This study focuses on the development and implementation of an enterprise-level information system for State Universities and Colleges (SUCs) in the Philippines, specifically addressing the mandates of Instruction, Research, and Extension. The study adopts a sequential exploratory mixed-method approach, utilizing the Agile System Development Model for system development. The system's effectiveness and acceptability were evaluated using quantitative data from 20 IT experts and 100 end-users, and qualitative data from interviews and secondary data. The study also conducted a survey to assess the system's acceptability in terms of flexibility and configuration. The findings reveal that the system received an average weighted mean of 3.44 for flexibility and 3.39 for configuration, indicating a good level of acceptability among end-users. The study also identifies several strategic implementation strategies for the deployment of the system to interested SUCs, including policy integration and risk management. The study provides valuable insights into the development and implementation of enterprise-level information systems in educational institutions, highlighting the importance of aligning digital governance with institutional mandates and requirements.

Energy consumption significantly increased in recent decades, notably at the household level, due to economic development, rising population, and technological advancements. To address this sustainability concern, peer-to-peer energy sharing service (P2PESS) is introduced as a solution to household level energy needs. However, P2PESS has yet to be fully explored in terms of development and adoption. As such, this study attempts to provide an understanding of the adoption intention on artificial intelligence (AI) in P2PESS a developing country. This study is realized by developing an extended adoption intention model analyzed through partial-least squares - structural equation modeling (PLS-SEM). Results show that attitude is the most significant predictor of AI in P2PESS adoption intention. This study also reveals that the trust dimension has the strongest effect on attitude, while attitude toward use has the strongest effect on behavioral intention. Also, this study confirms ease of use and usefulness as critical factors in adoption intention. Meanwhile, AI-anxiety is the least significant predictor in the model. Finally, this study is the first evidence of AI in P2PESS adoption intention from the perspective of household level users.

This project was pushed through to engage people towards proper waste collection, through the utilization of mobile devices of the communities in different municipalities. The study aims to develop and implement a sustainable and efficient waste management collection system by informing the residents of the garbage truck collection schedule available on their mobile devices. Additionally, the platform utilizes optimization and decision support algorithms, including queuing algorithms, to receive and review complaints efficiently. The researcher employed an incremental software development methodology, allowing the software to be developed and tested even when requirements are still evolving. The study is descriptive-correlational, as it involves evaluating the developed system based on feedback from expert respondents. The evaluation yielded an overall mean performance score of 4.75, interpreted as “Strongly Agree,” indicating that the system is well-prepared for deployment.

Sustainability in transportation technologies is growing in all parts of the world through electric and micro-mobility sharing services. As such, there is a need to explore the factors that influence its adoption and use behavior. However, this is relatively underexamined in many developing countries. This study attempts to understand the intention and use behavior of micro-mobility as a service (MaaS) in the Philippines, a developing country. This study used survey data, and analysis was performed using partial least squares and structural equation modeling (PLS-SEM). Results show that performance expectancy is the strongest predictor of intention, while satisfaction is the least significant predictor. Factors such as social influence, price value, and habit have a positive effect on intention. Overall, the predictive model is explained by the coefficient of determination, revealing that behavior intention, satisfaction, and use behavior have large predictive relevance. This study provides theoretical and practical implications for further micro-mobility research in the future.

This study explores the beneficial uses of Microsoft Copilot as a support tool for Baliwag Polytechnic College instructors' lesson planning and activity design. Researchers evaluate the influence of Copilot on the creation of instructional content by examining the experiences and opinions of educators. The study demonstrates the advantages, difficulties, and opportunities for customization that come with incorporating Copilot into the curriculum. The results indicate that Copilot can significantly improve the effectiveness and caliber of lesson design, but also highlight certain implementation issues. This research offers insights into the future of technology-enhanced education and contributes to the expanding body of research on AI-assisted teaching strategies.

This study enabled the researcher to create a graduate tracer monitoring platform. It aimed to provide a centralized channel to monitor institutions' graduates in terms of their job employment, to assess academic programs using modified instruments so necessary interventions may be provided, and to provide a matching algorithm that can be used both by industry partners and respective alumni. This study employed a Decision Support System and mapping recommendation analysis using a rule-based algorithm to evaluate the results of alumni program evaluation on five areas or dimensions, namely curriculum, faculty, facility, laboratory, and student services. It sets the threshold to determine if the results of the areas mentioned above are beyond the passing rate and implements the interventions for each area. The content management system was also used in this study to change the contents of the Alumni Program Evaluation, the interventions, the threshold, and many more. Based on the results, no intervention must be implemented in all areas/dimensions since the mean and the composite mean were more than the 4.0 threshold that was set in the proposed system. The overall rating of the respondents using the technology acceptance model numerical rating is 4.42 with an interpretation of “Agree.” As observed all criteria are rated either agree or strongly agree which indicates a high standard has been set in the development of the system. This means that the system is ready for deployment.

This capstone project aims to create a visual book with an augmented reality feature and a CMS-based website. The application uses augmented reality technology to track target images on the book and display 3D models and animations of the 30 endangered bird species in the Philippines. To elaborate, when a target image is found, the application will display the 3D model of the bird and two different animations for the user to explore. The application was developed using Vuforia and Unity; the 3D models of the endangered bird species will be modeled and animated using Autodesk Maya, Substance Painter, and ZBrush, and the design and layout of the book were created using Adobe Photoshop and Adobe InDesign. The developed system is one of the first AR books about endangered bird species in the country, attempting to disseminate information and raise awareness about the status of endangered birds using augmented reality. To prove that the application is practical and usable, the researchers surveyed 70 respondents consisting of ten from the client’s organization, 40 from the general public, ten bird lovers, and ten I.T. professionals. Based on the survey results, the system proves to be practical and usable in disseminating information and raising awareness about the status of endangered birds. Future researchers can improve the system by adopting some features and enhancing the application so that users can still utilize the mobile application even without the visual book. The researchers also encourage future researchers to implement the application in other devices with different operating systems, such as iOS and Windows, to cater to a broader range of users.

Relative humidity is an important environmental parameter and is widely used in various fields. Prediction of humidity levels is crucial for climate modeling, heat stress, air quality forecasting, and public health. Machine learning techniques have shown potential for predicting humidity due to their nonlinear nature. However, there is a research gap in humidity prediction in the Philippines, specifically the lack of studies utilizing the available parameters provided by PAGASA, presenting an opportunity for further investigation and development of models for predicting humidity levels in the country. In this study, the researchers used a publicly available dataset from PAGASA containing weather measurements from 2000 to 2022 in the Philippines. Various machine learning models were trained and tested, with hyperparameter tuning performed using Bayesian optimization. The Gaussian Process Regression model with optimized hyperparameters achieved the best performance in predicting relative humidity, with the lowest RMSE and highest R-squared values. This study provides a reliable way to predict humidity levels in the Philippines based on weather parameters.

E-commerce plays an essential role in selling products or services online because it can reach more customers than traditional retail. If the customer data is appropriately mishandled, it disrupts the business’ data organization and poor customer relationship management. The study focuses on creating an e-commerce website that efficiently handles the data and integrates a personalized hybrid recommender system. Content-based and collaborative filtering methods were used in the recommender system to improve customer relationship management, streamline procedures, organize inventory and sales, and increase profits. Sales forecasting using ARIMA was also added to use the customer data for efficient business decisions. ISO 9126 was the software quality model used to evaluate the developed system using the software quality characteristics functionality, usability, maintainability, and efficiency. The system got an overall mean score of 4.57, which is excellent, which means the system can perform smooth transactions from ordering up to the checkout and organized products, sales, and inventory. The integration of the recommender systems was able to give recommendations based on the customer's preferences, which enhances the user experience that may lead to an increase in sales of the business since the suggestions are tailored recommendations to the users.

Wind power is a challenge in power generation. The tortuous process stages in generating voltage become a significant problem to be solved properly. One indicator of the process is the determination of the right wind speed because it always changes at any time and under circumstances. For this reason, accurate predictions are needed so as to maintain the smooth integration of wind power into the overall system. Machine learning is used as a promising approach to dealing with wind intermittent power because wind speed prediction methods have been developed in recent years. This study explores climate patterns in the Philippines using data collected from PAGASA. The data is trained and tested with a machine learning model to predict wind speed. This research resulted in the Gaussian Process Regression (GPR) model outperforming other models and is very suitable for datasets in achieving accurate and reliable predictions.