our lives; classifying content in a typical Arabic text is a significant and arduous challenge. The process of classifying a document involves placing it in the appropriate class or category. The major goal of this work is to use pre-processing techniques to evaluate the effectiveness of machine learning (ML) algorithms. The inclusion of preprocessing in this research methodology is vital. This study uses machine learning methods to classify different Arabic documents and uses five well-known classification systems' performance in categorizing the documents. This work used a model developed using various algorithms, namely Support Vector Machines, Naive Bayes, Logistic Regression, K-Nearest Neighbors, and Random Forest, for the classification procedures. The findings indicate that SVM achieved the highest performance evaluation, boasting an accuracy of 98%, surpassing all other algorithms employed in this study.
Canva-based Animation Comic Video Media in Informatics Learning at SMP Negeri 14 Padang Huda, Asrul; Sari, Liza Mustika; Effendi, Hansi; Giatman, Muhammad; Firdaus, -; Sukmawati, Murni
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.9.1.3177

Abstract

This research addresses challenges in conventional learning media, limited facilities, and suboptimal use of technology, which hinder student material mastery, motivation, and independence. The study aims to develop and validate Canva-based animated comic video learning media for Informatics subjects in class VIII at SMP N 14 Padang. Using the Research and Development (R&D) method with a 4D development model—define, design, develop, disseminate—primary data were collected from validators, teachers, and students. Descriptive and inferential analyses were employed to evaluate the validity and practicality of the media. The learning media achieved high validity scores: 0.963 for media design and 0.975 for material content. Expert evaluations highlighted the media’s effective visual design, systematic content presentation, and alignment with curriculum objectives. Practicality was confirmed with average scores of 97.04% from teachers and 93.14% from students, who appreciated its ease of use, accessibility across devices, and engaging, interactive features that support both independent and collaborative learning. This study underscores the importance of integrating technology into learning media to enhance education quality. Canva-based animated comic videos are not only applicable to Informatics but also have potential for adaptation to other subjects. The combination of visual, audio, and interactive elements fosters engaging, flexible, and impactful learning experiences for students. Future research could explore AI integration for personalized learning and broader testing across diverse student groups and subjects. This research provides a foundation for developing innovative, accessible, and inclusive technology-based learning tools to improve education quality in the digital era.
Disease Classification by SVM and GBC Algorithms AL Kafaf, Dhrgam; Thamir, Noor N
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.9.1.2557

Abstract

Artificial intelligence (AI) application in disease classification is a rapidly growing area of interest for medical practitioners in diagnosing illnesses. This work provides a comprehensive study on the application of AI, particularly Machine Learning (ML) algorithms, for predicting diseases based on symptoms in healthcare. The research wants to improve the diagnosis of illnesses using symptom data by utilizing two popular ML algorithms, the Support Vector Machine (SVM) and the Gradient Boosting Classifier (GBC). The research utilizes a dataset containing 4,921 items, split into 80% for training and 20% for testing. The methodology section includes information on the procedures for collecting and preparing data, such as importing data, handling missing values, categorizing symptom severity, and dividing the data. Subsequently, a range of measurement performances such as F1 score, accuracy, precision, and recall are utilized to evaluate the model technology's effectiveness. The default hyperparameters of the GBC model are used for evaluation, while the SVM model is optimized through parameter adjustments using GridSearchCV.  The effectiveness of the GBC model is evaluated utilizing similar metrics, while the SVM model demonstrates high accuracy across different hyperparameter configurations. The research suggests that ML algorithms have the potential to enhance the precision of predicting illnesses, and it also considers the significance of these discoveries within the broader scope of AI in healthcare. The research sets the stage for potential explorations in this field, emphasizing the importance of continual research and enhancement of AI techniques to enhance healthcare outcomes.
Simulation of Land Use and Land Cover of Peatland Bengkalis Using QGIS Fauziah, Fauziah; Hayati, Nur; Prasetyo, Lilik Budi
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.9.1.2432

Abstract

The phenomenon of forest and peatland fires in Bengkalis Regency is inseparable from the change in land use and cover (LULC). The dynamic LULC in Bengkalis Regency is caused by economic factors sourced from land-based resource management. As a result, negative impacts such as environmental damage can trigger fires. Therefore, this study attempts to observe the LULC patterns on peatlands in the Bengkalis Regency using overlay techniques using QGIS. QGIS functions unlock the software's full potential, empowering you to manipulate data, automate workflows, create custom expressions, and perform advanced spatial analysis—all within a single platform. There are 12 LULC that can be identified on peatlands in Bengkalis Regency, including plantations (42.98%), primary forests (42.68%), shrubs (12.29%), residential and activity areas (0.71%), fields/farmlands (0.64%), lakes/ponds (0.43%), empty/bare land (0.18%), rivers (0.05%), and ponds, ponds, mangrove forests, and rice fields ranging from 0.004% to 0.008%. In addition, in the Bengkalis Regency, concession areas of at least 175,081.19 Ha are in the Peatland Ecosystem Protection Function (FLEG). LULC simulation provides a powerful tool for assessing the potential impact of various development plans and policies on society, the economy, and the environment, enabling more sustainable and responsible choices. A comprehensive understanding of land use and land-cover patterns is essential for further research on sustainable resource management and climate change mitigation. While LULC research has advanced significantly, several critical questions require further investigation
Mixed Learning Models and IoT Devices: Effectively Increasing Competence and Training Independent Learning Students in Unnormal Situations Purba, Ramen Antonov; Simarmata, Janner; Limbong, Tonni; Damanik, Romanus
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.8.4.2553

Abstract

Abnormal situations often occur, such as natural disasters and COVID-19. Educational institutions struggle to regulate learning. The web programming course aims to shape students into website programmers. Independence in learning is needed so that competence is obtained. Students are not enough to rely on learning from the lecturer. This study aims to analyze the combination of the Inquiry-Based Learning model with IoT devices based on Android mobile. As a supporter, an application is built with a mobile programming language. This type of research is quasi-experimental. Calculations using SPSS 23.0. An experimental class learns to use the Inquiry-Based Learning model with IoT devices, and a control class learns with various media. The research subjects were 60 students of Information Management. The study found differences in students' competence and learning independence in those who learned to use the inquiry-based learning model with IoT devices compared to those who studied with various media. The test results showed a higher increase in the experimental class. The experimental class's value is 14.40 for a gain of 7.5. The sig. value is  .000, and the average gain is .83. Control class score is 11.87, an increase of 5.1, sig. value is .000, and the average gain is .53. Applying the inquiry-based learning model with IoT devices has also proven to be effective as a model and learning media in abnormal situations and reinforced by the average gain of the experimental class, which is greater than the control class. Future research could use different methods to determine what methods are most effective.
Exploring the Capabilities of GPT Models in Drafting Course Assessments Based on Bloom’s Taxonomy Muhamad, Gilang Aulia; Alsulami, Bassma Saleh; Thabit, Khalid Omar
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.9.1.2811

Abstract

The application of Generative Pre-trained Transformer (GPT) models is significantly essential in automating drafting course assessment based on Bloom’s Taxonomy, specifically GPT-3.5-turbo, GPT-4, and GPT-4o. Therefore, this study aimed to explore the interaction between Artificial Intelligence (AI) models and educational content using refined prompt engineering methods to enhance the accuracy and relevance of the generated questions. For the investigation, the processing 146 Course Learning Outcomes (CLOs) method was applied through each model using OpenAI Application Programming Interface (API). Metrics such as 'Accuracy', 'Precision', 'Recall', and 'F1 Score' were used to assess the performance of each model. The results showed that GPT-4 was suitable for complex course assessments, showing superior performance in delivering detailed and precise responses. A cost-effective solution was obtained using GPT-3.5-turbo for generating simpler course assessment, while GPT-4o provided a middle ground, balancing cost, and performance. The results showed the potential of AI to reduce the administrative burden on instructors by streamlining the creation and refinement of course assessments. The enhancement of course assessments was also facilitated by automation, thereby supporting more adaptive questions. The potential for broader AI integration into educational practices promised a transformative impact on traditional course assessment drafting methods, enabling more dynamic and educational experiences. Moreover, further studies were recommended to explore the ethical dimensions of AI in education, the ability to handle diverse tasks, as well as assess the long-term impacts on learning outcomes and educational equity.
Implementation of Virtual Reality Moot Court for Simulation and Procedural Law Learning of the Constitutional Court Hidayah, Nur Putri; Wicaksono, Galih Wasis; Perdana, Muhammad Ilham; Faiz, Ahmad; Cholidah, -
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.8.4.3125

Abstract

The limited space for moot court simulations in law learning is one of the main obstacles. In the Constitutional Court's judicial practice, no faculty has a Moot courtroom identical to the actual courtroom. Every law student must be able to practice trial to improve their argumentation, advocacy, legal reasoning, and other problem-solving skills. This research aims to build and develop a Virtual Reality (VR) Moot Court that can be used as a Moot Court in the trial of the Constitutional Court. VR Moot Court is a means of practicum in the constitutional procedure law course. This research was carried out through scenario preparation and system design stages, followed by 3D asset optimization, user interaction design, multi-user design, and testing. This research utilizes Unity to build 3D assets and Spatial.io as a VR platform. For more immersive use, users can use VR headsets such as Oculus. However, VR Moot Court can also be accessed via smartphone or PC for broader use. The development of VR Moot Court is quite complex, requiring the optimization of assets used across various devices. This study optimizes poly, texture, material, and lighting. The results of VR Moot Court development in this study tested the system's functionality and measured the optimization results. The results of system optimization tests have shown a decrease in GPU and CPU usage. Meanwhile, the results of the functionality and user satisfaction tests also show that VR Moot Court, in addition to taking course learning outcomes in the constitutional court's procedural law course, this system is also relevant to the actual Constitutional Court courtroom. This research in the future requires the development of a type of moot courtroom for other kinds of courts.
GLCM and PSNR Analysis of Woven Fabric Images Made from Natural Dyes Due to Sunlight Exposure Batarius, Patrisius; Santoso, Albertus Joko; Sinlae, Alfry Aristo Jansen
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.8.4.2265

Abstract

Traditional woven fabrics generally use natural dyes that come from the local area. Natural dyes are often considered low quality if exposed to sunlight. This study aims to analyze the effect of sunlight on the image of woven fabrics made from natural dyes. The natural dyes used come from noni (Morinda citrofolia L), which produces a red color; Tarum (Indigofera tinctoria L), which produces a blue-black color; and corn starch juice, which produces a white color. A thread made of cotton is dipped and cooked to produce the desired color. The analysis is done by comparing the value of GLCM (Grey Lever Co-Coruent Matrix) features, changes in the value of Mean Square Error (MSE), and Peak Signal Noise Ratio (PSNR) with the original image. The original image is taken before the woven fabric is dried in the sun. The changing image is taken after the woven fabric is dried in the sun with variations in drying times. The drying time of woven fabric is 1 hour. Sun drying starts from 09:00 to 14:00. The distance between the original and sun-dried images is 30 cm. The original image and the sun-dried image went through cropping and resizing the image to be the same size. The grayscale image type is used for the GLCM, MSE, and PSNR comparison process. The image size used is 128x128 for woven fabric images with three kinds of colors (white, red, and blue) and 256x256 pixels for images with white color. The results showed that the quality of the images produced at drying hours of 09.00-10.00 to 14.00-15.00 tended to be low, with a significant difference between the original image and the changed image. The lowest point of quality lies in the drying time of 12.00-13.00 and 13.00-14.00. For the GLCM features, the sun-dried image at 14.00-15.00 has a homogeneity value close to the original value. For contrast features, the image dried at 10.00-11.00 has a contrast value that is close to the original image contrast value. This shows the smaller the difference in pixel intensity in the image.
Visualization of Accounting and Indigenous People Research: A Bibliometric Review Using R Thahirah, Khadijah Ath; Triyuwono, Iwan; Mulawarman, Aji Dedi; Achsin, M.
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.9.2.2878

Abstract

This study aims to map out the evolution of research trends on accounting and Indigenous peoples by using bibliometric analysis. Most bibliometric literature articles rely on basic graphical representations generated by computer systems. The methodology for conducting bibliometric analysis presented in this paper consists of three stages, namely data collection, software selection and analysis. This study used published papers from the Scopus database was carried out on 13 June 2024 and found 42 indexed research publications on the topic of accounting and indigenous people between 1999 and 2023. The map of research development in the field of accounting and Indigenous people is obtained through the export process, which was analyzed using the R Biblioshiny application program. The findings demonstrated a development trend with a static increase in the number of publications about accounting and research on Indigenous people. Besides, the results show that the journal with the most publication and impact is the Accounting, Auditing, and Accountability Journal. The country with the most objects of study is Australia. The development of research related to accounting and Indigenous People was growing, although not too massive. Along with these conditions, various trends in   Accounting and Indigenous People Research topics grew. The results of this study also indicate that the most widely used topic keywords are Accounting, Indigenous, People, and Research.  The findings of this study provide scholars with a comprehensive understanding of the current research work in the field of accounting and indigenous people and its future directions.

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