Seaweed has emerged as a promising resource in the quest for renewable energy. This study explores the potential of residual carrageenan extract from Eucheuma cottonii as a sustainable feedstock for bioethanol production. Carrageenan, a polysaccharide widely used in food and industrial applications, leaves significant residues after extraction. These residues are rich in fermentable sugars, making them an ideal candidate for bioethanol synthesis. The research investigates the optimal conditions for hydrolysis and fermentation processes to maximize ethanol yield. Results demonstrate that carrageenan residues can produce bioethanol efficiently, presenting an innovative solution for utilizing waste from the seaweed industry. This approach contributes to sustainable energy production while addressing environmental concerns related to seaweed waste.

Sentiment analysis is a powerful tool for transforming consumer feedback into actionable insights, enabling businesses to refine strategies and improve customer experiences. This study evaluates the performance of machine learning models, including Logistic Regression, Random Forest, SVM, LSTM, and BERT, for sentiment classification on a diverse dataset of customer reviews. BERT outperformed other models, achieving an AUC-ROC of 0.97 and an accuracy of 94.2%, showcasing its ability to capture complex semantic patterns in text. The findings provide businesses with critical insights into consumer sentiment, guiding decision-making and enhancing competitive advantage. The study also addresses challenges such as data ambiguity, ethical considerations, and computational demands, offering practical recommendations for implementing scalable and effective sentiment analysis solutions. These results demonstrate the potential of machine learning-driven sentiment analysis in shaping customer-focused business strategies and fostering growth in a data-driven market.

This study investigates the application of machine learning algorithms for predictive analytics in credit risk management, aiming to enhance the accuracy of predicting credit defaults. The research compares multiple machine learning models, including logistic regression, decision trees, random forests, gradient boosting, XGBoost, and LightGBM, using a real-world credit risk dataset. The study focuses on evaluating the models' performance based on metrics such as accuracy, precision, recall, and F1-score. The results show that ensemble models, particularly XGBoost and LightGBM, outperform traditional algorithms in terms of predictive accuracy and computational efficiency, demonstrating their ability to effectively handle complex datasets. The comparative analysis highlights the strengths and weaknesses of each model, providing insights into the trade-offs between interpretability and predictive power. XGBoost and LightGBM are found to be highly effective for credit risk prediction, though challenges such as model interpretability and overfitting remain. The findings suggest that machine learning offers a promising approach for improving credit risk management, with implications for the financial industry to make more informed, data-driven lending decisions. The study underscores the importance of addressing interpretability concerns and data quality issues in real-world applications, paving the way for future advancements in machine learning for credit risk prediction.