Rice is a vital staple crop and an essential component of the diet in many Asian nations. Over the years challenges have arisen in rice farming due to the vulnerability of rice crops to attacks by more than 100 types of insects which can cause significant yield losses. This research focuses on predicting rice bug infestations in rice fields using data mining and machine learning techniques. Several machine learning algorithms including Decision Tree, Optimizable Discriminant, Naïve Bayes, Support Vector Machine, K-Nearest Neighbors, Ensemble, and Neural Network, were employed to analyzed patterns and build predictive models for rice pest infestations. The dataset spans from 2005 to 2019 and includes historical data, field observations, and interviews with agriculturists and farmers. The study aims to enhance understanding of the factors influencing rice bug infestations and to develop timely alert systems to minimize potential crop losses. Model performance was improved through the fine-tuning of hyperparameters, with training and evaluation conducted using an 80:20 data split and 10-fold cross-validation. The KNN and Ensemble models achieved the highest accuracy rates at 98.3%. These findings provide valuable insights for agricultural institutions and farmers in selected municipalities of Region IVA and may also be adapted for other crops with similar pest challenges using comparable parameters.

This study applies machine learning (ML) algorithms within the Knowledge Discovery in Databases (KDD) framework to analyze Bachelor of Science in Information Systems (BSIS) graduate tracer study data. Two core tasks were addressed: (1) predicting structured graduate outcomes, such as employment status, and (2) classifying sentiments from textual curriculum feedback. Data selection, preprocessing, and transformation prepared structured and unstructured inputs for supervised ML experiments conducted in Python 3.x using Google Colab, with scikit-learn supporting classical algorithms and TensorFlow/Keras enabling deep learning deployment. Algorithms evaluated included Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, and Long Short-Term Memory (LSTM) networks. Results showed that Logistic Regression achieved the best performance for predicting employment status, with 74.19% accuracy, precision of 0.82, and the highest Kappa score (0.54), outperforming all other models. Gradient Boosting emerged as the most effective approach for sentiment analysis, attaining balanced results across recall (0.497), F1-score (0.481), and Kappa (0.441). Insights from confusion matrices revealed that Logistic Regression excelled at distinguishing employed graduates but struggled with underemployed and unemployed categories, while Gradient Boosting strongly identified neutral sentiments but faced challenges in detecting negative feedback. The findings highlight that algorithm selection should be task-specific: Gradient Boosting is more effective for sentiment analysis, whereas classical models like Logistic Regression are better suited for structured prediction. A hybrid framework combining both is recommended to enhance tracer study systems, enabling richer, data-driven insights for curriculum evaluation, institutional planning, and graduate outcome monitoring.

Marine ecosystems face unprecedented threats from pollution, overfishing, and climate change, creating an urgent need for conservation initiatives. While consumer awareness of ocean degradation is increasing, there remains a persistent gap between environmental concern and actual purchasing behavior toward sustainable products. This study aims to examine public readiness to adopt OceanGuardian, a sustainable e-commerce platform for marine conservation products, by integrating behavioral, technological, and environmental perspectives. Using a modified Extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, survey data from 600 respondents were analyzed with machine learning models, including Support Vector Machines, Random Forests, and XGBoost, to identify key determinants of consumer intention and use behavior. Results indicate that social influence, performance expectancy, affordability, and habit formation significantly predict adoption, with Support Vector Machines achieving the highest predictive accuracy (92.5%). The findings highlight the potential of artificial intelligence to enhance consumer behavior analysis while recognizing challenges such as economic barriers and consumer skepticism. The study offers theoretical contributions by extending UTAUT2 with environmental factors and provides practical insights for policymakers and businesses to design strategies that foster sustainable shopping and strengthen marine conservation efforts.

Social class inequality is the stratification of classes based on wealth, income, influence, and access to resources. Individuals who are categorized as low class face unequal access to basic needs, inaccessible health services, deterioration of mental health, entry into the poverty cycle, and crime. As suggested by the relative references, the factors of social class in the Philippines are underemployment, unemployment, and income inequality. In alignment with the Sustainable Development Goal (SDG) 10 entitled Reduced Inequalities, the proponents' objective is to raise awareness towards social class inequalities in the Philippines by creating a 3D first-person action investigation game entitled Overdrive to showcase a premise based on the experiences of the lower class and its possible solutions. The development used the SCRUM development cycle methodology. The game is accompanied by a Content Management System-based website to promote the game materials. To gather the data, the proponents conducted beta testing among IT students of the FEU Institute of Technology, and a few external technical and non-technical individuals. The testing was followed by a Likert scale questionnaire which gathered the satisfaction level of the game and assets, integration of the study within the story, and the website. A weighted average mean was utilized to evaluate the data. The overall data resulted in a mean of 4.35 which interprets a ‘Satisfied’ rating towards the created game and conducted project.

Blood banking is the process of collecting, separating and warehousing blood. There are numerous file-based repositories of blood bank management that exist for storing data for blood bank ecosystem such as hospitals and centers. This functions for maintaining the information of donors, availability of blood, and transaction information. Currently, these systems are effort intensive, costly, and failed to achieve efficiency in terms of its filtering mechanism which makes repository penetrating faster and reliable. This paper introduces a new design for blood banking ecosystem with proper filtering solution using frame-based approach. The system has three major features: (1) blood camp setup module, (2) stocks management module which includes the blood donation and blood releasing, and (3) the filtering system module which shows the nearest blood camp with the available blood type based on the patients’ needs. Also, with the use of frame-based approach as filtering method, the system is more efficient and reliable compared to other blood banking repository systems. The system’s functionality was tested for its efficiency, usability, and reliability and the results are revealed in the survey. Conclusions and future work were also provided in this paper.