bluuebunny/crossref_metadata_2025 huggingface dataset:
Unveiling Gold Membership Classification Using Machine Learning
Authors: Christiano Tjokro Vincencius , Oetama Raymond Sunardi , Prasetiawan Iwan
Published: February 2025

The main challenge in loyalty programs is selecting customers with limited funding. To address it, we explore various machine learning-based classification models. This study aims to enhance the effectiveness of a marketing strategy that promotes gold membership to customers with prior transaction history. Previously, much research applied decision trees, random forests, and logistic regression for classification, but gradient boosting is still unpopular. However, in this study, the Gradient Boost algorithm exhibits the best performance among these models, achieving an impressive accuracy of around 88%. This result underscores the model's capability to classify customers, thereby suggesting its potential to significantly enhance the marketing strategy's effectiveness. The analysis identifies crucial features that influence the model's predictive capabilities. Notably, the recency of the last visit, the number of transactions involving wine and meat, marital status, and the number of offline store transactions are identified as influential factors. Leveraging machine learning techniques enables the automation of the customer selection process, facilitating the attraction of a more extensive customer base. By targeting those customers most likely to respond positively to the gold membership offer, efficient resource allocation can be achieved. This research provides valuable insights and practical recommendations for implementing an effective marketing strategy under resource constraints. Combining machine learning algorithms and feature identification enables efficient targeting of potential customers, maximizing the impact of the gold membership offering. Implementing the findings of this study could lead to increased customer acquisition and improved overall business performance.
Assessing the Multifaceted Determinants of Collaborative Competence Among Students in the Digital Learning: A Comprehensive Analysis
Authors: Saputra Indra , Sari Resti Elma , Mahniza Melda , Hayatunnufus Hayatunnufus , Rahmiati Rahmiati , Yanita Merita , Yupelmi Mimi
Published: February 2025

The study background related to the students' collaborative competence in digital learning is still relatively low. The objective of this study is to examine the elements influencing collaborative competence. The study involved 107 cosmetology and beauty education students. The method used was a survey with data collection using questionnaires developed based on predetermined variable indicators. The analysis of data employed Structural Equation Model-Partial Least Square (SEM-PLS) with Smart PLS 4.0 software. The SEM results describe the Convergent Validity (Loading Factor and Average Variance Extracted) and Discriminant Validity (Fornell Larcker Criterion and Cross Loading), which states that the measurement model is valid. Furthermore, the Composite reliability and Cronbach's Alpha conclude that the measurement model is reliable. The analysis results indicate a positive and significant correlation of predictor variables, including project-based learning, social media, instructional approach, and material relevance to collaborative competence. Based on the variable analysis, material relevance becomes the highest aspect, followed by project-based learning, which increases the collaborative competence of students. Conversely, social media as a mediator variable weakens the level of correlation of the predictor variables to collaborative competence. This study contributes to understanding factors affecting students' collaborative competence in digital learning environments, with significant implications for educators, institutions, and policymakers in shaping digital learning frameworks enhancing collaborative competence. Future research, including longitudinal studies, could investigate the lasting impact of digital learning environments on developing collaborative competence over time.
EEG Power Analysis of Children with Autism Spectrum Disorders (ASD) Based on EIBI Curriculum Levels
Authors: Rahmahtrisilvia Rahmahtrisilvia , Setiawan Rudi , Sopandi Asep Ahmad , Efrina Elsa , Kusumastuti Grahita
Published: February 2025

Early Intervention Behavioral Therapy as a method has been shown to aid children diagnosed with Autism in adjusting behavior through Applied Behavior Analysis. While there are three levels of ABA, EIBI does not provide a concrete metric of what separates between the individual levels. The current study focuses on differentiating the electrical patterns found in EEG in children and plans to explore how EIBI can serve across the ABA spectrum. The electrodes F3, F4, C3, C4, P3, P4, O1, and O2 were used to capture the EEG signals and were utilized in estimating the power, spectral density using the Welch method. It was observed during the statistical examination that there existed differences in the results of power across the frequency band amongst the groups. The higher levels of Alpha lead us to believe that there was better emotional management. The chronic group was shown to have more prominent Delta power reflecting weakened control. Comparatively, beginning level’s theta power was found to be higher across all groups showcasing change in attention requiring tasks. Due to greater focus being placed on the lower range frequency activity there existed no noteworthy changes in the Beta and Gamma portions. These findings highlight the role of EIBI in neuromodulation in the Alpha and Delta bands, and its application in the enhancement of emotional and neurological stability. EEG is an effective measure as it quantifies EIBI outcomes. Further studies should examine the long-term effects and enhance curriculum concepts to increase the efficacy of the interventions.
Development of Augmented Reality Based Interactive Learning Media on Electric Motor Installation Subjects
Authors: Muskhir Mukhlidi , Luthfi Afdal , Sidiq Hazmi , Fadillah Rahmat
Published: February 2025

Interactive learning media play an essential role in the learning process. In today's digital era, interactive learning media is one of the best choices for improving the quality of learning by making it more effective and efficient. This study aims to develop augmented reality (AR)--based interactive learning media for electric motor installation subjects. The method used in this research is the research and development (R&D) method with the 4-D development model. This research consists of several stages: defining, designing, developing, and disseminating. Based on the research results, it can be concluded that this augmented reality learning media development research has produced a valid and practical augmented reality learning medium for electric motor installation subjects. The media and material validation results consist of two media validators and two material validators. Obtained a media validation value of 0.58 and a material validation value of 0.62 using the validity interval category ≥ 0.4. Thus, the media and materials developed were declared valid. The augmented reality learning media practicality test results obtained 86.63% by using the practicality interval category > 75%–100%. Thus, AR-based learning media in electric motor installation have been considered practical. Implications for further research could include developing AR media for other subjects or applying AR technology in various learning contexts, including integrating more complex interactive features or adapting the media for broader educational needs. In addition, this research could encourage exploring AR in distance learning scenarios and increase student engagement through gamification or further simulation in skills-based learning.
Measuring Score of Ethnic Tolerance Index among Peacekeepers using a MyETI System Dashboard
Authors: Wan Husin Wan Norhasniah , Kamarudin Nur Diyana , Hilmi Muhammad Amjad , Zainurin Siti Juwairiah , Jamilah Maryam
Published: February 2025

In post-conflict nations, the state-building process might take up to ten years to provide positive outcomes. This scenario has become increasingly challenging due to the escalation of intolerance among different ethnic groups, leading to incidents of communal violence in the aftermath of the war. Therefore, it is imperative to have ethnic tolerance and cultural understanding in peacekeeping operations that occur in a more intricate setting. The presence of ethnic tolerance among peacekeepers is crucial for ensuring the efficacy of peacekeeping missions. The increase of intolerant perspectives often causes the beginning of ethnic conflicts in multi-ethnic societies. Therefore, the main objective of peacekeepers deployed in these countries is to reinstate peace and security. This study proposes employing an online analysis to assess the ethnic tolerance index among peacekeepers accurately. The suggested method entails collecting and analyzing real-time survey data via a MyETI system dashboard, which may precisely evaluate the ethnic tolerance index score among Malaysian individuals. The MyETI e-survey has 103 questionnaires organized into four main categories: ethnic cross-relationships, governance, ethnic tolerance, and religious beliefs. To achieve the study's goal, 103 Malaysian peacekeepers who have previously been deployed to different United Nations Peacekeeping Operations (UNPKO) will be requested to answer the questions using the MyETI dashboard. The results could enhance the ethical guidelines for cultural competence, prioritizing understanding ethnic tolerance in peacekeeping operations or deployments.
Boosting Performance of SVM in Koi Classification Using Direct Methods-Based Optimization
Authors: Arkananta Muhammad Hafizh , Fawwaz Al Maki Wikky
Published: February 2025

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.
Knowledge-Based Intelligent System for Diagnosing Three-Wheeled Motorcycle Engine Faults
Authors: Ary Setyadi Heribertus , Supriyanta Supriyanta , Nurohim Galih Setiawan , Widodo Pudji , Sutanto Yusuf
Published: February 2025

Three-wheeled motor engine damage is one of the most serious problems with all motorcycles. When problems appear, it becomes difficult for users to repair and diagnose faults because knowledge about machine breakdown symptoms is minimal. Most motorcycle repair shops don’t have mechanics who understand tricycle motorbike engines, so they are less accurate in diagnosing damage symptoms, only based on estimates. Three-wheeled motorbikes have several differences in structure and spare parts compared to motorcycles because tricycle motorbikes have an axle like a car. For this problem, an information system is needed with a method that combines an expert's experience, expertise, and knowledge to develop expert system applications based on several cases that have been experienced and are known as case-based reasoning. This research aims to produce a web-based expert system to diagnose and solve tricycle motorbike engine damage problems. The case-based reasoning method with the K-Nearest Neighbor algorithm is used to assist in analyzing engine damage and give solutions to the issues in three-wheeled motorbike engines. Using two methods is appropriate because of the answers found and the similarities calculated by the cosine similarity method, which experts then review to get the proper solution. From testing using 20 samples of diagnostic data, an accuracy percentage of 85% was obtained. The calculation result for precision is 85%, and recall is 85%.
Mixed Learning Models and IoT Devices: Effectively Increasing Competence and Training Independent Learning Students in Unnormal Situations
Authors: Purba Ramen Antonov , Simarmata Janner , Limbong Tonni , Damanik Romanus
Published: February 2025

Abnormal situations often occur, such as natural disasters and COVID-19. Educational institutions struggle to regulate learning. The web programming course aims to shape students into website programmers. Independence in learning is needed so that competence is obtained. Students are not enough to rely on learning from the lecturer. This study aims to analyze the combination of the Inquiry-Based Learning model with IoT devices based on Android mobile. As a supporter, an application is built with a mobile programming language. This type of research is quasi-experimental. Calculations using SPSS 23.0. An experimental class learns to use the Inquiry-Based Learning model with IoT devices, and a control class learns with various media. The research subjects were 60 students of Information Management. The study found differences in students' competence and learning independence in those who learned to use the inquiry-based learning model with IoT devices compared to those who studied with various media. The test results showed a higher increase in the experimental class. The experimental class's value is 14.40 for a gain of 7.5. The sig. value is  .000, and the average gain is .83. Control class score is 11.87, an increase of 5.1, sig. value is .000, and the average gain is .53. Applying the inquiry-based learning model with IoT devices has also proven to be effective as a model and learning media in abnormal situations and reinforced by the average gain of the experimental class, which is greater than the control class. Future research could use different methods to determine what methods are most effective.
Performance Analysis Of Machine Learning Algorithms Using The Ensemble Method On Predicting The Impact Of Inflation On Indonesia's Economic Growth
Authors: Abdulloh Ferian Fauzi , Aminuddin Afrig , Rahardi Majid , Harianto Fetrus Jari
Published: February 2025

The warning of a global recession expected in 2023 is currently the world's concern. Global financial institutions have raised interest rates to lower inflation, which has led to this problem. This study aims to evaluate the effect of interest rates and inflation on Indonesia's economic growth and compare the performance of machine learning models, specifically Random Forest and XGBoost, in analyzing the impact of inflation. A qualitative methodology was used for the literature survey, while the quantitative approach involved the implementation of machine learning algorithms using the Ensemble Method. The results show that Random Forest performs better than XGBoost in predicting the impact of inflation on economic growth, with MSE values of 0.799 and 0.864 and MAE of 0.576 and 0.619, respectively. In addition, the R-squared value of Random Forest 0.908 is also higher than that of XGBoost 0.901, indicating that the model can better explain the variation in the target data. The practical implication of this study is that the Random Forest model can be more effectively used in analyzing the impact of inflation on Indonesia's economic growth. Recommendations for future research include exploring other methods and using more extended time series to deepen the understanding of the relationship between interest rates, inflation, and economic growth.
Why People Benefit from Online Learning: Empirical Assessment from Jordan
Authors: Gharaibeh Malik Khlaif , Gharaibeh Natheer Khleaf
Published: February 2025

Most countries have imposed online learning on universities and schools due to the COVID-19 pandemic. These days, despite the end of the impact of the COVID-19 pandemic on the educational sector, many countries in the world are still adopting this type of education and trying to develop its methods due to the many benefits it provides. The main objective of conducting this study is to determine the main factors affecting the acceptance of online learning in Jordan. The data were analyzed using SmartPLS 4. 940 questionnaires were distributed in Irbid and Amman. The study's results supported the hypotheses, as it was found that the acceptance of e-learning is statistically and positively associated with the four variables. This study provides essential guidelines for decision-makers and those in charge of the educational process, as it supports the body of knowledge with new variables that were not used in previous studies. Online learning is considered inevitable for adoption in universities and schools, especially when looking at the benefits that institutions derive from its adoption. Saving time, effort, and costs are the most important benefits when applying online learning. This study attempted to determine the main factors affecting the acceptance of online learning in Jordan. The study's results aligned with the hypotheses that technological development, women's empowerment, disabilities, and environmental benefits significantly affect the acceptance of online learning. This study presents a new model and theoretical framework that researchers in this field can build upon.
Enhancing Contactless Respiratory Rate Measurement Accuracy: Integration of 24GHz FMCW Radar and XGBoost Machine Learning
Authors: Arisandy - , Erfianto Bayu , Setyorini -
Published: February 2025

Advancements in non-contact vital sign monitoring are crucial for enhancing patient measurements' accuracy and overall patient experiences. This research explores the integration of 24GHz Frequency-Modulated Continuous-Wave (FMCW) radar with the XGBoost machine learning algorithm to improve the detection of respiratory rate (RR). This innovative approach offers a promising alternative to traditional contact-based methods. The study utilizes FMCW radar to detect respiratory motion, while signal patterns are analyzed using XGBoost to ensure accuracy across various healthcare environments. The method involves collecting signals, pre-processing to remove noise and irrelevant data, and extracting features to be analyzed by the XGBoost algorithm. The collected dataset, which includes controlled and randomized respiratory rates from a diverse subject pool, establishes a solid basis for the algorithm's training and validation, ensuring extensive adaptability and precision. Empirical results show that XGBoost surpasses other machine learning models' accuracy and reliability. Importantly, this method significantly reduces error margins compared to established benchmarks, leading to substantial improvements in RR measurement. The implications of this study are wide-ranging, indicating that such a system could significantly enhance patient care standards by providing continuous, accurate, and non-intrusive monitoring, especially in settings where traditional methods are impractical or uncomfortable. Future research should aim to refine the system's real-world applicability, assess long-term reliability, and optimize the technology for integration into existing healthcare frameworks, thereby further transforming the landscape of patient monitoring technologies.
Syllable Segmentation with Vowel Detection on Verse Quranic Recitation
Authors: Setiyaningsih Timor , Azmi Mohd Sanusi , Draman Azah Kamilah
Published: February 2025

In speech recognition, segmentation involves partitioning a continuous audio signal containing speech into smaller units or segments, such as words, phonemes, or syllables. This process is paramount in speech recognition systems, as it delineates the boundaries between distinct speech elements, facilitating subsequent analysis and processing. Segmentation accuracy significantly impacts speech recognition systems' overall precision and performance, enabling more precise identification and processing of individual speech units. Moreover, proper segmentation empowers the automatic speech recognition (ASR) system to distinguish between different syllables or words effectively, leading to more efficient speech recognition outcomes.  This research paper investigates the importance of vowel detection for syllable segmentation in speech recognition, particularly in Arabic speech, such as the Quran, where changes in each syllable can alter the meaning. While existing techniques only consider pronunciation by different readers, this study employs onset detection to account for the presence of Arabic vowels. Specifically, the study focuses on detecting the onset of the recitation of Surah Al-Fatihah's fourth verse using 50 data sets in the syllable detection testing process. The results indicate that syllable detection performs excellently on syllables with /a/ and /i/ vowels. However, syllables with /u/ vowels produce results below 70%. The study suggests that the onset-based method is ideal for syllables with the presence of /a/and /i/ vowels, demonstrating the importance of considering Arabic vowel letters in speech recognition.
Enhancing Land Management through U-Net Deep Learning: A Case Study on Climate-Related Land Degradation in Berembun Forest Reserve in Malaysia
Authors: Chew Yee Jian , Ooi Shih Yin , Mohd-Razali Sheriza , Pang Ying Han , You Lim Zheng
Published: February 2025

In the face of accelerating climate change, effective management of land resources needs innovative technological approaches. This study, conducted in the Berembun Forest Reserve, Jelebu, Malaysia, leverages advancements in geospatial technology and machine learning to address the pressing issue of land degradation, focusing on forested areas vulnerable to landslides. Utilizing high-resolution Unmanned Aerial Vehicle (UAV) imagery, the U-Net convolutional neural network model is employed for the precise classification and early detection of landslide-induced land degradation. Through a systematic analysis of 15 high-quality UAV images of 5472 x 3647 pixels, segmented into 256 x 256-pixel patches, the U-Net model demonstrated remarkable accuracy, achieving a mean Intersection-over-Union (IoU) of 0.9466. These findings underscore the model's potential to significantly enhance land management practices by providing timely and cost-effective landslide detection. Adopting such deep learning techniques is a pivotal shift towards more sustainable and resilient land management strategies in the era of climate change. This research showcases the practical application of machine learning in environmental monitoring and paves the way for future innovations. Implications for further research include integrating additional spectral bands, addressing environmental variability, and expanding applications across diverse landscapes to improve environmental monitoring, conservation efforts, and resilience strategies. Developing automated frameworks for real-time data processing and model deployment could further revolutionize the field, enabling more responsive and efficient land management practices.
Real-Time Digital Assistance for Exercise: Exercise Tracking System with MediaPipe Angle Directive Rules
Authors: Sim Kok Swee , Wong Shun Wei , Low Alex , Yunus Andi Prademon , Lim Chee Peng
Published: February 2025

This paper focuses on developing an exercise tracking system capable of recognizing simple exercises, such as push-ups, pull-ups, and sit-ups, with high accuracy, leveraging human pose estimation techniques to enhance prediction performance. Exercise tracking can help users to perform workouts correctly and improve overall physical and mental health. The system utilizes the HSiPu2 dataset for training and evaluation, employing MediaPipe as the human pose estimation input and a Multi-Layer Perceptron (MLP) model for exercise recognition. Initially, a baseline MLP with three layers was implemented, followed by an improved expand-shrink MLP architecture designed to enhance model performance. The results demonstrate that the expand-shrink MLP model has achieved a 16% higher accuracy than the baseline, showcasing its effectiveness in accurately recognizing simple exercises based on pose estimation data. This advancement highlights the potential of the model to support a broader range of exercise types, offering a robust solution for monitoring workouts. The system provides meaningful feedback to users by ensuring accurate exercise recognition and promoting safe and effective physical activity. Future research can explore integrating this system with real-time feedback mechanisms, enabling users to receive immediate corrections during workouts. Expanding the dataset to include diverse exercise routines, including complex and dynamic movements, could enhance the system’s applicability. These developments would pave the way for more comprehensive and practical exercise-tracking solutions, supporting individuals to maintain a healthy lifestyle and improving the accessibility of fitness technologies.
Datasets for Artificial Intelligence-based Spine Analysis: A Scoping Review
Authors: Zaim Ahmad Muhammad Shahrul , Ab. Aziz Nor Azlina , Siong Lim Heng , Ghazali Anith Khairunnisa
Published: February 2025

The advancement of artificial intelligence (AI) and intense learning is key to automating the diagnosis and inspection of spinal-related pathologies. This automation reduces the need for human manual analysis. Reducing the burden on the healthcare system and the risk of human error. Spine medical images have several modalities, such as X-rays, computed tomography (CT), and magnetic resonance imaging (MRI). Each modality captured the vertebral features differently. The choice of modality affects the performance of the applied algorithms. It is also important to note that a large amount of data is better for training AI algorithms, profound learning algorithms. However, medical images are often limited owing to privacy concerns and the lack of open-source databases. Therefore, it is essential to identify data sources to ensure the success of AI projects for spine analysis. This review discusses available datasets and their characteristics, such as modality, size, and labels.  Additionally, the demographics and applications of the data were also discussed. The platform utilized to obtain related literature in this study is Lens. A scoping review was used in this study to extract information from related literature. The number of literature included in this study is 39. A total of 43 datasets, which include 32 private and 11 public datasets, are discussed in this review. This work will benefit researchers and developers developing an AI-based spinal analysis system.
Improved Fuzzy Possibilistic C-Means using Artificial Bee Colony for Clustering New Student’s Financial Capability to Determine Tuition Level
Authors: Satriyanto Edi , Surya Wardhani Ni Wayan , Anam Syaiful , Mahmudy Wayan Firdaus
Published: February 2025

Outliers in the dataset will affect the quality of the cluster, so a good clustering method is needed. Based on the Mahalanobis distance method, it is known that the research dataset has outliers. Clustering methods that are often used for this type of data are Fuzzy C-means (FCM), Possibilistic C-means (PCM), and Fuzzy Possibilistic C-means (FPCM). This study aims to develop a clustering method that is more robust to outliers by using the Artificial Bee Colony (ABC) algorithm to minimize the objective function of FPCM. This study produces a new algorithm called Artificial Bee Colony Fuzzy Possibilistic C-Means (ABCFPCM) so that the resulting clusters are not easily trapped in the local optimum. This study also provides cluster centroid initialization using K-Means++ to improve cluster quality. ABCFPCM performs best because it significantly increases the Silhouette value and the Between Sum Squares (BSS) and Total Sum Squares (TSS) ratio. ABCFPCM performance provides the best cluster quality of 72.16% based on the BSS/TSS ratio, FPCM of 70.71%, and FCM K-Means++ of 68.14%. K-Means++ in the cluster method does not affect cluster performance except for FCM, where cluster quality is slightly increased. The centroid results of 8 clusters as the best performance of ABCFPCM are used to determine the tuition rate level. The impact of this study is to improve the quality of FPCM performance because it is no longer trapped in a local optimum at the cluster centroid.
Enhanced U-Net Architecture for Glottis Segmentation with VGG-16
Authors: Aldi Febri , Yuhandri Yuhandri , Tajuddin Muhammad
Published: February 2025

Laryngeal endoscopic image analysis with segmentation techniques has great potential in detecting various diseases in the glottic area, which is essential for early diagnosis and proper treatment. This study proposes developing the U-Net architecture by integrating the VGG-16 model, aiming to improve the accuracy in detecting glottic areas. VGG-16 is applied to the encoder and bridge sections so that the model can take advantage of previously learned knowledge. This modification is expected to improve segmentation performance compared to standard U-Net, especially in handling variations in laryngeal image complexity. The dataset used consisted of 1,200 images taken randomly from the BAGLS website, a collection of laryngeal endoscopic image data rich in variation. The training results show that the standard U-Net produces an accuracy of 0.9995, IoU 0.6744, and DSC 0.7814. The improved U-Net showed a significant performance improvement, with an accuracy of 0.9998, an IoU of 0.8223, and a DSC of 0.9153. This improvement confirms that modifying the U-Net architecture using VGG-16 provides superior results in detecting glottic areas precisely. VGG-16 also helps model performance in overcoming the problem of smaller datasets. In addition, both models were tested using relevant evaluation metrics, and the test results showed that the improved U-Net consistently outperformed other CNN-based segmentation methods. These advantages show that the proposed approach improves accuracy and contributes significantly to developing glottic disease detection methods through laryngeal endoscopic image analysis, which can ultimately support clinical practice in detecting abnormalities in glottis more effectively.
An Investigation of the Student Learning Satisfaction Model for User Story Learning in Software Engineering Course
Authors: Zul Muhammad Ihsan , Mohd. Yasin Suhaila , Sahid Dadang Syarif Sihabudin
Published: February 2025

Software engineering courses are essential for students to become professional software engineers. These courses expose them to their first user stories (US). Despite extensive studies on US-related issues, quality remains the most prominently discussed topic. Therefore, it is essential to investigate US education in higher education to produce qualified software practitioners. In the educational context, such investigations are typically measured using the learning satisfaction approach. This study aims to investigate the suitability of the learning satisfaction model in software engineering courses, specifically in the US context. Subsequently, the study will identify opportunities for improving US teaching methods. The applied learning satisfaction model consists of four factors: perceived ease of use, perceived usefulness, learning motivation, and learning satisfaction. These factors are derived by combining the Technology Acceptance Model (TAM) and Learning Motivation Theory. The study employs Confirmatory Factor Analysis (CFA) using the partial least squares structural equation modelling (PLS-SEM). The measurement model and model evaluation fit stages are used to assess the suitability of the implemented learning satisfaction model. The structural model examines opportunities for improving the US teaching method based on the identified factors. The study involves 142 software engineering students as respondents. The results indicate that the current model requires adjustments in indicators and model fit, particularly SRMR and NFI, to align with the study. Regarding learning enhancement, the factors of perceived ease of use and perceived usefulness suggest that improvements in US teaching methods are necessary to increase student learning satisfaction in US learning.
The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification
Authors: Rahmi Nadya Alinda , Defit Sarjon , Okfalisa -
Published: February 2025

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.
Named Entity Recognition in Medical Domain: A systematic Literature Review
Authors: Kusuma Selvia Ferdiana , Wibowo Prasetyo , Abdillah Abid Famasya , Basuki Setio
Published: February 2025

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.
Diagnosis of Diseases in Rubber Stems Using the Dempster Shafer Method
Authors: Sukmono Yudi , Pratiwi Sinthya Ayu , Hatta Heliza Rahmania , Septiarini Anindita , Padmo Azam Masa Amin , Wijayanti Arini
Published: February 2025

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.
Data-Driven User Personas in Requirement Engineering with NLP and Behavior Analysis
Authors: Liang He , Muhammad Sufri , Zainudin M.N. Shah
Published: February 2025

As technology rapidly evolves, software development faces growing complexity, requiring adaptation to dynamic user expectations. This study addresses a critical gap in the existing literature by integrating behavioral data and sentiment analysis into the user persona development process within the requirement engineering framework. The primary objective is to create more accurate and representative user personas that better guide software design and development. To achieve this, the research employs advanced Natural Language Processing (NLP) techniques to systematically analyze extensive behavioral and sentiment data collected from social media platforms. The integration process involves segmenting user data into behavioral patterns and emotional states, which are then synthesized to develop nuanced user personas. These personas are expected to significantly improve the accuracy of user requirements, leading to enhanced software performance, increased user satisfaction, and greater development efficiency. The target application area for this research is mobile telecommunications, where precise user understanding is critical. The results indicate that this approach not only refines the traditional persona method but also addresses the evolving needs of users more holistically. By advancing the methodology for user-centered design, this study contributes to the broader field of requirement engineering. Future research will validate and refine this approach across diverse domains, ensuring its adaptability and effectiveness in different contexts. This paper thus has the potential to make a significant impact on how user personas are developed and utilized in software engineering.
Improving Data Reliability Assessment in ETL Processes through Quality Scoring Technique in Data Analytics
Authors: Atika Razali Nor Famiera , Baharom Salmi , Abdullah Salfarina , Admodisastro Novia Indriaty
Published: February 2025

The foundation of a relevant and accurate data analysis is reliable data. Technique and measurement are essential to evaluate current data quality regarding reliability and establish a baseline for ongoing improvement initiatives. Without tools or visualizations, data engineers may find it challenging to monitor and maintain the reliability of the massive data from the extraction, transformation, and loading (ETL) data load process. Data reliability assessment is a helpful technique in analyzing the quality of data reliability and information on the present state of data before commencing any analytics. The proposed technique hinges on the metric and measurement defining data reliability and the dashboard platform where the integration with the user in dictating the weight of data and the final output, which is the final data reliability score, will be projected. The score obtained affirms whether improvements are needed on the data or if an organization can proceed with data analytics. The technique considers the data extraction, transformation, and loading (ETL) procedures used to gather datasets. Data significance or weight was determined according to the analytics needs and preferences, indicating an acceptable score for generating insights. Ultimately, when utilizing the data reliability assessment metrics technique, we are credited with an overall picture of our data’s reliability aspect, as only one look is offered based on the intended analysis. This new approach boosts the confidence among data practitioners and stakeholders, especially those relying on findings generated from data analysis. Furthermore, the overview assists in enhancing the current state of data, where the derived score helps identify possible areas of improvement in the ETL process. Accuracy and efficiency assessment of the proposed technique also showed positive feedback in measuring the method in measuring the reliability of data.
A Theoretical Framework of Knowledge Management Systems on Quality Management Systems
Authors: Nizam Mohammad Mohammad Fakhrul , Abdullah Rusli , Ab. Jabar Marzanah , Haizan Nor Rozi Nor , Mohd Nur Nurhayati
Published: February 2025

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.
Information Behavior Model of e-Health Literacy for Online Health Information-seeking Effectiveness
Authors: Xuewen Wang , Azmi Murad Masrah Azrifah , ZhangLi Wu , Ismail Ismi Arif , Mohamed Shaffril Hayrol Azril
Published: February 2025

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.
Validating a Quality Model through Expert Review for Green Information Systems
Authors: Muhammad Shireen , Jusoh Yusmadi Yah , Haizan Nor Rozi Nor , Jussupbekova Gulzat Tyrysbekovna , Baidibekova Aidin Orisbayevna
Published: February 2025

Software developers use a quality model as a guide to help them determine the quality factors of a software product they are designing. This study aims to validate a quality model for green information systems, or green IS, towards achieving eco-sustainability. Thus, this study aims to identify quality factors for green IS that will contribute to eco-sustainability. This study's methodology included two rounds of expert evaluation with four experts. Two strategies were deployed in each round to discover the quality factors. In the first round, the strategy was to take existing software quality factors and interpret them in the context of eco-sustainability. In the second round, existing eco-sustainability goals were adopted and clustered into categories; in this study’s context, quality factors were aligned with the eco-sustainability goals. An initial model consisting of 35 quality factors was synthesized from a green IS design framework, sustainable software, and social media literature. The experts presented and assessed the model in the first round. Thus, 18 quality factors have been selected for the next second-round review. Five quality factors— accuracy, completeness, accessibility, customization, and collaboration emerged from the second-round review. Each quality factor was aligned to the eco-sustainability sub-goals of eco-efficiency, eco-equity, and eco-effectiveness, which resulted in the development of a proposed model. The experts concluded that the revised model could be employed in a data collection survey since it closely resembles the green IS quality model for eco-sustainability.