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.

Amidst the clear-cut changes constantly happening in the educational landscape, Higher Education Institutions (HEIs) are continuously pursuing graduates that meet global standards. The rise of remote jobs from previous years opened a gateway of opportunities for Filipino graduates to ensure employment from various multinational employers. To maintain this, HEIs in the Philippines must be able to offer quality education and programs that meet exceptional standards. This study aims to address the inability of tertiary institutions to track the competencies that the students have gained by integrating the outcome-based education (OBE) framework through an online platform. This paper also enumerates the benefits of having an OBE Management system such as achieving a holistic view of evaluating students' competencies, the system integrates educational data from various sources such as grading system, Learning Management System (LMS), and surveys. The system development research process is conducted in this study. One of the objectives of this study is the integration of drill-down analysis into the OBE Management system. This allows users to create reports easily and faster, furthermore, it aids the country in achieving Sustainable Development Goal (SGD) 4 for Quality Education. The premise of the study also contributes to the impact of system development on attaining quality education for HEIs.

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.

Sustainability is a present concern in many developing countries, where the role of the household is pivotal in realizing its benefits. This study aims to explore artificial intelligence in green energy technologies (AIGET) adoption intention among household-level respondents selected in the National Capital Region (NCR), Philippines. The study has 446 respondents and analyzed using partial least squares and structural equation modeling approaches (PLS-SEM). Among the factors tested, results revealed that perceived usefulness is the strongest predictor of AIGET adoption intention. Factors such as usefulness, ease of use, subjective norms, and perceived risk have a positive effect on attitude. This confirms that attitude has a positive impact on behavioral intention on AIGET. Finally, this study shows that household-level participants have a positive interest in adopting AIGET, considering its usefulness and ease of use. This study presents useful theoretical and practical contributions to further its uptake in the Philippines and other developing countries.

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.

Part of speech tagging (POS) is crucial in natural language processing, identifying the grammatical categories of words in sentences. This research highlights the lack of focus on POS tagging for Asian languages, particularly Cebuano. Limited research on Cebuano has hindered linguistic documentation and understanding of its grammar and vocabulary. This study introduces a Cebuano POS tagger using the Hidden Markov Model (HMM) to improve Cebuano text processing. The researchers also propose a method for handling unfamiliar words. Results show the algorithm performs well on a news text corpus of 25,000 datasets, with an accuracy of 84 %, precision of 80%, recall of 81.52%, and F1-score of 82%. These outcomes demonstrate the algorithm's effectiveness in addressing language challenges in specific genres. Additionally, the research contributes to the Sustainable Development Goals (SDGs) by promoting linguistic diversity and fostering inclusive language technologies. The study provides insights into Cebuano's linguistic traits and grammatical structures, offering a foundation for further research in natural language processing.

To ensure students achieve timely and satisfactory graduation, it's essential to assess their future performance based on ongoing academic records and implement instructional interventions. Within the educational context, students fall into two categories: regular and irregular, each governed by distinct academic regulations. Regular students follow a predetermined curriculum, which provides a clear path to graduation and enhanced access to required courses, facilitating efficient progress toward degree completion. On the other hand, irregular students encounter challenges such as disruptions and delays, necessitating additional time and support to fulfill degree requirements. Guiding both regular and irregular students and improving their study plans require appropriate guidance and academic intervention. To address the existing research gap, this study presents a Criteria-based Recommender Platform for Achieving Optimal Time-to-Graduation Utilizing a Backward Chaining Algorithm. This platform automatically generates a personalized study plan by considering predefined criteria and parameters, enabling students to evaluate the timeline for completing their degree program. By leveraging the backward chaining algorithm, the platform's predictive model captures intricate relationships and dependencies within the data, providing valuable insights and predictions. This adaptive approach continuously refines predictions based on new data, enhancing accuracy and utility in guiding decision-making processes related to study plan generation.

The difficulty with manually analyzing and processing data is that it takes too long, prone to degradation and other problems. Government agencies cannot reduce cases due to a lack of sufficient knowledge and analysis to determine the fundamental trend of RTI cases. Besides, the general public is utterly uninformed of road situations. Roadcast, a Vehicle Incident Management System, is a practical approach to minimizing road incidents. This study presents the automation of the manual process of collecting and analyzing incident data and transparency with the general public. The output of the study will be a web application system that will produce descriptive analytics, hot spots, and forecasting (7MA), supporting the PNP’s preparation for the projected future incident cases in the following week and determining the hazardous area in Pasig City through the hot spots. The general public will be informed of the number of road incidents in a particular barangay, entire cases in Pasig City, and other data visualizations integrated into the dashboard. Scrum Methodology was used to construct the system, and numerous users with varying responsibilities and administrator permissions were necessary to access and control the system. Alpha and Beta Testing examined the system’s functionality, usability, reliability, performance, and supportability. The researchers used purposive sampling to survey (11) technical and (49) non-technical respondents. The researchers received a score of 4.74, which translates to “Strongly Agree.” The system received positive feedback from both technical and non-technical respondents.

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.

In the context of higher education, ensuring timely and successful graduation for students is a pivotal objective, necessitating a comprehensive understanding of their academic performance and tailored interventions. Evaluating ongoing academic records is crucial for effective pedagogical interventions, but limited research on student performance in completing degrees has introduced challenges. To address these, academic institutions are adopting flexible curricula designs, prompting the need for diverse course offerings. Amidst this, two student categories emerge: regular and irregular, each presenting unique challenges. A Rule-Based Suggestion Platform for Academic Program Completion was conceptualized, designed, developed, and rigorously evaluated through the use of the Technology Acceptance Model (TAM). This innovative platform, which harnesses the power of rule-based decision-making, was created to address the intricate challenges surrounding students' timely and successful program completion within the academic landscape. The platform's underlying architecture and functionality were crafted to provide students with personalized and optimized recommendations, guiding them towards informed decisions in shaping their educational journey. The development process involved the integration of advanced rule-building mechanisms, enabling the system to analyze individual student profiles, academic progress, and program requirements. This data-driven approach empowers the platform to generate customized study plans that not only consider the students' academic ambitions but also adhere to predefined constraints and parameters. By evaluating the platform's performance through the Technology Acceptance Model, this study assesses the users' perception and acceptance of this novel tool, shedding light on its effectiveness and potential impact on enhancing the academic planning process.