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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 59 Documents
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Search results for , issue "Vol 8, No 4 (2024)" : 59 Documents clear
A Model for Enhancing Pattern Recognition in Clinical Narrative Datasets through Text-Based Feature Selection and SHAP Technique Dalhatu, Sirajo Muhammad; Azmi Murad, Masrah Azrifah
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.8.4.3664

Abstract

Clinical narratives contain crucial patient information for predicting cardiac failure. Accurate and timely cardiac failure recognition (CFR) significantly impacts patient outcomes but faces challenges like limited dataset sizes, feature space sparsity, and underutilization of vital sign data. This study addresses these issues by developing a methodology to improve CFR accuracy and interpretability within clinical narratives. Four datasets—the Framingham Heart Study, Heart Disease from Kaggle, Cleveland Heart Disease, and Heart Failure Clinical Records—undergo preprocessing, including handling missing values, removing duplicates, scaling, encoding categorical variables, and transforming unstructured data using natural language processing (NLP). Various feature selection methods (Chi-Squared, Forward Selection, L1 Regularization) are used to identify influential features for CFR, and the SHapley Additive exPlanations (SHAP) technique is integrated to improve interpretability. Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) models are trained and evaluated. Performance was evaluated using accuracy, precision, recall, f1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results indicate that L1 Regularization with LR and Chi-Squared with RF perform best for specific datasets. The final model, combining all datasets with Forward Selection and RF, achieves high accuracy (91%), precision (87%), recall (97%), f1-score (91%), and AUC-ROC (94%). This study concludes that advanced text-based feature selection and SHAP interpretability significantly enhance CFR model accuracy and transparency, aiding clinical decision-making. Future research should incorporate more diverse datasets, explore advanced NLP techniques, and validate models in various clinical settings to enhance robustness and applicability.
Recipient Feasibility Decision Support System Micro Small Medium Business Assistance Use Method Analytic Hierarchy Process and Simple Additives Weighting Abdullah, Dahlan; Erliana, Cut Ita; Bintoro, Andik; Hartono, Hartono; Ikhwani, Muhammad; Nazaruddin, Nazaruddin
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.8.4.2321

Abstract

Study This aims To determine the eligibility of MSME assistance recipients with the method AHP (Analytic Hierarchy Process) And SAW (Simple Additives weighting). The AHP method is used to determine the weight of each criterion. Meanwhile, SAW is used To determine the rank selection of beneficiaries. This is very important for Indonesia's economy during the crisis, Where MSME's own Power stands to face a crisis economy. Criteria used in a way This uses six measures: type of business, Amount of power Work, turnover per month, amount of assets, sector MSME, And sector business. Decision support systems are designed to support someone who must make certain decisions. That is, interactive, Flexible, Data quality, and Expert Procedure. Study System Supporters Decision Appropriateness Recipient Help Business Micro Community Use Analytic Hierarchy Process (AHP) and Simple Additive Methods weighting (SAW), Study This done in Subdistrict Intersection Three Regency Pidie Aceh Province to facilitate the Selection of Eligibility of Government Assistance Recipients For Build a business Micro Society. Testing is done in this study, namely black box testing. Results Testing black box shows that the system can walk with Good by function, with results calculation method AHP and results calculation method SAW in determining eligibility selection MSME aid recipients. The results of the level of accuracy testing on the AHP and SAW methods with six criteria and alternatives the requirements is 75%.
Issues in Chinese Requirements Specifications: Insights from Survey Data and Static Analysis Jiaying, He; Yap, Ng Keng; Osman, Mohd Hafeez; Hassan, Sa’adah
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.8.4.3667

Abstract

Requirements engineering is crucial for software project success. Issues like requirements ambiguity, inconsistency, and unverifiability contribute to unclear, conflicting, or untestable specifications, which can undermine the effectiveness and success of a software project. These issues have been identified as factors contributing to software project failure. However, there’s limited research on the current state of these issues in China. The research objectives of this study are to address the most commonly used methods for expressing Chinese software requirements and uncover issues related to ambiguity, inconsistency, and unverifiability, which can be solved by using artificial intelligence techniques to investigate possible solutions to these problems. An online survey of 422 software professionals in China identifies key issues in Chinese software requirement expressions that AI techniques can address. The study examines various expression methods, tools for enhancing clarity, and challenges specific to Chinese requirements. Findings reveal that ambiguity, inconsistency, and unverifiability significantly impact project success. While natural language specification and prototyping improve clarity, they may increase the time required for requirements engineering. Effective communication is typically achieved through natural language, prototyping, storyboarding, and pseudo-coding, whereas decision tables and block diagrams are less commonly used and linked to problematic requirements. Using tables, prototype diagrams, and natural language descriptions helps mitigate these issues, though it may extend engineering time. The study suggests strategies to improve the efficiency and quality of requirements expression and highlights the need to develop Chinese boilerplates and refining tools to enhance clarity in the future.
Comparison of Convolutional Neural Networks Transfer Learning Models for Disease Classification of Food Crop Faurina, Ruvita; Rahma, Silvia; Vatresia, Arie; Susanto, Agus
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.8.4.1936

Abstract

Indonesia is an agricultural country with 29% of the workforce working in the agricultural sector, however, farmers' knowledge and practices depend on informal local wisdom based on inherited past practices. Moreover, identifying diseases in plants is difficult to do with human vision so that intelligent technology is needed.  In this paper, an architecture of CNN models such as MobileNetV2, ResNetV50, InceptionV3 and DenseNet121 will be built to detect diseases based on leaf images of several crops obtained from the agroai dataset containing multiple crops namely bean, chili, corn, potato, tomato and tea. The model is used through transfer learning for feature extraction of the trained model with imagenet weights, with 4 fully connected layers. Each model for each crop will be compared to get the best model based on the accuracy of training, evaluation and testing. ResNet50 has the best performance for four type of plants, including bean plants with training accuracy of 99.49%, validation of 99.52%, testing of 98.96%, chili plants with training accuracy of 98.03%, evaluation of 98.75%, testing of 100%, tea plants with training accuracy of 99.62%, evaluation of 99.6%, testing of 99.74% and tomato plants with training accuracy of 99.62%, validation of 99.7%, testing of 99.37%. Moreover, MobileNetV3 has the best performance for 2 types of crops that is corn with training accuracy of 99.22%, validation of 99.69%, testing of 99.55%, and potato with training accuracy of 99.62%, evaluation of 99.60%, testing of 99.74%.
Involvement of Various Selection Methods for Genetic Algorithms in Determining the Optimal Production Schedule Problem Muliono, Rizki; Silviana, Nukhe Andri; Novita, Nanda
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.8.4.2632

Abstract

This research investigates using genetic algorithms (GA) to optimize production scheduling in Medan's shoe industry. The study compares traditional manual and First Come First Serve (FCFS) methods against a GA approach, incorporating selection variations such as Boltzmann, Fitness Uniform Selection Scheme (FUSS), Exponential Rank Selection, and Roulette Wheel Selection. The optimal production order is derived from the chromosome with the highest fitness. Results indicate that GA with FUSS selection significantly reduces production time from 73,630 minutes to 45,650 minutes, achieving a 35% improvement in efficiency. This optimization is attributed to FUSS’s ability to maintain a diverse population, preventing premature convergence and ensuring a broader solution for space exploration. Additionally, it was found that using a smaller population size relative to the number of generations yields better optimization results. The study also demonstrates that while Roulette Wheel Selection shows more variability, it achieves higher optimization over time than FCFS. The practical implications of these findings are substantial for the shoe industry, including faster production cycles, better resource allocation, and an enhanced ability to meet customer demands. These benefits are exemplified by implementing the SISPROMA application, an innovative production scheduling information system that leverages machine learning to optimize scheduling in the manufacturing industry. This study provides valuable insights into applying genetic algorithms for production scheduling, highlighting their potential to enhance operational efficiency and reduce costs. Future research should explore additional optimization techniques and real-world applications to validate and extend these findings, ensuring broader applicability and continuous improvements in manufacturing efficiency.
Diagnosis of Diseases in Rubber Stems Using the Dempster Shafer Method Sukmono, Yudi; Pratiwi, Sinthya Ayu; Hatta, Heliza Rahmania; Septiarini, Anindita; Padmo Azam Masa, Amin; Wijayanti, Arini
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.8.4.3474

Abstract

Rubber (Hevea Brasiliensis) is a non-timber forest product originating from the Americas and is currently widely distributed worldwide, including in East Kalimantan, Indonesia. In their management in East Kalimantan, farmers often encounter diseases in rubber plants, especially diseases of the stems, which can cause plant death. This disease requires treatment, but if it is too severe, it can harm farmers economically and in production, so it is essential for farmers to recognize the symptoms of this disease early from changes in the rubber plant stems. This study aims to diagnose diseases of rubber stems using the Dempster Shafer method. Dempster Shafer is a relevant method for overcoming the uncertainty of symptoms and rules, enabling expert systems to generate conclusions with certainty. This method has advantages in solving various problems and simultaneously combining evidence (facts) from several sources. This research was conducted by analyzing a dataset of 80 data, covering 7 types of diseases and 27 different symptoms. The accuracy test results show that the research has an accuracy rate of 96.25%. The implications of this research are significant. It is hoped that it can significantly help rubber plantation farmers in East Kalimantan and also make a valuable contribution to agricultural and plantation extension agents in overcoming the challenges faced due to diseases in rubber plant stems. Thus, this research could increase the productivity and sustainability of the rubber plantation sector in this region.
Boosting Performance of SVM in Koi Classification Using Direct Methods-Based Optimization Arkananta, Muhammad Hafizh; Fawwaz Al Maki, Wikky
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.8.4.2474

Abstract

Many koi fish enthusiasts keep or buy them just for their attractive colors without knowing what type of koi fish they are. The manual classification of koi fish species is still frequently incorrect. As a result, it is critical to apply a machine learning technique to identify various koi fish species. This research implemented a computer vision algorithm to classify koi fish species using the Support Vector Machine (SVM) as the classifier. However, the maximum accuracy SVM can achieve in our koi fish classification system is 79%.  To achieve better performance, the SVM was optimized by applying various optimization methods from the Direct Method group, i.e., the Generalized Pattern Search (GPS), the Powell method, and the Nelder-Mead method. Three optimization methods from the Direct Method group have successfully improved the performance of SVM in this task. Experimental results demonstrated that using the Generalized Pattern Search (GPS) in our classification system can increase the accuracy to 98%. Also, implementing the Powell and the Nelder-Mead method can make the koi classification system obtain a better accuracy of 99%. These results indicate that the proposed approach is a viable solution to overcome the limitations of the SVM algorithm.
Batiknet: Batik Classification-based Management Application for Inexperienced User Putra, Muhammad Taufik Dwi; Pradana, Hilmil; Munawir, Munawir; Pradeka, Deden; Yuniarti, Ana Rahma; Sadik, Jafar; Andhika R, Muhammad
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.8.4.3086

Abstract

Batik has significantly contributed to the Indonesian economy, is diverse, and is spread throughout cities. Currently, batik patterns are very diverse and spread from Sabang to Merauke. Each batik pattern holds distinct meanings, philosophies of life, and ancestral heritage and reflects the region where it was crafted. We introduce a new batik dataset containing five patterns: Kawung, Megamendung, Parang, Sekarjagad, and Truntum. The Convolutional Neural Network (CNN) method is an effective Deep Learning method for extracting image information. CNNs have become the state of the art for various image processing tasks, such as classification, segmentation, and object recognition. This study used several state-of-the-art architectures, including Xception, ResNet50V2, MobileNetV2, and DenseNet169. However, we chose EfficientNetV2 as the primary feature extractor due to its superior performance. Our results show that EfficientNetV2 outperformed other architectures in training, validation, and testing accuracy, making it the best choice for classifying batik patterns. The training process resulted in an accuracy of 98% for training, 97% for validation, and 96% for testing. To ensure the accessibility and practical application of this research, we developed a user-friendly, web-based interface with a RESTful API, making the tool accessible to a broader audience. The application is named "BatikNet," a name chosen to reflect the blend of traditional batik culture ("Batik") with neural network technology ("Net"). This research contributes a valuable dataset and a practical tool for future studies and applications in batik pattern recognition and supports the preservation and understanding of Indonesian cultural heritage
A scoping review and bibliometric analysis (ScoRBA) on dengue infection and machine learning research Zahiruddin, Haikal; Zukarnain, Zuriani Ahmad; Wijaya, Adi
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.8.4.2249

Abstract

Dengue, a fast-spreading vector-borne infectious disease, requires early prediction and prompt decision-making for effective control. To address this issue, we present a comprehensive scoping review and bibliometric analysis (ScoRBA) that aims to map the current literature landscape, identify main research themes, and offer valuable insights into advancements and challenges in dengue infection and machine learning research. Materials for this analysis consist of scholarly articles related to dengue and machine learning research retrieved from the Scopus database. Our method involves a rigorous literature examination, utilizing keyword co-occurrence analysis. Our study reveals a growing interest in dengue and machine learning research, reflected in an increasing number of publications. Through keyword co-occurrence analysis, we identify four major research themes: Data mining using machine learning for dengue prediction, Deep learning approach for dengue prediction models, Neural network optimization for dengue diagnostic systems, and Climate-driven dengue prediction with IoT & remote sensing. Advancements include substantial improvements in prediction models through machine learning and IoT integration, albeit with identified limitations, necessitating ongoing research and refinement. Our findings hold direct implications for public health professionals, academics, and decision-makers, offering data-driven strategies for dengue outbreak control. The identified research themes act as a roadmap for future investigations, guiding the development of more robust tools for early prediction and decision-making in the battle against dengue. This study contributes to understanding the evolving landscape of dengue research, facilitating informed actions to mitigate the impact of this infectious disease. 
Information Behavior Model of e-Health Literacy for Online Health Information-seeking Effectiveness Xuewen, Wang; Azmi Murad, Masrah Azrifah; ZhangLi, Wu; Ismail, Ismi Arif; Mohamed Shaffril, Hayrol Azril
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.8.4.3642

Abstract

This study examines the growing imbalance between the availability and demand for medical resources, rising healthcare costs, and the critical role of accessible health information in disease prevention and public health. The rapid advancement of information technology has established the Internet as a primary source of health information, leading to an overload that surpasses users' processing capacity and causes significant cognitive and emotional challenges. This phenomenon profoundly affects users' health information behavior and decision-making, particularly in self-health management. To address these challenges, eHealth literacy must incorporate an understanding of users' information behavior. This research analyzed the literature on eHealth literacy through a systematic review, identifying key components and categorizing them using Squiers' method. The findings reveal that current definitions fail to address the variability in online health information quality and lack a comprehensive model for understanding information behavior in an overloaded environment. As a solution, this study proposes a new definition of eHealth literacy: the capacity to efficiently search for, access, evaluate, and apply relevant information based on physiological, emotional, and cognitive needs when using electronic health resources. This new definition emphasizes discernment, proactive engagement, personalized use, and practical application of information in health management. The Information Behavior Model of eHealth Literacy (IBeHL) highlights eHealth literacy's multifaceted and dynamic nature, influenced by environmental factors, and recognizes both active information seeking and passive information exposure. Future research should focus on refining this model and exploring its potential to enhance health information behavior and decision-making.

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