Communities in DSpace
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Recent Submissions
Online employee health record management system for bureau of fire protection regional office I
(Don Mariano Marcos Memorial State Univeristy - South La Union Campus, 2023-04) Tusino, Art Justice G.; Adones, Sheila Mae R.; Gagaza, Elaine Grace M.; Krizza D. Lagtay; Quezada, Ryan S.
Nowadays, almost all organizations had their own health record management
systems to ensure a secure and standardized work environment. However, the purpose of
this study was to technically aid the Regional Health Service Division in managing
employee health records.
This study is focused on developing and designing an online employee health
record management system for the Bureau of Fire Protection Regional Office I, using
descriptive and developmental research methods. The objectives were to identify the
functional and non-functional requirements of the system, to develop the system using
Rapid Application Development (RAD), and to evaluate the system's level of usability with
the end-users.
The developed system was evaluated in terms of usefulness, information quality,
and interface quality, through the use of the Post-Study System Usability Questionnaire
(PSSUQ) questionnaire. The result oj the survey accumulated an average weighted mean
of 1.27, 1.40, 1.10 accordingly, which implied that the developed system was extremely
usable.
MySMSAe student portal
(Don Mariano Marcos Memorial State Univeristy - South La Union Campus, 2023-04) Gomez, Beverly F.; Agsaoay, Mark Anthony S.; Biagtan, Jomel V.; Ringor, Quiendy B.; Tablazon, Jaspher O.
A portal provides an engaging student experience with a single point of access
and hub to all application, service, information and content of the school.
This study aimed to dewlap MySMSAe student portal for Saint Mary of the Sea
Academy. Specifically. it aimed to determine the level of access of the end-users of
MySMSAe student portal; build and test MySMSAe student portal using the DevOps
Lifecycle; and evaluate the student portal using the Computer System Usability
Questionnaire. The researchers employed descriptive and developmental research to
properly organize the presentation of data.
Sentiment analysis of customer tweets related to philippine internet service providers
(Don Mariano Marcos Memorial State Univeristy - South La Union Campus, 2023-04) Acenas, Jennifer C.; Casela, Jericho A.; Cerenado, Armalyn B.; Estacio, Anthony N.
The researchers conducted a sentiment analysis on customer tweets which are
related to the two chosen Philippine internet service providers to determine the company's
efficiency in delivering the best to their customers. It aimed to determine the preprocessing
techniques to be used on the extracted dataset; to train the sentiment analysis model using
machine learning algorithms; and to evaluate the performance of the models in terms of
accuracy.
The research design of this study was experimental approach. The researchers
gathered tweets as data by using RapidMiner and Twitter API Three algorithms were used
in this study: Naive Bayes with Laplace Smoothing, Support Vector Machine with Kernel
Types and K-Nearest Neighbor with K-values as parameters. Both corpora showed that
Support Vector Machine with kernel type Radial Basis Function had the highest accuracy
and was concluded to be the best algorithm and parameter. Internet Service Provider 1
has higher accuracy with 0.8581 while Internet Service Provider 2 have an accuracy score
of 0.7027 therefore the best corpus was Internet Service Provider 1.
Encryption and decryption algorithms using a substitution cipher and planar graph
(Don Mariano Marcos Memorial State Univeristy - South La Union Campus, 2023-04) Molina, Rose Marie P.; Ramirez, Christian V.; Reyes, Olivia A.
This study employed pure basic research that deals with the formulation of
algorithms for encoding and decoding data using substitution Cipher and planar graph.
The encrypted message was sent either through graph or linear form. Moreover, weight of
the edges and label of vertices of the planar graph were used to enhance the security of the
algorithm created. In addition, a computer program was developed using the Python
computer software based from the formulated encryption and decryption algorithms using
a substitution cipher and planar graph.
As a result, the TeAM EleGATe encryption and TEAM ProTex decryption
algorithms were constructed in terms of its weight of the edges and label of vertices.
Additionally, a computer program was developed using the Python Programming
Language based from the formulated encryption and decryption algorithms.
Ethnicity-inclusive malnutrition detection with convolutional neural network in the philippines
(Don Mariano Marcos Memorial State Univeristy - South La Union Campus, 2024-04) Cerezo, Angelo Gabriel S.; Perez, John Paul Rey B.; Rocacorba, Arjay B.; Soriben, Joy M.; Valdez, Azriel V.
In this study, the researchers developed a deep learning-based malnutrition
detection model for public elementary schools in La Union and Pangasinan. Their primary
objectives were to collect a suitable dataset, apply pre-processing techniques, train a
Convolutional Neural Network (CNN) using AlexNet architecture, and evaluate its
performance. They gathered 400 images from Bigbiga Elementary School and Sta. Rita
West Elementary School, pre-processed, and augmented the data to create 1,200 training
samples, 400 validation samples, and 80 test samples. Techniques like Horizontal Flip,
Brightness Adjustments, Noise Reduction, and Random Rotation were used to enhance
dataset quality. The CNN model trained on this data achieved a 66% test accuracy in
identifying malnutrition. This research provides a foundation for early malnutrition
detection, suggesting future improvements through additional pre-processing techniques,
dataset expansion, and integrating the model into applications for broader lise, potentially
improving public health outcomes