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 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.
 

Purpose: This research aims to develop a system for detecting the credibility of information on social media X by classifying tweets as credible or non-credible. Additionally, it seeks to improve the accuracy of classification and prediction of information credibility using feature extraction methods, semantic features, feature expansion, and optimization.
Methods: The system is built using a deep learning approach with Long Short-Term Memory (LSTM), Term Frequency-Inverse Document Frequency (TF-IDF), Robustly optimized BERT Approach (RoBERTa), Global Vector (GloVe), and Particle Swarm Optimization (PSO). The dataset consists of 54,766 Indonesian tweets from social media X, focusing on the 2024 General Election and using several keywords such as ‘Pemilu 2024’, ‘Pilpres 2024’, ‘anies baswedan’, ‘Prabowo’, ‘#GanjarPranowo’, and ‘#debatCapres’.
Result: The results of this study show that the highest accuracy achieved is 89.09% using LSTM with an 80:20 data split, baseline unigram, RoBERTa, Top1 corpus IndoNews, and PSO of the LSTM model’s hyperparameters, resulting in a highly significant statistical improvement of 0.96% over the baseline model.
Novelty: This research contributes to information credibility classification research using RoBERTa to add semantic features and GloVe to expand features by utilizing a built corpus and finding similar words to connect with these expanded features. Additionally, PSO is applied to find the optimal hyperparameters, thereby improving the performance and accuracy of the LSTM classification model.

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.

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.

Purpose: This research aims to develop a classification model using histogram-based gradient boosting to identify relevant contextual tweets about disasters. This model can then be used for subsequent data cleaning stages.
Methods: This study uses a semi-supervised approach to develop a classification model using histogram-based gradient boosting. The model is trained to identify and remove irrelevant tweets that are related to disasters and gathered from Twitter. Optimization techniques, such as the AdaBoost classifier, calibrated classifier, and self-training classifier, are used to enhance the model's performance. The goal is to accurately recognize and categorize relevant tweets for additional data analysis and decision-making.
Result: The classification model that has been developed has achieved a high F1-score of 93.07%, which indicates its effectiveness in filtering disaster-related tweets that are relevant. This highlights the potential of the model to enable more precise aid distribution and faster decision-making in disaster response efforts. The successful implementation of the model also demonstrates its usefulness in utilizing social media data to enhance disaster management practices.
Novelty: This research contributes to the analysis of social media through machine learning algorithms. By utilizing social media, specifically Twitter, as a valuable resource for disaster response efforts, this study tackles challenges related to data collection and analysis in disaster management. The classification of relevant tweets into different types of natural disasters offers opportunities to enhance stakeholder decision-making processes in disaster scenarios.

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 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.

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.

This study explores the challenges experienced encountered among basketball players in tournament sports participation. The study employed a descriptive-exploratory case study approach. The researcher used semi-structured interviews and non-participatory observation to collect data from six basketball players who met the researcher's criteria. The finding’s revealed that the participants were challenged during tournament sports participation. The participants pointed out that during sports tournament participation, the participants revealed that some of their struggles were physical restraints, health conditions, such as injuries, not enough resting time, in-game pressure, and lack of communication. Results revealed that the presence of pressure, miscommunication of the team, and health conditions are their highly faced struggles during the game. The findings have proven that the challenges experienced by athletes undeniably affect their performance, which is also influenced by how they perceive situations in a competitive sports environment. This study highlights the importance of addressing both physical and mental health concerns to enhance player performance and team success in competitive sports environments.