A Fundamental Framework For Analysis Of Rainfall Prediction Features Significance,
2025
Multimedia University, Malaysia
A Fundamental Framework For Analysis Of Rainfall Prediction Features Significance, Ye Zhian Teoh, Yim Ling Loo
Journal of Informatics and Web Engineering
Rainfall prediction efforts had been prevalent ever since the impact of climate change on occurrences of natural disasters globally. Implementation of machine and deep learning techniques on features that contribute to rainfall occurrences were conducted with aims of seeking greater prediction accuracy for rainfall occurrences with a lack of study for significance of features in rainfall occurrence prediction. This study presents a framework of rainfall prediction features' significance analysis in the case study of Peninsular Malaysia rainfall occurrences. Features investigated in this study consist of temperature, humidity and wind speed. The designed framework for the investigation includes phases of data …
Enhancing Citrus Plant Health Through The Application Of Image Processing Techniques For Disease Detection,
2025
SRM Valliammai Engineering College, India
Enhancing Citrus Plant Health Through The Application Of Image Processing Techniques For Disease Detection, C Pabitha, B Vanathi, K Revathi, S. Benila
Journal of Informatics and Web Engineering
The foremost task in agriculture is the decisive identification of citrus plants and the timely identification of diseases in the plants with the aim of improving the quality of crops and the yield. In this work, a machine learning algorithm focuses on image processing of citrus to solve issues that are significant and cause concern in agriculture. This work focus on the machine learning models like VGG 19 and VGG 16. In addition, dataset curation, data augmentation and various other methods were employed. The dataset used in this research is a composed one which is recorded in a comprehensive manner …
A New Bw Index For Quantifying Scholars' Research Influence,
2025
Taiyuan University of Technology, China
A New Bw Index For Quantifying Scholars' Research Influence, Bilal Ahmed, Li Wang, Waqar Hussain, Saim Qureshi
Journal of Informatics and Web Engineering
The growing importance of measuring and evaluating academic performance in academic hiring, promotions, funding allocation, and resource distribution has fueled the demand for better metrics. Traditional ranking indicators, such as publication count and citation-based indices, often fail to capture for the interdisciplinary influence and qualitative dimensions of research impact. These shortcomings highlight the need for more comprehensive evaluation metrics. The current study introduces a novel BW Index, which integrates both quantitative and qualitative aspects of researcher contributions aiming to provide a more balanced and comprehensive evaluation of scholarly impact. For evaluating the effectiveness of proposed index, a comparative analysis was …
Facilitating Digital Experience Sharing Among Vehicles Through Utilisation Of Pre-Existing Communication Infrastructure,
2025
University of Science & Technology, Pakistan
Facilitating Digital Experience Sharing Among Vehicles Through Utilisation Of Pre-Existing Communication Infrastructure, Asad Hussain, Umar Farooq, Ihsan Rabbi
Journal of Informatics and Web Engineering
Vehicular communication applications are expanding quickly because of the approach of related technologies, such as vehicular cloud and the Internet of Vehicles (IoV). The combination of the Internet of Things (IoT) and smart transportation is the Internet of vehicles. Data related to infotainment, safety, and effectiveness with different vehicles and the Sustainable framework of vehicles can be exchanged. However, after the appearance of such empowering advancements, still a huge number of ideas need research. Data sharing (related trips and navigation) of new/old models and other new/old model vehicles with the owner's agreement should be taken care of. This paper proposes …
Sentiment Analysis Of The 2024 General Election Through Twitter Using Long-Short-Term Memory Algorithm,
2025
Tokyo Metropolitan University, Japan
Sentiment Analysis Of The 2024 General Election Through Twitter Using Long-Short-Term Memory Algorithm, Angga Wahyu W, Haidar Hilmy Andana, Junta Zeniarja, Aris Febriyanto
Journal of Informatics and Web Engineering
This study analyses sentiment related to the 2024 Indonesian Presidential Election using the Long Short-Term Memory (LSTM) algorithm. A total of 2,400 tweets in the Indonesian language were gathered, with approximately 400 tweets sampled per week. In the data preparation, lexicon-based sentiment tagging, oversampling for class balance, and the creation and training of an LSTM model are all included in the study approach. The built model consists of embedding layers, Conv1D, and two LSTM layers. The LSTM model was selected due to its ability to capture long-range contextual dependencies in sequential text data like tweets, facilitated by its gate mechanisms …
Sentiment Analysis Of The Israel-Palestine Conflict On X: Insights From The Indonesian Perspective Using A Long Short-Term Memory Algorithm,
2025
Maulana Azad University, India
Sentiment Analysis Of The Israel-Palestine Conflict On X: Insights From The Indonesian Perspective Using A Long Short-Term Memory Algorithm, Mohammad Taleb Noori, Muhammad Alif Rahman, Agus Purnomo, Aripin
Journal of Informatics and Web Engineering
The Israel-Palestine conflict which has persisted for decades drives mounting global interest that consequently influences public opinion worldwide. This article examines the sentiment analysis of X (Twitter) data pertaining to the conflict using the Long Short-Term Memory (LSTM) model. This study presents public reactions through an analysis of 1,700 tweets collected between May and July 2023 which encapsulate key recent developments. In this study, several steps were conducted, namely 1) crawling process to get raw data; 2) preprocessing: cleansing, case folding, tokenization, stop word removal, and stemming; 3) modelling and validation using the LSTM model; 4) model evaluation based on …
Aspects-Based Sentiment Analysis Of Extreme Weather On Twitter Using Long Short-Term Memory,
2025
Termez State University, Uzbekistan
Aspects-Based Sentiment Analysis Of Extreme Weather On Twitter Using Long Short-Term Memory, Tursun Abdurahmonov, Muhammad Nabil Toby Abiyyu, Dzikru Nur Khayat, M. Ary Heryanto
Journal of Informatics and Web Engineering
This study presents an aspect-based sentiment analysis of tweets related to extreme weather events in Indonesia, utilizing the Long Short-Term Memory (LSTM) model. The dataset was obtained through a Twitter crawling process, followed by a series of preprocessing steps including data cleaning, stop word removal, normalization, tokenization, and stemming. The three primary areas of emphasis in the study were kinds of bad weather forecasts, and the government or society reactions. Using a lexicon-based technique, sentiment labelling generated three groups: positive, neutral, and negative. A random oversampling method was employed to address the data imbalance. The model using the LSTM algorithm …
Sentiment Analysis And Topic Modelling On Twitter Related To Mobile Legends: Bang Bang Game Using Lexicon-Based, Lda, And Svm,
2025
Dian Nuswantoro University, Indonesia
Sentiment Analysis And Topic Modelling On Twitter Related To Mobile Legends: Bang Bang Game Using Lexicon-Based, Lda, And Svm, Hikmal Muhammad, Fauzi Adi Rafrastara, Kristoforus Adrian Setiadi, Arley Japardi
Journal of Informatics and Web Engineering
Mobile Legends: Bang Bang (MLBB) has become a significant phenomenon within the global e-sports landscape, attracting millions of active players and fans. This study presents a comprehensive sentiment analysis and topic modelling of MLBB-related discussions on Platform Twitter, combining a lexicon-based approach, Latent Dirichlet Allocation (LDA), and Support Vector Machine (SVM) classification within a unified analytical pipeline. A dataset of 4,313 tweets was analysed, revealing that 70.8% expressed neutral sentiment, suggesting that much of the community's communication is informational rather than emotionally charged. Positive sentiments were associated with game content updates and rewards, while negative sentiments focused on technical and …
Editorial Preview For June 2025 Issue,
2025
MMU Press
Editorial Preview For June 2025 Issue, Su-Cheng Haw
Journal of Informatics and Web Engineering
Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This June 2025 release contains 28 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Augmented Intelligence, Artificial Intelligence (AI), Generative AI, Data Mining (DM), Software Engineering, Sentiment Analysis, Recommender Systems, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 6 papers curated by our Thematic Editor, Assoc. Prof. Dr. Heru Agus Santoso, under the theme "Augmented Intelligence". In his editorial, Assoc. Prof. Dr. Heru …
Diabetes Risk Prediction Using Shapley Additive Explanations For Feature Engineering,
2025
Multimedia University, Malaysia
Diabetes Risk Prediction Using Shapley Additive Explanations For Feature Engineering, Chinwe Miracle Chituru, Sin-Ban Ho, Ian Chai
Journal of Informatics and Web Engineering
Diabetes is prevalent globally, expected to increase in the next few years. This includes people with different types of diabetes including type 1 diabetes and type 2 diabetes. There are several causes for the increase: dietary decisions and lack of exercise as the main ones. This global health challenge calls for effective prediction and early management of the disease. This research focuses on the decision tree algorithm utilization to predict the risk of diabetes and model interpretability with the integration of SHapley Additive exPlanations (SHAP) for feature engineering. Random forest and gradient boosting models were developed to identify the risk …
Image-Based Detection And Classification Of Poultry Diseases From Chicken Droppings In Open House Poultry Farms,
2025
Multimedia University, Malaysia
Image-Based Detection And Classification Of Poultry Diseases From Chicken Droppings In Open House Poultry Farms, Md Najmul Hasan, Zu Jun Khow, Vik Tor Goh, Sarina Mansor, Yi-Fei Tan
Journal of Informatics and Web Engineering
Monitoring chicken health is essential for maintaining the production efficiency of poultry farms and meeting the demand for poultry products. Previous studies have explored various methods, including utilizing sound, behaviour, and the shape of the chickens, as well as the conditions of their droppings, to assess chicken health. In this research, we monitor chicken droppings as a reliable indicator of chicken health. We develop an automated system for detecting chicken droppings and identifying health conditions, specifically in open house poultry farms in Malaysia. Open poultry houses are the most common design in Malaysia due to their lower construction and maintenance …
Analysis Of Forensic Disk Imaging Tools For Data Acquisition And Preservation,
2025
Sunway University, Malaysia
Analysis Of Forensic Disk Imaging Tools For Data Acquisition And Preservation, Michelle Chee Ern Lim, Brandon Chen Hong Chow, Le Ying Lim, Tarini Shanbagamaran, Darren Yong Jun Lim, Ngu War Hlaing, Ahmad Sahban Rafsanjani
Journal of Informatics and Web Engineering
The identification, preservation, analysis, and presentation of electronic evidence to support legal or organizational inquiries constitute the discipline of digital forensics, which is crucial to contemporary investigations. A crucial component of forensic inquiry, disk imaging guarantees precision, dependability, and legal defensibility. To preserve the original evidence, disk imaging makes an identical, bit-by-bit duplicate of a digital storage device, capturing hidden data, deleted material, and active files. Given the critical role of disk imaging in forensic investigations, selecting the right tool is crucial for accuracy, efficiency, and compliance with forensic standards. This study assesses widely used tools, including AccessData FTK Imager, …
Performance Benchmarking: Pre-Trained Models And Custom Convolutional Neural Networks In Deep Learning,
2025
Study World College of Engineering, India
Performance Benchmarking: Pre-Trained Models And Custom Convolutional Neural Networks In Deep Learning, Sheemona Joseph C., S. Ganesh, S. Kannadhasan, K. Selvipriya
Journal of Informatics and Web Engineering
Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffic signs—a crucial task for autonomous vehicles (AVs) and advanced driver assistance systems (ADAS). These technologies' ability to accurately recognize traffic signs enables them to make informed decisions in real time, thereby elevating the standard for overall driving safety. The study uses a large, annotated dataset of images of traffic signs to train and assess the CNN model. We developed a model that can recognize a large number of traffic lights, …
Editorial: Augmented Intelligence For Enabling Knowledge-Driven Decision Making,
2025
Universitas Dian Nuswantoro, Indonesia
Editorial: Augmented Intelligence For Enabling Knowledge-Driven Decision Making, Heru Agus Santoso
Journal of Informatics and Web Engineering
More flexible and cooperative decision-making processes are required as a result of society's digital transformation. The role of augmented intelligence, that is the synergistic fusion of artificial intelligence and human judgment in facilitating knowledge-driven strategies across various domains is examined in this thematic issue. The integration of business intelligence and software engineering, which forms the foundation for creating intelligent, scalable, and explicable systems, is essential to this investigation. The six chosen papers in this issue show how machine learning techniques can be used to mine and model both structured (such as health records indicators) and unstructured (such as product reviews, …
Exploring The Relationship Between Academic Achievement And Chatgpt Usage: A Survey Of Higher Education Students In Malaysia,
2025
Multimedia University, Malaysia
Exploring The Relationship Between Academic Achievement And Chatgpt Usage: A Survey Of Higher Education Students In Malaysia, Mawar Madiah, A'Tiqah Rashidah Abu Samah
Journal of Informatics and Web Engineering
Students' academic achievement is caused by several factors which include cognitive capability, study habits, instructor effectiveness, family support, socio-economic status, and access to modern learning technologies like ChatGPT. Since its introduction, studies have suggested that ChatGPT has significantly impacted students’ academic performance. This study aims to explore whether students who perform well academically in higher education are more likely to use ChatGPT to enhance their studies and to gather their opinions on this learning tool. A survey was conducted with forty-one students with excellent academic performance. The results of this study show that most students with high academic achievement view …
Performance Evaluation On Covid-19 Prediction Using Machine Learning Models,
2025
Multimedia University, Malaysia
Performance Evaluation On Covid-19 Prediction Using Machine Learning Models, Obai Ali Abderlahman, Naveen Palanichamy, Su-Cheng Haw, Subhashini Gopal
Journal of Informatics and Web Engineering
The COVID-19 pandemic has placed enormous strain on providing health care services internationally while reinforcing the argument for the need to strengthen forecasting techniques. Existing forecasting methods have drawbacks, especially in determining the long-term consequences of the pandemic and understanding its broad reach across various locations and populations. This project proposes an evaluation of machine learning (ML) models with the aim of improving predictions, particularly the accuracy in long-term forecasting, of subsequent trends of the COVID-19 pandemic. A systematic review highlights previous forecasting attempts as a reference for the approach. This project emphasizes extensive data collection, model formulation and testing …
Machine Learning Model For Assessing Human Well-Being Using Brain Wave Activities,
2025
HELP University, Malaysia
Machine Learning Model For Assessing Human Well-Being Using Brain Wave Activities, Sellappan Palaniappan, Rajasvaran Logeswaran, Yoke Leng Yong
Journal of Informatics and Web Engineering
This study presents a novel machine learning approach to assess human well-being through the analysis of brain wave activities. We developed a Random Forest classifier to categorize brain wave patterns into three states of well-being: good, normal, and bad. Using synthetic data simulating electroencephalography (EEG) readings, our model achieved an overall accuracy of 96.17%. The feature importance analysis revealed that alpha waves (34%) and beta waves (29%) were the most significant predictors of well-being states, which aligns with existing neuroscientific literature linking alpha activity to relaxation and beta activity to cognitive engagement. The confusion matrix demonstrated the model's particular strength …
A Conceptual Approach To Predicting Seismic Events And Flood Risks Using Convolutional Neural Networks,
2025
Multimedia University, Malaysia
A Conceptual Approach To Predicting Seismic Events And Flood Risks Using Convolutional Neural Networks, Mahmoud Rehan, Wan-Noorshahida Mohd-Isa, Noramiza Hashim
Journal of Informatics and Web Engineering
This paper explores the application of convolutional neural networks (CNNs) in predictive modelling for seismic events and flood risks, with a particular focus on forecasting extreme quantile events that exceed historical data limits. Traditional risk assessment methods often struggle to estimate such extremes, highlighting the need for more advanced predictive models capable of handling rare but high-impact events. This research enhances CNN architecture to improve accuracy in high quantile predictions by integrating multi-source spatiotemporal data, addressing a critical research gap. The methodology involves incorporating diverse datasets, including geospatial, meteorological, and historical seismic or flood records, into CNN models to augment …
The Impact Of Deep Learning In Brain Tumour Analysis,
2025
Karnavati University, India
The Impact Of Deep Learning In Brain Tumour Analysis, Sangeeta Giri, Manivel Kandasamy, Meet Rafaliya
Journal of Informatics and Web Engineering
The need for early and precise identification of abnormalities has made the detection and classification of brain tumours essential components of medical diagnosis. Because brain tumours are naturally complex and can have a wide range of sizes, shapes, and types, conventional diagnostic techniques like MRI interpretation and manual evaluations are difficult and time-consuming. Traditional methods frequently depend on human expertise, which is prone to errors, delays, and variability. Deep learning (DL) developments, on the other hand, have completely changed this field by providing increased automation, efficiency, and precision in tumour detection and classification because they can automatically extract pertinent features …
Exploring User Perceptions Of Security In Mobile Banking: A Study In Malaysia,
2025
Multimedia University, Malaysia
Exploring User Perceptions Of Security In Mobile Banking: A Study In Malaysia, Kai En Lim, Ying Han Pang, Shih Yin Ooi, Wan Xuan Kow, Tang Xing Cheang, Mao Wei Tan
Journal of Informatics and Web Engineering
Mobile banking has become increasingly popular in Malaysia due to its convenience and efficiency. However, persistent security concerns such as vulnerabilities to cyber attacks, malware and phishing affect user trust and impede adoption. This study aims to explore how users perceive and experience the security features of mobile banking applications. In spite of the implementation of security controls, understanding user perceptions and experiences remains essential, as these are core factors which influence mobile adoption. Thus, this study investigates the user experience of mobile banking security features in Malaysia. Specifically, we focus on the users’ perceptions, satisfaction levels and concerns. We …
