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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 52 Documents
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Search results for , issue "Vol 9, No 1 (2025)" : 52 Documents clear
Multi-Objective k-Nearest Neighbor for Breast Cancer Detection Nataliani, Yessica; Arthur, Christian; Wellem, Theophilus; Hartomo, Kristoko Dwi; Wahab, Nur Haliza Abdul
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.9.1.2669

Abstract

Early detection of cancer is crucial. This study aims to increase the efficiency of breast cancer detection using the modified k-nearest neighbor (k-NN) algorithm. Since k-NN faces challenges with sensitivity to k values and computational complexity, a modification of k-NN was proposed, namely a multi-objective k-NN model. It was developed to incorporate multi-objective optimization and local density to create a more robust and efficient classification algorithm. The model dynamically determines the k value based on the sample density, optimizing accuracy and efficiency. Breast cancer data were collected from the University of Wisconsin Hospitals, Madison. The experimental results showed that the multi-objective k-NN model outperformed traditional k-NN and k-NN with feedback support. The proposed model achieved an accuracy of 93.7%, with precision values of 93% for the negative cancer class and 94% for the positive cancer class. These results indicate that the multi-objective k-NN model provides superior accuracy and precision in breast cancer detection, demonstrating its potential for clinical applications.
Enhancing Vision-Based Vehicle Detection and Counting Systems with the Darknet Algorithm and CNN Model Rangkuti, Abdul Haris; Athala, Varyl Hasbi
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.9.1.2550

Abstract

This study focuses on developing an algorithm that accurately calculates the volume of vehicles passing through a busy crossroads in Indonesia using object recognition. The high density of vehicles and their proximity often pose a challenge when distinguishing between vehicle types using a camera. Therefore, the proposed algorithm is designed to assign a unique identity (ID) to each vehicle and other objects, such as pedestrians, ensuring that volume calculations are not repeated. The objective is to provide an equitable comparison of road density and the total number of detected vehicles, enabling the determination of whether the road is crowded. To accomplish this, the algorithm incorporates the Non-Max Suppression function, which displays bounding boxes around objects with confidence values and counts the objects within each box. Even when objects are nearby, the algorithm tracks them effectively, thanks to the support of the Darknet Algorithm. The main capabilities of this algorithm for improving vehicle detection include enhanced accuracy, speed, and generalization ability. Typically, it is used in conjunction with the You Only Look Once (YOLO) object detection framework. Five convolutional neural network models are tested to assess the algorithm's accuracy: YOLOv3, YOLOv4, CrResNext50, DenseNet201-YOLOv4, and YOLOv7-tiny. The training process utilizes the Darknet Algorithm. The best-performing models, YOLOv3 and YOLOv4, achieve exceptional accuracy and F1 scores of up to 99%. They are followed by CrResNext50 and DenseNet201-YOLOv4, which achieve accuracy rates of 92% and 98% and F1 scores of 94% and 98%, respectively. The YOLOv7-tiny model achieves an accuracy rate and F1 score of 86% and 88%, respectively. Overall, the results demonstrate the algorithm's success in accurately detecting and calculating the volume of vehicles and other objects in a busy intersection. This makes it a valuable tool for regional government decision-making.
Automatic Feature Extraction of Marble Fleck in Digital Beef Images to Support Decision Preferences Pranata, Feriantano Sundang; Adif, Anjjani Mardhika; Na'am, Jufriadif
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.9.1.2813

Abstract

Beef is one of the essential food ingredients to meet human nutritional needs. These nutrients are fundamental to the growth and development of the human body. The primary nutrient found in beef is protein. The nutritional value of protein in beef can be observed by the quality of the beef itself. An indicator of the protein level is the amount of marbling or white streaks in the meat. Marbling is characterized by a marble-like pattern in the meat layers. This study aims to process beef images to automatically identify marbling. The data processed is secondary data obtained from Kaggle.com, consisting of 60 images with a resolution of 800 by 800 pixels. This study develops a highly subjective method to produce fast and accurate classification. The processing stages used are pre-processing, segmentation, and extraction. The automatic stage is in the extraction, by developing a filtering algorithm. The results of this study can identify the marbling fleck ratio of each beef image very well, where each beef image has marbling flecks. The area of marbling flecks varies greatly depending on the quality of the meat, with the lowest quality having a ratio of 1.0% and the highest being 71.39%. This ratio level becomes an indicator in determining the quality of the meat, which is the primary preference in making accurate decisions in selecting meat quality. Thus, this study can serve as an indicator in determining the appropriate meat preference choice.
Development of Quantum Physics Laboratory Based on Immersive Virtual Reality Mufit, Fatni; Dhanil, Muhammad; Hendriyani, Yeka
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.9.1.2244

Abstract

This study aims to develop a VR-based quantum physics laboratory to visualize quantum physics phenomena and support more interactive and efficient learning. The research method follows the stages of needs analysis, design and development, and implementation. The needs analysis stage involved 97 students to gather information about the challenges in quantum physics learning.The needs analysis results showed that quantum physics was a difficult subject to understand due to its abstract nature, and there was a need for media that could help students understand the material through simulations and experiments in a virtual environment. The design and development stage produced scenarios and storyboards encompassing all the necessary elements for VR implementation. During the implementation stage, the design was realized in the form of a VR laboratory presenting various quantum physics experiments, including black body radiation, the Compton effect, the photoelectric effect, and X-ray production. The results of the implementation of the use of VR through validity testing and practicality tests obtained a score of 0.92 in the valid category and 92.25% in the very practical category. This research contributes to supporting the availability of quantum physics experimental equipment in schools and makes it easier for students to understand abstract quantum physics concepts through interesting virtual interactivity. The VR-based quantum physics laboratory was successfully developed as an innovative solution for quantum physics learning. Future research suggests that VR can be developed for other learning areas and that further studies explore the effects of VR on skills and health.
Academic Performance Prediction Using Supervised Learning Algorithms in University Admission Gufroni, Acep Irham; Purwanto, Purwanto; Farikhin, Farikhin
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.9.1.2974

Abstract

Each educational institution has designed an academic system with the aim of providing as perfect learning as possible to students. The quality of good students is influenced by various factors, one of which is the available academic system. Previous research has shown that the quality of a student, which can be called academic achievement, can be determined through historical data on the student admission process. This research aims to process one of the admission processes previously implemented in Indonesian state universities using the National Selection for State University Entrance (SNMPTN) data, combined with Cumulative Achievement Index (GPA) data, so that it can be processed using a machine learning model. The algorithm used to create the model is a Supervised Learning Classification algorithm, which includes a Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB). The research was carried out in three schemes based on the percentages of training data and test data. The results obtained show that DT produces the highest accuracy and precision values, with an accuracy value of 0.79 and a precision value of 0.56, respectively. The XGB produces the highest recall and f1-score values, with a recall value of 0.35 and an f1-score value of 0.36. The model with the highest f1-score can be selected as the best model, namely, the model with the XGB algorithm on a 70%-30% train-test data scheme. The resulting model achieved a success rate of 77%.
Analyzing Perceptions of Maternal and Pediatric Care in Jakarta: An Integrated VADER and GloVe Analysis of Google Reviews in Mother and Child Hospitals Al Qarana, Gilang; Rianto, Leonov; Charles, Charles; Purnomo, Lorio
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.9.1.2551

Abstract

In the rapidly digitizing landscape of healthcare feedback, online reviews have become a vital source of patient-reported experiences. This study leverages sentiment analysis to decode the narrative content of Google reviews for Mother and Child Hospitals in Jakarta. Utilizing the VADER sentiment analysis tool and GloVe for keyword extraction, the research aimed to correlate qualitative sentiment with quantitative star ratings. This study meticulously processed and analyzed a selection of Google reviews using VADER for sentiment scoring and GloVe for refining the focus on relevant healthcare discussions. This methodological approach allowed for a comprehensive sentiment assessment of the reviews. The analysis revealed a prevalent positive sentiment in higher-rated reviews and negative sentiment in lower-rated reviews, with notable anomalies that underscore the complexity of patient experiences and perceptions. Specific aspects of care, including staff behavior, facility quality, and treatment efficacy, were recurrent themes in the feedback. These findings highlight the potential of patient-reported experiences in shaping healthcare practices and policy. The study emphasizes the importance of healthcare providers understanding and responding to patient feedback to improve care quality. Limitations such as the representativeness of online reviews and the challenges of sentiment analysis in capturing nuanced emotions are discussed. This study offers valuable insights into patient perceptions of maternal and pediatric care in Jakarta, affirming the significance of leveraging online reviews for healthcare quality monitoring and improvement
Classification of Diabetic Retinopathy Based on Fundus Image Using InceptionV3 Minarno, Agus Eko; Bagaskara, Andhika Dwija; Bimantoro, Fitri; Suharso, Wildan
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.9.1.2155

Abstract

Diabetic Retinopathy (DR) is a progressive eye condition that can lead to blindness, particularly affecting individuals with diabetes. It is commonly diagnosed through the examination of digital retinal images, with fundus photography being recognized as a reliable method for identifying abnormalities in the retina of diabetic patients. However, manual diagnosis based on these images is time-consuming and labor-intensive, necessitating the development of automated systems to enhance both accuracy and efficiency. Recent advancements in machine learning, particularly image classification systems, provide a promising avenue for streamlining the diagnostic process. This study aims to classify DR using Convolutional Neural Networks (CNN), explicitly employing the InceptionV3 architecture to optimize performance. This research also explores the impact of different preprocessing and data augmentation techniques on classification accuracy, focusing on the APTOS 2019 Blindness Detection dataset. Data preprocessing and augmentation are crucial steps in deep learning to enhance model generalization and mitigate overfitting. The study uses preprocessing and data augmentation to train the InceptionV3 model. Results indicate that the model achieves 86.5% accuracy on training data and 82.73% accuracy on test data, significantly improving performance compared to models trained without data augmentation. Additionally, the findings demonstrate that the absence of data augmentation leads to overfitting, as evidenced by performance graphs that show a marked decline in test accuracy relative to training accuracy. This research highlights the importance of tailored preprocessing and augmentation techniques in improving CNN models' robustness and predictive capability for DR detection. 
Enhancing Motoric Impulsivity Detection in Children through Deep Learning and Body Keypoint Recognition Dalimarta, Fahmy F.; Andono, Pulung N.; Soeleman, Moch. A.; Hasibuan, Zainal A.
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.9.1.2779

Abstract

Quantifying motoric impulsivity in pediatric settings is crucial for safeguarding children and for devising effective intervention strategies. Existing quantitative techniques, such as accelerometry, have been utilized to assess it, but they often prove insufficient for accurately differentiating impulsive movements from regular ones. Conventional assessment methods are frequently used and rely on subjective assessments, which hinders the accurate characterization of impulsive behavior. To address this research gap, our study introduced an innovative objective approach using computer vision and deep learning techniques. We utilized MediaPipe to track precise body movement data from a child. The data were then analyzed using a Bidirectional Long Short-Term Memory (Bi-LSTM) network to process sequential information and recognize patterns indicative of impulsivity. Our approach successfully distinguished impulsive movements, marked by rapid changes in position and inconsistent movement velocities, from typical behavioral patterns with an accuracy rate of 98.21%. This research demonstrates the effectiveness of combining computer vision and deep learning to measure motoric impulsivity more precisely and impartially than prevailing qualitative techniques. Our model quantifies behaviors, enabling the development of improved safety protocols and targeted interventions in educational and recreational settings. This research has broader implications, suggesting a framework for future studies on pediatric motion analysis and behavioral assessment.
Development of Intelligent Marine Logistics Models Using Machine Learning Nguyen, Minh Duc; Nguyen, P. Quy Phong; Luong Ha, Chuc Quynh; Dinh, Xuan Manh; Cao, Van Sam; Dat Do, Hoang
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.9.1.3666

Abstract

This study looks into the development of intelligent maritime logistics models that use machine learning approaches to forecast crucial metrics like fuel usage and port delays. A comprehensive dataset assessed five machine learning models: Linear Regression, Decision Tree, Random Forest, XGBoost, and AdaBoost. Predictive capacities were assessed using key performance measures such as R², MSE, and MAPE. The results show significant heterogeneity in model performance, with Linear Regression attaining a modest test R² of 0.6845 for fuel prediction and 0.8831 for port delay prediction but suffering from high MSE (58745.23) and MAPE (26.90% for fuel). The Decision Tree showed significant overfitting, with a perfect R² (1.000) on training but decreasing to 0.7743 for fuel and 0.9880 for port delay on testing. Random Forest demonstrated balanced performance, with test R² values of 0.7598 for fuel and 0.9548 for port delay. MAPE values were also lower (23.66% for fuel and 5.66% for port delay). The best-performing model was XGBoost, with near-perfect test R² values of 0.7439 for fuel and 0.9880 for port delay, as well as a low MSE (39579.79 for fuel and 0.23 for port delay). AdaBoost produced comparable but somewhat lower results, with test R² values of 0.7188 for fuel and 0.9485 for port delay. These findings demonstrate XGBoost's strength in capturing nonlinear interactions and making solid predictions, whereas ensemble approaches outperform simpler models such as Linear Regression.
Understanding Search Behavior in the Simulated Kalman Filter Algorithm Abdul Aziz, Nor Hidayati; Jing Hao, Ooi
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.9.1.3538

Abstract

In computational optimization, metaheuristic algorithms are crucial for solving complex and dynamic problems. It is important to fully understand how an algorithm searches, as it helps to improve the algorithm and its applications in various domains. This paper provides a detailed analysis of how the Simulated Kalman Filter algorithm searches for optimal solutions. The SKF algorithm is an optimization method inspired by the Kalman filter estimation techniques. The algorithm was introduced in 2015 to address unimodal problems. Since its inception, the SKF algorithm has undergone improvements and is used to solve a range of optimization problems. Our study aims to bridge the gap in existing research by investigating how SKF effectively balances the search space exploration and known solution exploitation. Through systematic experimentation using the Brown function as a benchmark, we explored the social dynamics and movement style of the SKF algorithm, in addition to the convergence efficiency and accuracy. When we applied the same approach as suggested in the referenced paper, we gained insights into SKF’s unique strengths and limitations of SKF when compared to other algorithms. The findings illustrate SKF’s unique capabilities in handling the exploration-exploitation trade-off. This study helps to set the foundation for creating more advanced algorithms and optimization strategies in the future. Future research will examine how enhancements to the SKF algorithm impact and enhance its search behavior.

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