Browsing by Author "Bangug, Cristy M."

Now showing 1 - 6 of 6
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    Enhancing math learning through gamification:
    (Don Mariano Marcos Memorial State University – Mid La Union Campus, 2025-11) Concubierta, Anthony Geoff Angelo S.; Boado, Elijah Nathan T.; Campos, Jobert F.; Madrio, Marben Antony L.; Patacsil, Joseph A.; Bangug, Cristy M.; Rodriguez, Marylen D.; Marquez, Jansen Paul R.
    This study developed Number Hunt, a Roblox-based educational game designed to enhance Grade 7 mathematical proficiency through gamification. Utilizing the Agile-XP methodology, the researchers created interactive mechanics for three key topics: sets, polynomials, and geometry. Evaluation involved seven experts and 30 Grade 7 students using the Game-Based Learning Approach (GBLA) and Game Experience Questionnaires (GEQ). Expert GBLA results indicated "Good Acceptability" across Knowledge (3.00), Perception (2.81), and Attitude (3.11), validating its pedagogical value. Student GEQ results reflected a "Positive Experience" (mean: 4.19), with a high Challenge score (4.31) signaling strong engagement and motivation. These findings demonstrated that Number Hunt effectively promoted student involvement and learning pleasure by successfully integrating curriculum-aligned goals with immersive gameplay. By balancing challenge and education, the platform served as a viable tool for improving mathematical learning outcomes.
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    FINEWISE:
    (Don Mariano Marcos Memorial State University – Mid La Union Campus, 2025-05) Baliguat, John Rey F.; Florendo, Rolielyn B.; Laña, Richard Justine P.; Pimentel III, Florencio L.; Malamion, Edelvar A.; Novencido, Denver A.; Bangug, Cristy M.; Flores, Florida
    The study developed Finewise, an intelligent financial planning and budgeting system for the barangay Tanquigan city of San Fernando, La Union. Finewise answered the following objectives: to determine the existing process of financial planning in Barangay Tanquigan; to develop an financial planning and budgeting system using RAD; and to determine the usability of the developed system using the System Usability Survey. There were a total of 10 respondents who evaluated the level of acceptability' of the system using System Usability Survey. The result of the SUS got a mean rating of 90.75 which was interpreted as acceptable, meaning all the features of the system work exactly as intended.
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    MedShelf:
    (Don Mariano Marcos Memorial State University – Mid La Union Campus, 2025-05) Dulay, Rachelle Anne Y.; Maddela, Ma. Geannele L.; Miranda, Joshua C.; Obaldo III, Perfecto A.; Sardeng, Shekainah Kim S.; Estira, Marydel C.; Bangug, Cristy M.; Catbagan, Jocelyn R.
    This study designed and developed MedShelf, a web-based information system aimed at addressing operational inefficiencies in inventory and patient record management at the San Juan Municipal Health Office and streamline manual processes, minimize data entry errors, and enable real-time tracking and easy access to essential health records. Employing both descriptive and developmental research designs, the study adopted the ScrumBan methodology to iteratively design and refine the system in alignment with user needs and stakeholder feedback. MedShelf was developed using appropriate programming languages and frameworks, incorporating features such as real-time inventory monitoring, automated low-stock and expiration alerts, and a usercentric interface. The system's quality and effectiveness were evaluated based on ISO 25010 system quality standards, focusing on dimensions such as functional suitability, reliability, usability, and security. Results demonstrated that MedShelf significantly improved the efficiency and reliability of the health office's operations.
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    Modified median filtering algorithm for image noise reduction.
    (IAEME Publication, 2020-10) Bangug, Cristy M.; Fajardo, Arnel C.
    Median filtering caters image noise reduction while serving quality image outputs. Some of the few benefits of using median filtering are input noise values removal in a huge scale and noise reduction with an average or high dynamic resolution as it does the job of eliminating high density of noise. This paper presents a Modified Median Filtering Algorithm for Image Noise Reduction. The performance parameters criterion metrics Peak Signal-to-Noise-Ratio (PSNR), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) were used to justify simulations. Based on the results obtained, it shows that the Modified Median Filtering Algorithm has a clearer and detailed output.
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    SearchScam:
    (Don Mariano Marcos Memorial State University - Mid La Union Campus, 2024-12) Daz, Blanca Camille N.; Carig, Hannah Grace T.; Culaton, Jomar S.; Urbi, Rey F.; Malicdem, Alvin R.; Bangug, Cristy M.; Estira, Marydel C.; Sapuay-Guillen, Sheena I.
    The growing prevalence of online scams makesthisstudy explored the development of a sentiment-analysis-based system designed to classify online gambling platforms as either legitimate or fraudulent. By employing advanced tools such as the Google Places API. 1VHOIS API. and OpenAI’s natural language processing models, the system effectively analyzed user feedback. metadata, and social media trends to detect scam indicators. Following the CRISP-DM framework, rigorous methodologies for data collection, preprocessing, and analysis were applied, resulting in a system that provided clear and interpretable credibility scores. Validated through the System Usability Scale (SUS) with a score of 71.07, the research highlighted the pivotal role of sentiment analysisin processing unstructured data and enhancing cybersecurity efforts. This work contributed to combating online fraud by offering a practical tool that bridges technological innovation with user safety, fostering trust and security in the digital age.
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    VISIONARICE:
    (Don Mariano Marcos Memorial State University - Mid La Union Campus, 2025-11) Licudine, Aaron Jay C.; David, Maria Luisa E.; Madayag, Janvic B.; Sobredo, Irene P.; Patacsil, Joseph A.; Novencido, Denver A.; Bangug, Cristy M.; Coloma, Leah P.
    This study aimed to enhance rice crop health monitoring in La Union by developing a web-based system that applied machine learning for the early prediction of rice leaf diseases. It provided farmers and agriculture staff with timely and reliable insights to reduce yield losses and support sustainable crop management in the region. The researchers followed the CRISP-DM methodology, which included business understanding, data understanding, data preparation, modeling, evaluation, and deployment. System development was guided by the Extreme Programming (XP) model, enabling iterative prototyping with continuous user feedback. A convolutional neural network (CNN) using the ResNet architecture was trained on rice leaf images to classify common diseases such as leaf streak and blast, and it was integrated into a web-based application for real-time diagnosis and accessible information. Usability testing with 40 respondents, including farmers, agriculture staff, and IT experts, yielded high System Usability Scale (SUS) scores, indicating excellent usability, effectiveness, and strong user satisfaction.