This article presents a systematic review of pragmatic competence in second language acquisition (SLA), emphasizing its critical role in effective communication and intercultural understanding. Pragmatic competence encompasses the ability to understand and use language appropriately in diverse social and cultural contexts, including mastering speech acts, politeness strategies, conversational implicatures, and intercultural pragmatics. Despite its importance, pragmatic competence often receives less attention in language instruction compared to grammar and vocabulary. The review highlights the effectiveness of explicit and implicit teaching methods, authentic interaction, and cultural immersion in developing pragmatic skills. It also underscores the role of technology, such as digital platforms and simulations, in enhancing learners’ ability to navigate real-life communication scenarios.
The findings reveal significant challenges, including cultural variability in pragmatic norms, difficulties in standardizing assessments, and the influence of learners’ individual differences. The study calls for innovative teaching and evaluation methods, focusing on personalized approaches and the integration of pragmatic learning with emotional intelligence. By synthesizing current research and practices, this article provides valuable insights for educators, curriculum developers, and researchers, advocating for adaptive, culturally informed, and technology-supported strategies to enhance pragmatic competence in SLA.

The elderly are an age group that experiences the aging process with various physical, mental and social changes that affect their quality of life. As the number of elderly people increases in various countries, it is important to have a clear understanding of the scope of the needs and conditions of the elderly from a social, health and economic perspective. The aim of this research is to determine the scope of the needs and conditions of the elderly. This research method uses a qualitative approach and involves literature study, data collection techniques using interview methods and literature research. This research discusses the health needs and conditions of the elderly, with a focus on medical aspects of care, including ongoing care and management of chronic conditions (such as diabetes, cholesterol and high blood pressure). Elderly people also experience several challenges, such as accessing health care facilities which is often difficult due to distance, physical limitations, or dependence on family for care. Therefore, researchers hope to provide a deeper understanding of the challenges and needs of the elderly, as well as families spending more time providing care.
 

This study aims to analyze the impact of mind mapping on reading comprehension skills among elementary school students (SD/MI). In this journal, the approach used is a literature review by analyzing various relevant sources related to the influence of mind mapping on reading comprehension skills in SD/MI. This method aims to collect, evaluate, and summarize existing information in the literature, including journal articles, books, and credible online sources. The results of the analysis indicate that students who utilize mind mapping experience a significant improvement in their reading comprehension abilities. These findings suggest that mind mapping can be an effective strategy for enhancing students' reading comprehension by helping them organize information visually and facilitating a more interactive learning process. This study recommends that teachers consider incorporating mind mapping into their teaching practices to improve the quality of reading comprehension among students in SD/MI.

The system of information and communication in Islamic education in the digital era enables the more effective dissemination of Islamic values. Technology supports interactive learning, but challenges related to information verification still exist. This study integrates the values of the Qur'an to improve the quality of Islamic education and ensure the accuracy of information. Using a qualitative approach and literature review, the study analyzes the concepts of information and communication systems in Surah Al-Kahfi 13, Ali Imran 110, and Al-Hujurat 6 through scholarly journal articles. The results show: QS. Al-Kahfi 13 emphasizes the importance of truth, ethics, and morality in the Islamic education information system. Islamic education should wisely utilize technology to spread correct knowledge, prioritize spiritual values, and encourage positive change. QS. Ali Imran 110 calls for the use of modern technology, such as Qur'an apps and interactive videos, to convey Islamic values in an engaging way. QS. Al-Hujurat 6 highlights the importance of information verification to prevent hoaxes and ensure valid religious information through proper digital literacy.
 

Chicken Nuggets   is   a   processed   product   made   from   ground   meat   with   several   additional ingredients and moulded in various shapes. Innovatio in the use of breadfruit which has nutrients in the form of carbohydrates, fiber, protein, vitamins, and so on so that this food product has the potential to be of choice materials in chiken nugget product. Meanwhile,  Lampisang  Village,  Peukan Bada District,  Aceh Besar Regency,  as  a  location  for Community  Service  activity. The activities carried out were counseling and demontration on chicken nuggets with addition buah sukun. From this activity it can beconcluded that the implementation through community service processing chicken meat with addition buah sukun into nuggets, service participants can increase their knowledge and skills  in diversifikasi chicken meat. The implementation of the activity has been successful and has receiveda good response.

Purpose: This study explores diabetic retinopathy (DR), a complication of diabetes leading to blindness, emphasizing early diagnostic interventions. Leveraging Macular OCT scan data, it aims to optimize prevention strategies through tree-based ensemble learning.
Methods: Data from RSKM Eye Center Padang (October-December 2022) were categorized into four scenarios based on physician certificates: Negative & non-diagnostic DR versus Positive DR, Negative versus Positive DR, Non-Diagnosis versus Positive DR, and Negative DR versus non-Diagnosis versus Positive DR. The suitability of each scenario for ensemble learning was assessed. Class imbalance was addressed with SMOTE, while potential underfitting in random forest models was investigated. Models (RF, ET, XGBoost, DRF) were compared based on accuracy, precision, recall, and speed.
Results: Tree-based ensemble learning effectively classifies DR, with RF performing exceptionally well (80% recall, 78.15% precision). ET demonstrates superior speed. Scenario III, encompassing positive and undiagnosed DR, emerges as optimal, with the highest recall and precision values. These findings underscore the practical utility of tree-based ensemble learning in DR classification, notably in Scenario III.
Novelty: This research distinguishes itself with its unique approach to validating tree-based ensemble learning for DR classification. This validation was accomplished using Macular OCT data and physician certificates, with ETDRS scores demonstrating promising classification capabilities.

Purpose: Investment is the allocation of funds with the aim of obtaining profits in the future. An example of the investment instruments with high returns and high risks are stocks. The risks associated with the investment can be reduced by forming a portfolio of quality stocks optimized through mean-variance (MV). This is necessary because successful selection of high-quality stocks depends on the future performance which can be determined through accurate price prediction.
Methods: Stock price can be predicted through the adoption of different forms of deep learning methods. Therefore, BiLSTM and LSTM models were applied in this research using the stocks listed on the LQ45 index as case study.
Result: The utilization of LSTM and BiLSTM models for stock price prediction produced favorable outcomes. It was observed that BiLSTM outperformed LSTM by achieving an average MAPE value of 2.1765, MAE of 104.05, and RMSE of 139.04. The model was subsequently applied to predict a set of stocks with the most promising returns which were later incorporated into the portfolio and further optimized using the Mean-Variance (MV). The results from the optimization and evaluation of the portfolio showed that the BiLSTM+MV strategy proposed had the highest Sharpe Ratio value at k=4 compared to the other models. The stocks found in the optimal portfolio were BRPT with a weight of 19.7%, ACES had 16.9%, MAPI 11.8%, and BMRI at 51.6%.
Novelty: This research conducted a novel comparison of LSTM and BiLSTM models for the prediction of stock prices of companies listed in the LQ45 index which were further used to construct a portfolio. Past research showed that the development of portfolios based on predictions was not popular.

YouTube is one of the most widely used social media sites in Indonesia, with 139 million users. YouTube provides users with the freedom to comment on existing videos with very diverse sentiments. This research aims to determine and describe the sentiments of YouTube social media users regarding the presidential candidacy of Ganjar Pranowo and Prabowo Subianto in the 2024 general election. This research uses mixed methods with Mozdeh application to collect data and analyze sentiment on YouTube social media. It shows that the sentiment of YouTube social media users towards Ganjar Pranowo tends to be positive with a score of 2 (weak) with a percentage value of 11.26% and negative sentiment with a score of 2 (weak) with a percentage value of 1.35%, and Prabowo Subianto tends to be positive with a score of 2 (weak) with a percentage value of 10.40% and negative sentiment with a score of 2 (weak) with a percentage value of 1.44%. Trends from Ganjar Pranowo and Prabowo Subianto on YouTube tend to fluctuate. Apart from that, the two supporters will continue to vote for the two presidential candidates even though there are issues affecting them. 

Purpose: This study aims to analyze public sentiment towards the Indonesian Electoral Commission (KPU) performance and evaluate a modified Convolutional Neural Network (CNN) model effectiveness in sentiment analysis.
Methods: This research employs several methods to achieve its objectives. First, data collection was conducted using web crawling techniques to gather public opinions on the performance of the Indonesian Electoral Commission for the 2024 elections, with a specific focus on platform X. A total of 5,782 data points were collected and underwent preprocessing before sentiment analysis was performed. This study uses the CNN method due to its exceptional ability to recognize patterns and features in text data through its convolutional layers. CNN is highly effective in sentiment analysis tasks because of its ability to capture local context and spatial features from text data, which is crucial for understanding the nuances of sentiment in comments. The modified CNN model was then trained and evaluated using a labeled dataset, where each comment was classified into positive, negative, or neutral sentiment categories. Modifying the CNN model involved adjusting its architecture and parameters, as well as adding layers such as batch normalization and dropout to optimize its performance. The effectiveness of the modified CNN model was assessed based on metrics such as classification accuracy, precision, recall, and F1 score. Through this methodological approach, the research aims to gain insights into public sentiment towards the KPU performance in the 2024 elections and to evaluate the effectiveness of the modified CNN model in sentiment analysis.
Result: The research revealed several significant findings. Firstly, most comments expressed concerns regarding performance aspects of KPU’s, including transparency, fairness, and integrity. Neutral sentiment dominated the discourse, with approximately 23.66% of comments conveying dissatisfaction or skepticism towards KPU's handling of the elections. Additionally, sentiments expressed on social media platform X mirrored those found across other platforms, indicating a consistent perception of KPU performance among users. Furthermore, the evaluation of the modified CNN model demonstrated a substantial improvement in accuracy, achieving an impressive 93% accuracy rate compared to the pre-modification model's accuracy of 77%. These results suggest that the modifications made to the CNN model effectively enhanced its performance in sentiment analysis tasks related to KPU performance during the 2024 elections. These findings contribute to a deeper understanding of public sentiment toward KPU performance and underscore the importance of leveraging advanced technology, such as modified CNN models, for sentiment analysis.
Novelty: This study contributes novelty in several ways. Firstly, it provides insights into public sentiment towards the performance of the KPU during the 2024 General Elections, which is crucial for understanding the perception of democracy in Indonesia. Second, the study employs a mixed-methods approach, combining web crawling techniques for data collection and a modified CNN model for sentiment analysis, which offers a comprehensive and advanced methodology for analyzing sentiments on social media platforms. Thirdly, the evaluation of the modified CNN model demonstrates a significant improvement in accuracy, indicating the approach's efficacy in analyzing sentiments related to KPU performance. This study offers valuable contributions to academic research and practical applications in sentiment analysis, particularly in democratic processes and institutional performance evaluation.

Globally, the prevalence of anemia in pregnant women in 2019 was 35.6%. The prevalence of anemia in developing countries is relatively high, such as Southeast Asia at 47.8%, compared to developed countries such as America at 18.9%. Efforts that can be made to prevent anemia include education, giving pregnant women biscuits and giving Fe tablets. This activity was carried out at the Bangun Rejo Village Health Center, Dusun VII, Tanjung Morawa District. The method used in this service is to provide education directly to pregnant women at the Bangun Rejo Village Health Center. The results of this activity were carried out in stages starting from preparation to evaluation. After implementing health education, giving pregnant women biscuits and giving Fe tablets, there was an increase in knowledge and consumption of Fe. Therefore, with this community service project, it is hoped that all pregnant women can increase awareness and Fulfill nutrition and balanced nutrition, regularly consume Fe tablets.