Color blindness affects approximately 8% of the global population, emphasizing the importance of early detection. The conventional Ishihara Test had several limitations, such as paper degradation, low interactivity, and difficulties in maintaining children’s focus. This research introduces AR-VISION, an Augmented Reality-based Ishihara Test application designed for inclusivity and child-friendliness. Employing a Research and Development approach with the Model Development Life Cycle (MDLC), AR-VISION was developed and evaluated through six main phases. The technical evaluation showed Algorithm C achieved the best compromise between speed, accuracy, and memory usage. User testing with 35 elementary school students indicated a significant increase in accuracy (from 72% to 91%) and engagement (from 60 to 87). In conclusion, AR-VISION enhanced the precision, interactivity, and motivation of children in color blindness screening, supporting SDG 3, 4, and 9 and Asta Cita No.4, while demonstrating the transformative potential of AR in health, education, and interactive learning

This study proposes a Long Short-Term Memory (LSTM) model to predict the hatching success of sea turtle eggs in the Anambas Islands Marine Conservation Area, Indonesia. Leveraging nesting data (2022–2024) provided by LKKPN Pekanbaru and associated environmental variables, the model’s performance was assessed across various configurations of time steps (2, 5, 7, 30, and 45 days) and data splits (ranging from 60:40 to 90:10). The optimal configuration—7-day time step with a 60:40 train-test split—yielded RMSE = 17.90, MAE = 8.67, and R² = 0.34. Results revealed strong seasonal nesting trends and statistically significant interspecies differences in incubation periods (p < 0.05). While the model demonstrated high predictive accuracy for standard incubation durations (30–45 days), performance declined in extreme cases, highlighting the need for location-specific environmental data. This research illustrates the practical application of LSTM for ecological time series forecasting and provides a machine learning framework to support decision-making in ecotourism scheduling and marine conservation planning in island-based coastal ecosystems.

This systematic literature review analyzes machine learning approaches for mobile phone price prediction based on device specifications through a comprehensive examination of 25 research studies from 2018 to 2024.The review reveals that ensemble methods, particularly Random Forest (achieving up to 97% accuracy) and Gradient Boosting (R² = 0.9829), consistently outperform individual algorithms across various datasets. Support Vector Machine models demonstrate superior classification performance with 96-97% accuracy, while neural networks show perfect best-performer ratios but remain underutilized (4.88% of implementations). The following keywords were used in this systematic review's extensive search strategy across IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar: ("mobile phone price prediction" OR "smartphone price prediction") AND ("machine learning" OR "artificial intelligence") AND ("specifications" OR "features") AND ("classification" OR "regression"). Strict inclusion/exclusion criteria were used to select 25 studies from an initial pool of 45 studies, with an emphasis on empirical research with quantitative performance metrics published between 2018 and 2024. The study reveals RAM, internal memory, battery capacity, and processor specifications as the key determining features for mobile phone pricing. According to the study, the primary factors influencing mobile phone pricing are processor specifications, RAM, internal memory, and battery capacity. This review identifies critical research gaps, including insufficient neural network exploration, poor dataset reporting practices (52% of studies omit dataset sizes), and lack of real-time market dynamics integration. The findings provide evidence-based guidance for researchers, manufacturers, and consumers in selecting optimal prediction algorithms and understanding key price-determining features in the evolving smartphone market. Study limitations include geographic bias toward specific markets represented in available datasets, limited access to proprietary datasets, and a primary focus on specification-based features that exclude market sentiment analysis

The use of persuasive language is one of the defining features of native advertisements. Therefore, detecting persuasive content in news is essential, since native ads often appear disguised as legitimate news articles, it is crucial to identify and filter such content to maintain objectivity and improve the user experience. This study aims to detect news with persuasive content i.e. persuasive news in English language using a natural language processing (NLP) approach. The proposed method incorporates text summarization methods, pre-trained word embeddings, and deep learning models. An additional Conv1D layer has been added to improve the model’s performance. The model were trained on an Indonesian news dataset translated into English using Google Translate API. Experimental results show that our proposed RoBERTa–BiLSTM-Conv1D model, outperformed other models, achieving 92% accuracy in identifying persuasive news in English. These findings indicate that the persuasive content detection model can be used for application in mainstream media environments to detect native ads in English language. In the future, the model can incorporate Indonesian and English news as training data to develop a cross-lingual native ads detection model

Accurate extraction of Population, Intervention, Comparison, and Outcome (PICO) elements from clinical texts is essential for supporting evidence-based medicine, particularly in cardiology where clinical data complexity presents significant challenges. This study investigates the comparative effectiveness of three contextual embedding models—BioBERT, PubMedBERT, and SciBERT—integrated with a Bidirectional Long Short-Term Memory (BiLSTM) architecture for multi-label PICO element classification on heart disease datasets. The experimental framework involved pre-processing clinical sentences, transforming them into contextual embeddings, and classifying PICO elements using BiLSTM-based sequence modeling. Evaluation was conducted using five key metrics: accuracy, precision, recall, F1-score, and hamming loss, supplemented by confusion matrix analysis for each PICO element. Results demonstrate that the BioBERT-BiLSTM model achieved superior performance, with an accuracy of 73.89%, F1-score of 78.54%, precision of 81.60%, and recall of 76.64%. PubMedBERT-BiLSTM exhibited the highest precision (84.12%) but lower recall, while SciBERT-BiLSTM produced slightly inferior results overall. These findings confirm the importance of using domain-specific embeddings, particularly models pre-trained on biomedical corpora, to improve classification accuracy in specialized clinical text tasks. This study concludes that the BioBERT-BiLSTM combination offers a reliable approach for automated PICO element extraction in the cardiology domain, contributing to the development of more accurate and efficient clinical decision-support systems

Native advertising is often difficult to detect because it resembles regular news articles. One indicator is the absence of diverse information sources or the reliance on a single perspective. Therefore, it is necessary to employ an extraction technique capable of consolidating various forms of identical entity mentions. This study integrates an NER model based on XLNet+BiLSTM+CRF with identical entity classification using Levenshtein distance features and static and contextual vector representations. The results show an F1-score of 93.71% at the entity level and 92.84% for identical entity identification, along with a list of unique citation sources. These findings demonstrate that this unique list can be an additional feature in detecting native advertising, which often relies on a single source. With an average unique entity coverage of 97.40%, the proposed architecture can extract unique entities within news articles

Machine learning has the potential to support hematologists in classifying leukemia by identifying abnormal chromosomes and specific gene markers. One effective technique for feature selection is Variable-Length Particle Swarm Optimization (VLPSO), where its performance depends heavily on parameter control, specifically the inertia weight (w) and acceleration factors (c), which regulate the search process. In previous VLPSO, static types of parameter control were applied to the Factor, and time-varying types were used by the Factor. Although its results showed good performance in VLPSO, there was no separation in the treatment of training data and test data, leaving a gap in understanding their impacts for real-world applications. This study explores how different parameter control strategies (static, time-varying, and adaptive) affect the performance of VLPSO with two comparison adaptive parameter control approaches, Adaptive 1 and Adaptive 2, in the VLPSO framework, each designed to dynamically adjust the control parameters w and c in different ways. The 10-fold cross-validation shows that VLPSO with an Adaptive one-parameter setting achieves better generalization with low train-test differences, especially in Decision Tree and Naïve Bayes classifiers, though with higher variability. Adaptive 2-parameter setting of VLPSO offers more consistent results with narrower variability across different settings. Static methods are the least reliable, while time-varying controls show moderate but unstable performance. Adaptive parameter tuning is recommended to improve VLPSO's stability, flexibility, and classification accuracy in biomedical applications. The results provide recommendations for parameter settings using an adaptive approach that has been proven to enhance the performance of VLPSO

This study aims to develop an automatic essay answer assessment system based on Natural Language Processing (NLP) to reduce the time and effort required for evaluation. The system uses Cosine Similarity and Manhattan Distance as evaluation metrics and implements two text embedding methods—Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW)—to represent the user’s answer text. The methodology begins with text processing and pre-processing, followed by embedding and similarity calculation between the user’s answer and the reference text to generate an evaluation score categorized into three levels: good, sufficient, and poor. Based on Cohen’s Kappa analysis, the kappa value for Cosine Similarity reaches 0.78, indicating high agreement between the Cosine TF-IDF and Cosine BoW methods. In contrast, Manhattan Distance yields a kappa value of -0.05, indicating a discrepancy between the two Manhattan-based methods. The evaluation results suggest that Cosine Similarity is more suitable, whereas Manhattan Distance is not relevant for this task. At the modeling stage, the best classification models are Decision Tree and Random Forest, each achieving an accuracy of 96.67%. Although Random Forest demonstrates a higher AUC than Decision Tree, it requires a longer training time. Overall, the system is considered effective for assessing essay answers with both purpose and consistency, offering potential applications in the field of education

This study aims to develop an automatic essay answer assessment system based on Natural Language Processing (NLP) to reduce the time and effort required for evaluation. The system uses Cosine Similarity and Manhattan Distance as evaluation metrics and implements two text embedding methods—Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW)—to represent the user’s answer text. The methodology begins with text processing and pre-processing, followed by embedding and similarity calculation between the user’s answer and the reference text to generate an evaluation score categorized into three levels: good, sufficient, and poor. Based on Cohen’s Kappa analysis, the kappa value for Cosine Similarity reaches 0.78, indicating high agreement between the Cosine TF-IDF and Cosine BoW methods. In contrast, Manhattan Distance yields a kappa value of -0.05, indicating a discrepancy between the two Manhattan-based methods. The evaluation results suggest that Cosine Similarity is more suitable, whereas Manhattan Distance is not relevant for this task. At the modeling stage, the best classification models are Decision Tree and Random Forest, each achieving an accuracy of 96.67%. Although Random Forest demonstrates a higher AUC than Decision Tree, it requires a longer training time. Overall, the system is considered effective for assessing essay answers with both purpose and consistency, offering potential applications in the field of education