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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 51 Documents
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Search results for , issue "Vol 9, No 4 (2025)" : 51 Documents clear
Exploring the Role of Machine Learning and Big Data Analytics in Enhancing Decision-Making Processes: A Systematic Literature Review Prawira, Nicholas; Wella, -; Natalia, Friska
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3244

Abstract

This Systematic Literature Review (SLR) analyzes the influence of Machine Learning (ML) and Big Data Analytics (BDA) on decision-making processes in several industries. The study aims to explore the potential of machine learning and big data analytics in enhancing decision-making, examining the tools and platforms used, and identifying the challenges encountered during deployment. Employing the PRISMA technique, 31 publications published from 2019 to 2024 were meticulously selected through a stringent screening process, using Scopus as the principal database. The results indicate that machine learning and big data analytics substantially enhance predictive accuracy, operational efficiency, and data privacy measures, while facilitating seamless integration with current systems. Furthermore, these technologies are becoming progressively accessible to Small and Medium Enterprises (SMEs). In the healthcare sector, machine learning models have exhibited a diagnosis accuracy of 99% in detecting breast cancer. Nonetheless, the report underscores other research deficiencies, particularly the necessity for more cost-effective solutions designed for SMEs. These limitations signify opportunities for future study to investigate ML and BDA applications in underexamined areas, such as logistics and manufacturing. This research highlights the necessity of creating economical, scalable, and industry-specific machine learning and big data analytics solutions to address existing difficulties. This systematic literature review (SLR) seeks to elucidate the function of machine learning (ML) and big data analytics (BDA) in decision-making, thereby assisting researchers and practitioners in enhancing the utilization of these technologies across many industrial applications.
Investigating the Role of Gamification in Motivating Students Learning Si, Josh Chong En; Karuppiah, Nanthakumar; Mahindran, Nirmal Kumaar; Law, Kyra Ley Sy
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3418

Abstract

This study investigates the role of gamification in enhancing student motivation within higher education, specifically targeting bachelor’s degree students in Klang Valley, Malaysia. The objective is to examine how the ARCS-R model by integrating the ARCS motivational framework (Attention, Relevance, Confidence, Satisfaction) with the Relatedness component of Self-Determination Theory (SDT) influences student motivation. A quantitative approach was employed, involving 208 business students who engaged in gamified learning activities via the Socrative platform. Participants completed competitive and collaborative tasks, followed by a survey measuring motivational constructs. Partial Least Squares Structural Equation Modelling (PLS-SEM) was utilized for data analysis. The results indicated that Relevance, Confidence, Satisfaction, and Relatedness had significant positive effects on student motivation, while Attention did not show a significant impact. The findings suggest that although gamified environments can enhance motivation, some elements, such as Attention, may be less effective without dynamic game design features. The study underscores the importance of integrating more interactive and adaptive game mechanics to sustain learner engagement. This research contributes to the understanding of motivation theories in gamified learning, offering empirical support for combining ARCS and SDT models. Future research should explore longitudinal effects and the role of personalized gamification strategies to optimize motivational outcomes across diverse student populations.
Processing of Brain Images Dataset: Introducing a Novel LBP Features Extraction Method to Enhance the Prediction System of Brain Hemorrhage Khalil, Adil Ibrahim; Mohammed, Mohammed Sami; K. Abbas, Ahmed
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3336

Abstract

To detect brain bleeding in CT images, this study presents an improved Local Binary Pattern (NLBP) operator for texture analysis in medical imaging. The suggested NLBP utilizes an XOR operation with multi-radius feature extraction (r=1 and r=2) to capture fine-grained and larger texture patterns. Applied techniques compare pixel intensities over two radii and use the NLBP operator on image patches. To emphasize sudden changes in texture, the binary patterns produced by these two radii were processed using XOR to highlight variations in pixel intensities. To achieve the goal of this study, four machine learning models were applied to the CT brain images dataset to identify hemorrhage cases from non-hemorrhagic. According to the results, the NLBP approach considerably improved classification performance over conventional LBP. The random forest algorithm achieved a superior prediction accuracy of 94.05% while employing the NLBP strategy for feature extraction, in contrast to only 70.03% accuracy obtained using the LBP method for a similar algorithm. The NLBP approach improved edge recognition and classification accuracy by highlighting differences between surrounding pixel brightness and capturing multi-scale texture information.  It concluded from these results that the NLBP operator provides a reliable method for medical image analysis by combining XOR-based refinement with multi-radius extraction. Additional investigation may examine the use of NLBP in different imaging modalities and refine the feature selection procedure for enhanced performance in various settings.
Assessing InsurTech Purchase Intentions Among Young Working Adults in Malaysia: A TRA Approach Mohd Rom, Noor Ashikin; Abdul Malek Beh, Nur Hidayah; Abdul Rani, Nazatul Shima; Md. Hassan, Nurbani; Agos Lokman, Farha Zafira
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3676

Abstract

InsurTech is emerging as a key player in Malaysia's insurance landscape, bolstered by strong government support, regulatory initiatives, and increasing consumer demand for digital solutions. As the insurance industry undergoes digital transformation, understanding the factors driving the adoption of InsurTech platforms is crucial for both academic inquiry and industry practice. This study aims to investigate the factors influencing young working adults' intentions to purchase life insurance using InsurTech platforms. Utilizing a quantitative approach based on the Theory of Reasoned Action (TRA), the research gathered data from 118 respondents through a non-probability convenience sampling method, which was then analyzed using SPSS version 29 and multiple linear regression. The findings indicate that attitude, subjective norms, insurance literacy, and trust have significant and positive effects on the intention to purchase life insurance via InsurTech platforms among Malaysian young working adults. Notably, attitude and trust emerged as the most influential factors, highlighting the transformative role of technology in shaping consumer behavior. These results, along with broader industry trends, emphasize the growing importance of InsurTech in the insurance sector, particularly in driving purchase intentions among younger demographics. To stay competitive, insurers must focus on fostering trust and enhancing the perceived value of their digital platforms. The rapid integration of AI, IoT, and other advanced technologies not only streamlines operations and reduces costs but also enhances customer experience, ensuring that InsurTech will remain central to the industry's future. For future research, investigating the impact of emerging technologies such as blockchain and AI on trust and customer experience within InsurTech platforms would be a promising direction.
A Case Study on Energy Efficiency at Universiti Pertahanan Nasional Malaysia (UPNM) Building for Smart Campus Initiatives Thanakodi, Suresh; Ismail, Nur Hidayah; Moh Nazar, Nazatul Shiema; Mukhtaruddin, Azharudin; Hidayat, Hendra; Mohd Isa, Mohd Rizal
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3409

Abstract

In efforts to minimize harmful greenhouse gas (GHG) emissions and mitigate climate change, energy efficiency has become a global priority. Commercial buildings, including university facilities such as Universiti Pertahanan Nasional Malaysia (UPNM), play a significant role in this effort, particularly in becoming a smart campus. Commercial buildings have been identified as major contributors to greenhouse gas (GHG) emissions, primarily due to their high energy consumption for air conditioning, lighting, and other operational needs. In alignment with the Paris Agreement 2016, the Malaysian government has implemented various energy-saving initiatives to reduce carbon emissions and achieve Energy Efficient Building Star Ratings. This study analyses five years of energy consumption at UPNM using the desktop audit method. Data collected from January 2018 to December 2022 includes the Nett Floor Area (NFA), energy consumption, and maximum demand of the UPNM buildings. The analysis encompasses energy consumption and expenditure, maximum demand, Building Energy Intensity (BEI), carbon footprint, and building energy labeling under the National Building Energy Labelling Standard. The findings indicate that UPNM has achieved a 5-star rating in 2020 and 2021, compared to a 4-star rating in 2018, 2019, and 2022. The BEI of UPNM buildings from 2018 to 2022 met the Energy Commission (EC) requirement of being below 135 kWh/m²/year. This study has also identified recommendations for further enhancing the energy efficiency of the UPNM Building, including regular maintenance of electrical appliances and conducting energy efficiency awareness campaigns.
Few-Shot-BERT-RNN Narrative Structure Analysis for Andersen's Stories Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Hernandez, Leonel
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3932

Abstract

Event Extraction (EE) is a pivotal task for NLP, where important events in the narrative text need to be detected and recognized. We present an alternative method for extracting events from Hans Christian Andersen's fairy tales, utilizing Few-Shot Learning with BERT (Bidirectional Encoder Representations from Transformers) and RNN (Recurrent Neural Network) in this paper. We selected Andersen's fairy tales because they are characterized by rich narratives and symbolic language, which also often prevents automatic event extraction. To reduce reliance on labeled samples, we utilize the Few-Shot Learning method, which enables the model to learn from a small number of labeled event examples trivially. The BERT model is used to generate deep representations by modeling the context between words and sentences. RNN is essential to capture the sequence of events in the story, which determines the structure of the narrative. The findings demonstrate that the proposed framework significantly improves event extraction, with high values of evaluation metrics such as in accuracy, precision, recall, and F1-score. The proposed method is also effective in extracting non-explicit events while keeping the narrative context. Despite the challenges posed by metaphorical language and subjective events, this work demonstrates that Few-Shot Learning, BERT, and RNNs offer a promising solution to the task of event extraction from complex narratives.
Restricted Boltzmann Machine Approach for Diagnosing Respiratory Diseases Haviluddin, -; Nurhalifah, Siti; Trahutomo, Dinnuhoni; Wibawa, Aji Prasetya; Utama, Agung Bella Putra
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3427

Abstract

Respiratory diseases remain a significant global health challenge, particularly in developing countries where high morbidity and mortality rates persist. This study aims to establish a diagnostic approach for respiratory diseases using the Restricted Boltzmann Machine (RBM) method to support early detection and improve clinical decision-making. The research utilizes 180 medical records from patients at I. A Moeis Samarinda Hospital, East Kalimantan, Indonesia, includes 22 symptom variables associated with six respiratory disease types: sinusitis, pharyngitis, bronchitis, pneumonia, tuberculosis, and asthma. The collected data were preprocessed into binary formats to represent symptomatic and asymptomatic conditions, facilitating practical training in the RBM model. Data splitting was conducted with 70:30, 80:20, and 90:10 ratios for training and testing sets. The RBM architecture was optimized to enhance model performance by tuning key parameters, including the number of epochs, learning rate, and hidden neurons. Experimental results demonstrate that the RBM model achieved high diagnostic accuracy, with an accuracy of 98%, sensitivity of 98%, and specificity of 99% under the configuration of 5000 epochs, a learning rate of 0.1, and 53 hidden neurons. These findings indicate the model’s capability to recognize patterns and accurately classify respiratory diseases based on clinical symptoms. The study highlights the potential of integrating AI-based diagnostic systems like RBM into healthcare services, particularly in resource-limited settings. Future research should explore larger, more diverse datasets and consider environmental and socioeconomic factors to improve the model’s generalizability and practical applicability.
Classification of Intraoral Images in Dental Diagnosis Based on GLCM Feature Extraction Using Support Vector Machine Romadhon, Nur Rizky; Sigit, Riyanto; Dewantara, Bima Sena Bayu
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3051

Abstract

This study aims to develop an AI-based diagnostic tool for classifying dental conditions and tooth types to enhance the accuracy and efficiency of dental diagnostics. Manual documentation and diagnosis in dentistry are often prone to errors, inefficiencies, and delays, leading to adverse patient outcomes. Leveraging digital image processing and machine learning, this research addresses these challenges by automating the classification process. Dental imaging data were collected from the Dental and Mouth Hospital (RSGM) of Nala Husada Surabaya, Indonesia, comprising 3,910 images categorized into dental conditions (1,767 images) and tooth types (2,143 images). The dataset was preprocessed through resizing, grayscale conversion, histogram equalization, and median filtering. Texture features were extracted using the Gray Level Co-occurrence Matrix (GLCM), and classification was performed using Support Vector Machine (SVM), K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest algorithms. The SVM algorithm achieved the highest accuracy of 54.24% for dental conditions and 41.49% for tooth types, outperforming other methods. However, the overall performance was suboptimal, primarily due to dataset limitations, reliance on GLCM for feature extraction, and insufficient preprocessing. The results highlight the potential of AI-based tools in dentistry but also underscore the need for improvements in dataset diversity, advanced feature extraction methods, and hyperparameter optimization. Future research should focus on expanding the dataset, exploring deep learning-based feature extraction, and employing robust evaluation strategies to enhance model performance. This study lays the groundwork for developing a more reliable and efficient AI-based diagnostic tool, ultimately improving patient outcomes and streamlining clinical workflows in dentistry.
A Novel Approach for Bali Cattle Classification: Integrating the Fuzzy Inference System with Certainty Factor and Morphometric Parameters Arnaldy, Defiana; Seminar, Kudang Boro; Neyman, Shelvie Nidya; Sukoco, Heru; Muladno, -
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3218

Abstract

Enhancing the productivity and quality of Balinese cattle is a crucial goal for improving livestock management practices in Indonesia. Traditional evaluation methods used by farmers are often subjective and inconsistent, leading to inaccuracies in cattle classification and limiting the effectiveness of breeding and selection processes. To address these challenges, this study proposes a Fuzzy Inference System with Certainty Factor (FIS-CF) to improve cattle classification by providing more objective and reliable grading criteria. The model utilizes key physical parameters, including shoulder height, body length, and chest circumference, as input features to categorize cattle into three quality classes. A diverse dataset was collected from the People's Animal Husbandry School (SPR) and various farms across Indonesia to evaluate the model's performance. The FIS-CF model achieved a classification accuracy of 95.93% and a balanced accuracy of 96.20%, outperforming traditional methods that rely on subjective assessment. These results demonstrate that the proposed model provides a consistent, scalable, and data-driven solution for livestock classification, helping farmers make more informed decisions in cattle selection and breeding. Additionally, the model addresses key limitations of current practices by reducing reliance on manual evaluations, which often vary between assessors. The findings highlight the potential for wider adoption of the FIS-CF model across the livestock sector to improve productivity and streamline herd management processes. Future research will aim to refine the model further by incorporating additional parameters, such as age and weight, and expanding its validation to larger datasets covering different cattle breeds and farming environments to ensure broader applicability in sustainable livestock management.
A Secure Cloud Service Game Theory Approach to Demand Response Modelling for Residential Users in Smart Grid L, Priya; V, Gomathi; Palanichamy, Naveen
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3541

Abstract

In today's world, electricity has become the keystone for every activity undertaken. As the population increases, the electricity demand has reached unprecedented levels, putting strain on electrical grids. In many developing countries, the residential sector consumes 60% of the peak load. The negative consequences of this trend provide a pathway for frequent brownouts, which lead to enormous losses for industries as well as residential households. To date, the flexibility of energy is usually achieved on the generation side. However, an easier way to counter this would be to manage usage on the demand side. The development of smart grid facilities has enabled communication between utilities and consumers. Therefore, the demand response functionality shows greater potential to stabilize the power supply and demand for the utility and consumers, respectively. In this paper, an intelligent secure cloud service game theory-based demand response modelling algorithm is proposed to handle peak demand in the residential sector. This innovative strategy enables residential consumers to achieve mutually beneficial outcomes. Enhancing communication security between utility providers and consumers, optimizing renewable energy utilization, and improving cost-effectiveness and reliability in electricity production and delivery are vital for meeting the rising demand. The simulation results suggest that the proposed approach efficiently reduces the Peak-to-Average Ratio, leading to mutual advantages for both consumers and utility providers. This approach addresses the growing demand for electricity while promoting sustainable energy through improved energy management practices.

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