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Sentiment Analysis of College Students’ School Clothing Preferences in the City of Dasmarinas, Cavite

Althea Carlyn Nina R. Baurile, Jona May E. Ymas, Nicole B. Lobri, Rolando B. Barrameda, Maryli F. Rosas, Josephine T. Eduardo, and Emelyn D. Mayuga

College of Science and Computer Studies Studies, De La Salle University – Dasmariñas, City of Dasmarinas, Cavite, Philippines

ABSTRACT

This study explores the sentiments of college students in Dasmariñas, Cavite, regarding mandatory school uniforms and dress code policies. A total of 484 students participated in the survey. Sentiment analysis using three algorithms – Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression – was employed. Among the three algorithms, SVM performed the best. Common positive sentiments on not having a uniform included "freedom," "comfort," and "self-expression" while common negatives were "hassle" and "tiresome." For dress code policies, positive words were "standard," "formal," and "uniformity" while negative words included "picky," "stiff," and "restrictive." The findings provide insights for educational institutions in revising clothing policies to better align with students' preferences and comfort.

Keywords: School clothing preference, mandatory uniform, dress code policies

Cite this Article:

A.C.N.R. Baurile, J.M.E. Ymas, N.B. Lobri, R.B. Barrameda, M.F. Rosas, J.T. Eduardo, and E.D. Mayuga. "Sentiment Analysis of College Students’ School Clothing Preferences in the City of Dasmarinas, Cavite". Journal of Engineering, Computing and Technology, vol. 3 no. 2. Aug., 2025 

Evaluating the Implementation and Outcomes of the Philippines’ Occupational Safety and Health Law

Christian Paolo Lagrana¹ and Nita Dwi Estika²

¹College of Professionals and Graduate Studies, De La Salle University – Dasmariñas, City of Dasmarinas, Cavite, Philippines

²Study Program of Architecture, Faculty of Engineering, Universitas Katolik Parahyangan, Bandung, Indonesia

ABSTRACT

The Occupational Safety and Health Law of the Philippines ensures the protection of every worker against injury, sickness, and accident through safe and healthy working conditions. Most companies in the country continuously provide and increase safety measures to prevent occupational diseases, injuries, and accidents. However, there are still small chances for unpleasant incidents such as encountering risks and hazards in workplaces. Taking into account the extreme level, the International Labour Organization (ILO), in some of the major findings in the latest statistical data on occupational accidents and diseases, recorded that there is still a high rate of documented accidents and diseases. The purpose of the research is to determine and gain a deeper understanding of how the law on occupational safety and health affected the records of occupational injuries, accidents, and diseases and how it contributed to decent work and economic growth. The data it provided discussed how the Philippines made policy and interventions that would enforce a safe and healthy work environment. Through statistical comparison, it is observed that there was improvement before and after the year of implementation of the Occupational Safety and Health Law and how it advances to the development of one of the sustainable development goals – Decent Work and Economic Growth. The paper concluded how the country took a step forward for the progress of occupational safety and health standards since there was documented information on occupational incidents and how it succeeded to the international principles due to the commendation of the International Labour Organization. The Philippine Statistics Authority recorded that there was a decrease in cases of occupational accidents, occupational injuries, and occupational diseases from year 2017 to year 2019. These findings highlight the significant role of strengthened OSH compliance in improving workplace safety. Furthermore, the study discusses how enhanced occupational safety policies support labor protection, improve productivity, and contribute to inclusive economic growth aligned with the Decent Work Agenda promoted by the ILO. The results indicate that the implementation of OSH Law represents a substantial national advancement toward safer workplaces while reinforcing the Philippines’ commitment to international labor standards and sustainable development goals. 

Keywords: Occupational Safety and Health, Sustainable Development Goals, Law, Regulations, Standards, International Labour Organization, Philippine Statistics Authority

Cite this Article:

C.P. Lagrana and N.D. Estika. "Evaluating the Implementation and Outcomes of the Philippines’ Occupational Safety and Health Law". Journal of Engineering, Computing and Technology, vol. 3 no. 2. Aug., 2025

Supervised Machine Learning: An Approach in Predicting the Adaptability Level of Diverse Learners

Maryli F. Rosas and Josephine T. Eduardo

College of Information and Computer Studies, De La Salle University – Dasmarinas, City of Dasmarinas, Cavite, Philippines

ABSTRACT

The COVID-19 pandemic disrupted educational systems globally, prompting a sudden shift from traditional classroom settings to online learning. This transformation significantly affected how students and teachers adapted to new learning environments. This study aimed to examine the adaptability of learners by analyzing their demographic profiles, specifically age, gender, and educational level, and identifying the key factors influencing their ability to adapt to online education. Using supervised machine learning techniques, the researchers developed three predictive scenarios: (1) based on socio-demographic factors (financial condition, internet type, and device used), (2) based on personal demographics (age and gender), and (3) a comprehensive combination of both. Among 13 machine learning models tested, the C5 algorithm emerged as the best-performing model, achieving an accuracy of 89.88%. Results indicate that financial condition, age, and class duration were the most significant predictors of online learning adaptability. These findings highlight the importance of considering socio-economic and educational contexts when designing inclusive and effective online learning environments. 

Keywords: covid-19, online learning, adaptability, machine learning, data mining

Cite this Article:

M.F. Rosas and J.T. Eduardo. "Supervised Machine Learning: An Approach in Predicting the Adaptability Level of Diverse Learners". Journal of Engineering, Computing and Technology, vol. 3 no. 2. Aug., 2025

Training Aid for Performing Yoga Pose Sequences Using Real-Time Pose Estimation Computer Vision

Eduard Patrick D. Naldoza, Miguel Sebastien A. Villanueva, and Karyl Anne Melendres

College of Engineering, Architecture and Technology, De La Salle University – Dasmarinas, City of Dasmarinas, Cavite, Philippines

ABSTRACT

The COVID-19 pandemic and the subsequent lockdowns impacted people’s physical and emotional well-being, with studies showing stress, anxiety, and depression. Yoga offers flexibility, fitness, and psychological benefits but is susceptible to malpractice. The study aimed to develop a training aid that offers personalized feedback on various poses, ensuring a safe yoga practice. Visual feedback gives guides for the poses, while auditory feedback alerts users to changes in the pose and corrects body parts for proper form. Multiple errors in performance are also emphasized for accurate performance. This system employs an open-source pose estimation library and an Application Program Interface (API) to function as a whole. This entire system resulted in a web application that runs locally within the computer and helps aid the user towards more intermediate and advanced skills in yoga. The results from this study showed a mean 97.978% accuracy or 2.022% error in detecting the user’s joint angles during yoga performance in average environmental conditions. The system received a positive review overall, receiving a mean score of 4.6467, as it was found to be functional, reliable, easy to use, significant, and sustainable by the end users.

Keywords: yoga, training aid, web application, computer vision, pose estimation

Cite this Article:

E.P.D. Naldoza, M.S.A. Villanueva, and K.A. Melendres. "Supervised Machine Learning: An Approach in Predicting the Adaptability Level of Diverse Learners". Journal of Engineering, Computing and Technology, vol. 3 no. 2. Aug., 2025

JOURNAL OF ENGINEERING, COMPUTING AND TECHNOLOGY

The Journal of Engineering, Computing and Technology (JECT) is a scholarly peer-reviewed, and academic research journal for engineers, computer scientist, academicians, and research scholars aiming to publish innovative, state of the art results coming from research and advances in different aspects of engineering, architecture, computer science and information technology.  JECT is dedicated to the dissemination of advanced technologies that will be beneficial to professionals and academic researchers. It is a bi-annual publication published by the University Research Office, De La Salle University-Dasmariñas.

The journal is governed by an editorial board whose members specialize in each of the mentioned disciplines.  The editor in chief is elected by the members of the editorial board.

Editorial Board

Editor-in-Chief

Maryli F. Rosas, DIT (Computer Science, De La Salle University-Dasmariñas)

Associate Editor

Ma. Cristina A. Macawile, PhD (Engineering, De La Salle University- Dasmariñas)

Managing Editor

Mr. Jaime Zeus C. Agustin (University Research Office, De La Salle University-Dasmariñas)

Members

Paulino H. Gatpandan, DIT (Computer Science, De La Salle University- Dasmariñas)

Maryjoie A. Lituanas, MSES (Engineering, De La Salle University- Dasmariñas)

Mengvi P. Gatpandan, DIT (Computer Science, Jose Rizal University, Mandaluyong City)

Daniel Dasig Jr., PhD (Field, Change Management Engineer, TELUS Communication Inc., Canada)

Nestor Tiglao, PhD (Electrical and Electronics Engineering Institute, University of the Philippines – Diliman)

Atty. Rudolph Val F. Guarin (Field, FGNG Law Office)

Prof. Sonia M. Pascua (Field, Drexel University, United States of America)

Brojo Kishore Mishra, PhD (Department of CSE, School of Engineering, GIET University, Gunupur, India)

Valentina Emilia Balas, PhD (Automation and Applied Informatics, Intelligent Systems Research, University of Arad, Romania)