UPLB ICS Peak One

The manual pen and paper enrollment process of Libon Community College (LICOM) is tedious, resource exten- sive, and less reliable for its stakeholders. This study developed a pre-enrollment system that automates the enrollment workflow of LICOM to improve their enrollment process. The quality of the system is evaluated through a modified version of the ISO 25010 standard by a sample of students, instructors, clearance signa- tory, and registrar. The SUS, security, performance efficiency, and functional suitability scores of the system are 75.25, 4.17, 4.15, and 4.34, respectively. This result indicates that eLICOM has good software and system quality in terms of usability, security, performance efficiency, and functional suitability; thus, the application is effective in improving the enrollment process of LICOM.

This study investigates the feasibility of using single- stage neural networks, particularly YOLOv7, for automating game recording in over-the-board (OTB) chess tournaments. Traditional manual recording on scoresheets is time-consuming, prompting interest in digital solutions. However, existing dig- ital chessboards are costly. The trained model achieved an mAP@.5:.95 of 0.6328, demonstrating effectiveness close to the state-of-the-art. While it exhibited high accuracy in detecting most pieces, challenges arose with pieces of similar appearances and those positioned farther from the camera. Portable Game Notation (PGN) generation yielded 100% accuracy for standard chess sets, yet smaller sets necessitated adequate lighting to enhance detail for enhanced accuracy.

One of the major problems that education would like to address is the facilitation of learner’s engagement and motivation toward learning. Further challenged by the COVID- 19 pandemic and the boom of numerous technological advances that disrupted the orthodox and unorthodox learning experi- ence. ÆDOM is a web platform that aims to utilize emerging technological paradigms such as the Metaverse and Blockchain to address and provide a reinforced learning environment. This study presents the development, implementation, and assessment of ÆDOM in delivering such service to Senior High School. The application was evaluated using methods such as System Usability Scale (SUS), Intrinsic Motivation Inventory (IMI), and data analytics within the system. It is concluded that the application is highly acceptable and usable while engaging learners’ motivation towards learning as defined in the Self-Determination Theory as its behavioral model. Results from this paper contribute to similar fields of study.

The Philippines is a disaster-prone country that needs to implement technologies to ensure the safety of its people. As such, this study aims to create a community-powered geographic information system in order to monitor ongoing disasters, and classify their severity in order to aid in disaster response. The source of data used were tweets from Twitter. The web program was able to pull data, classify tweets, and display in a Google Map. Further research can be done on two ways, by using other text classifiers besides Naive Bayes, or by implementing other ways of finding location from text such as Named Entity Recognition.

There are countless news articles online that people have many different ways of consuming them (news aggregators, etc.). This study proposes a mobile application that informs the user of information regarding crime, accidents, and natural disaster occurrences of their current location in an easy-to- digest manner– through maps and push notifications. The process includes scraping articles online, categorizing and extracting information using a a finetuned DistilBERT model, storing to a database then displaying on the mobile application. The web scraper and DistilBERT model had an accuracy percentage of -250%, 78.57%, and 100% for natural disaster, crime, and accident respectively. The mobile application had a System Usability Scale score of 81.25 evaluated to ”usable and can be recommended.”