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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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Named Entity Recognition in Medical Domain: A systematic Literature Review Kusuma, Selvia Ferdiana; Wibowo, Prasetyo; Abdillah, Abid Famasya; Basuki, Setio
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.8.4.3111

Abstract

Biomedical Named Entity Recognition (BioNER) is essential to bioinformatics because it identifies and classifies biological entities in biomedical texts. With the increasing number of biomedical literature and the rapid progress of the BioNER approach, it is essential to conduct a systematic literature review (SLR) on BioNER. This SLR consolidates existing information and provides directions for future studies in the BioNER field. This review systematically explores scientific journals and conferences published from 2019 to 2024. This research uses PubMed and Scholar as reference search databases because of their affiliation with other well-known publishers such as IEEE, Elsevier, and Springer. The results show a transition from conventional machine learning to deep learning. Neural networks and transformers show better performance in deep learning methods. The datasets often used in BioNER development are BC2GM, BC5CDR, and NCBI-Disease. Precision, Recall, and F1-Score are used in most papers to evaluate model performance. The performance of these models mostly depends on the availability of big annotated datasets and significant computational tools. Therefore, it is vital for future research to address the issues of annotated data and resource availability to build accurate models. Researchers should investigate the creation of ideal designs that lower computing complexity without compromising performance. Overall, this SLR offers a thorough overview of the latest research on BioNER. It provides significant insights for academics and practitioners in bioinformatics and medical research, helping them understand the innovative aspects of BioNER research.
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%.
The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification Rahmi, Nadya Alinda; Defit, Sarjon; Okfalisa, -
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.8.4.3092

Abstract

This research aims to investigate the effectiveness of hyperparameter tuning, particularly using Optuna, in enhancing the classification performance of machine learning models on scientific work reviews. The study focuses on automating the classification of academic papers into eight distinct fields: decision support systems, information technology, data science, technology education, artificial intelligence, expert systems, image processing, and information systems. The research dataset comprises reviews of scientific papers ranging from 150 to 500 words, collected from the repository of Universitas Putra Indonesia YPTK Padang. The classification process involved the application of the TF-IDF method for feature extraction, followed using various machine learning algorithms including SVM, MNB, KNN, and RF, with and without the integration of SMOTE for data balancing and Optuna for hyperparameter optimization. The results show that combining SMOTE with Optuna significantly improves the accuracy, precision, recall, and F1-score of the models, with the SVM algorithm achieving the highest accuracy at 90%. Additionally, the research explored the effectiveness of ensemble methods, revealing that hard voting combined with SMOTE and Optuna provided substantial improvements in classification performance. These findings underscore the importance of hyperparameter tuning and data balancing in optimizing machine learning models for text classification tasks. The implications of this research are broad, suggesting that the methodologies developed can be applied to various text classification tasks in different domains. Future research should consider exploring other hyperparameter tuning techniques and ensemble methods to further enhance model performance across diverse datasets.
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. 
A Theoretical Framework of Knowledge Management Systems on Quality Management Systems Nizam Mohammad, Mohammad Fakhrul; Abdullah, Rusli; Ab. Jabar, Marzanah; Haizan Nor, Rozi Nor; Mohd Nur, Nurhayati
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.8.4.3633

Abstract

Developing knowledge management systems (KMS) significantly supports, manages, or enhances the organization's knowledge management (KM) processes and activities. However, studies have shown that very few discussions focus on formulating the theoretical framework to support the development of KMS. Therefore, this paper aims to fill the void and gap in developing a more effective and efficient KMS. This study proposes a theoretical framework for KMS in the context of quality management systems (QMS), formulated based on three domain areas: established theories, knowledge management (KM) framework or model, and past KMS. This research output derived seven components of KMS (strategy, actors, KM process, source of knowledge, information management, continuous improvement, and infrastructure). This study has also contributed to the body of knowledge in KM by enriching the formulation of a theoretical framework for KMS. Although this study is conceptual and has yet to include the framework's validity and reliability testing, it has addressed a gap that can be potentially fulfilled and refined with more intense discussion and empirical studies.
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.
A Systematic Literature Review on Characteristics Influencing Software Reliability Subramanium, Lehka; Hassan, Saadah; Osman, Mohd. Hafeez; Zulzalil, Hazura
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.8.4.3665

Abstract

Reliability, as a non-functional requirement, is a crucial aspect that refers to the system's ability to perform its intended functions consistently and without failure over an extended period. It is essential in designing and implementing software systems, as it affects software quality. Maintaining software reliability is a significant challenge, as it is directly impacted by factors such as the complexity of the software design, the amount of code, and the measures taken to secure the system from unauthorized use. There are significant growing appeals for predicting reliability to account for risks. Research on reliability risk assessment has a long tradition; unfortunately, comprehensible reliability characteristics are still vague when determining potential risks. Clearly defining, prioritizing, and addressing reliability characteristics is essential for delivering reliable, high-quality software that meets user needs and business goals. The ignorance and lack of comprehensive reliability characteristics have evolved into inaccurate risk assessment, triggering malfunctions in the operational environment. Comprehensive characteristics are key elements to predict and estimate software reliability. The reliability characteristics could determine the precise objective of reliability efforts. This systematic literature review aims to identify the key characteristics influencing software reliability, the potential risks associated with these characteristics, and the metrics used to measure and assess them. Thirty-one research articles related to research questions have been reviewed. The findings indicate that comprehensive reliability characteristics could identify, classify, and prioritize potential risks, improving current metrics. It can be concluded that the accurate potential reliability risk can demonstrate the consequence of failure.
Metaheuristic Optimization in Dynamic Unit Commitment of Power Systems: Current Trends and Future Prospects Widayanti, Lilis; Afandi, Arif Nur; Herwanto, Heru Wahyu; Fitria, Vivi Aida
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.8.4.2423

Abstract

This paper discusses various optimization algorithms, a mathematics and computer science method essential to solving challenging real-world problems. The presented real-world scenario centers on a power system with dynamic unit commitment. Power systems involve many variables and constraints that can be changed. Metaheuristic optimization can be an effective method for finding an excellent solution to a problem. Dynamic unit commitment optimization is a critical aspect of power system operation. Dynamic unit commitment discusses determining the operating schedule of generating units and how much their production capacity is at each specific time interval to minimize production costs. This research aims to investigate the current trends and future challenges in applying metaheuristic optimization algorithms to power system problems, using the PRISMA approach to conduct a systematic literature review (SLR). This research starts by formulating research questions: what metaheuristic methods can be employed to tackle dynamic unit commitment challenges in power systems, and how effective are these methods in optimization? We established specific criteria for including and excluding studies, such as choosing articles published in English and concentrating on unit commitment in power systems. Out of the 487 papers discovered from the abstracts, only 14 were chosen for review after an extensive screening process based on complete studies that specifically addressed dynamic unit commitment. The conducted analysis provided insights into the accuracy and efficiency levels attained when using metaheuristic algorithms to solve the dynamic unit commitment problem. The research underscores the pivotal role of metaheuristic optimization in modern energy and power systems, highlighting its significance in tackling diverse challenges associated with enhancing the efficiency and effectiveness of power system utilization. Using metaheuristics in academic research has significantly improved scientific publications, resulting in an average annual increase of 43% in citations. This research's real contribution to the academic field is to provide an alternative reference for approximately 66 ASEAN universities with power system engineering majors. 

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