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All Journal Indonesian Journal of Electrical Engineering and Computer Science Advanced Journal of STEM Education (AJOSED) Journal of Healthcare and Biomedical Science (JHBS)
Sherwin B. Sapin
Laguna State Polytechnic University
Biochemistry, Genetics & Molecular Biology Education Immunology & microbiology Medicine & Pharmacology Nursing Public Health Other

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Fire incidents visualization and pattern recognition using machine learning algorithms Jonardo R. Asor; Jefferson L. Lerios; Sherwin B. Sapin; Jocelyn O. Padallan; Chester Alexis C. Buama
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1427-1435

Abstract

A fire incident is a devastating event that can be avoided with enough knowledge on how and when it may occur. For the past years, fire incidents have become a big problem for the Philippines, since it affects the socio-economic growth of the country. Machine learning algorithm is a well-known technique to predict and analyze data. It can also be used to recognize pattern and develop models for artificial intelligence. Pattern recognition through machine learning algorithm is already established and have proven itself accurate in different fields such as education, crime, health and many others including fire incidents. This paper aims to develop a model for recognizing patterns of fire incidents in the province of Laguna, Philippines implementing a machine learning algorithm. With the foregoing project, it is found out that a recurrent neural network shows an astonishing result in terms of pattern recognition. Further, it is also found that Calamba City is the most vulnerable area in case of fire occurrence in the Province of Laguna.
Intelligent aquaculture system for pisciculture simulation using deep learning algorithm Sherwin B. Sapin; Bryan A. Alibudbud; Paulo B. Molleno; Maureen B. Veluz; Jonardo R. Asor
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp561-568

Abstract

The project aims to develop an intelligent system for simulating pisciculture in Taal Lake in the Philippines through geographical information system and deep learning algorithm. Records of 2018-2020 from the database of Bureau of fisheries and aquatic resources IV-A-protected area management board (BFAR IVA-PAMB) was collected for model development. Deep learning algorithm model was developed and integrated to the system for time series analysis and simulation. Different technologies including tensorflow.js were used to successfully developed the intelligent system. It is found on this paper that recurrent neural network (RNN) is a good deep learning algorithm for predicting pisciculture in Taal lake. Further, it is also shown in the initial visualization of the system that barangay Sampaloc in Taal has highest rate of fish production in Taal while Tilapia nilotica sp. is the major product of the latter.
Effectiveness of Mathematics-Gammified Applications for Learners Interactive Numeracy Growth (Math-Galing) in Enhancing the Academic Performance of Grade 11 Learners Monique E. Malabayabas; Alberto D. Yazon; John Frederick B. Tessoro; Karen A. Manaig; Sherwin B. Sapin
Advanced Journal of STEM Education Vol. 2 No. 1 (2024): Advance Journal for STEM Education (AJOSED)
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/ajosed.v2i1.2324

Abstract

This study determined the effectiveness of the academic performance of Grade 11 learners using the Mathematics Gamified Application for Learner's Interactive Numeracy Growth (Math-GALING). This study used a quasi-experimental design. The participants of the study were selected through a match-pairing technique based on the results of their pretest among Grade 11 students, resulting in 29 pairs. These two groups of students, one from the Accountancy, Business, and Management strand and the other from the Humanities and Social Sciences strand, comprised the experimental and comparison groups, respectively. The participants' pretest, posttest, and formative test performances was evaluated using the mean and standard deviation. Similar to how independent t-tests were used to quantify the extent of the difference between two sets of scores, Cohen’s d was used to determine the significance of the difference between the pretest and posttest results. The salient findings from the data were as follows: there is always an opportunity for improvement, particularly when the goal is to enhance teaching and learning processes to provide students with quality education. This study could conclude that using a gamified learning application (Math-GALING) in the teaching of statistics and probability enhanced learners’ performances. It was found that the designed gamified learning application enhanced students’ academic performances.
Unraveling the Connections: Exploring the Relationship between Teaching Effectiveness and Academic Achievement in Blended Learning Environments Karen A. Manaig; Alberto D. Yazon; John Frederick B. Tesoro; Chester Alexis C. Buama; Sherwin B. Sapin
Advanced Journal of STEM Education Vol. 2 No. 2 (2024): Advanced Journal for STEM Education (AJOSED)
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/ajosed.v2i2.2718

Abstract

With the rise of blended learning, assessing the factors influencing student academic performance has become essential. Teaching effectiveness is a critical component of this dynamic, potentially affecting students’ academic success. This study examines the relationship between teaching effectiveness and the academic performance of students engaged in blended learning environments. The primary objective of this research was to determine whether a significant relationship exists between teaching effectiveness and student academic performance. Specifically, this study evaluated various aspects of teaching effectiveness, including the learning environment and instructional practices, and their impact on student achievement. A descriptive-correlational research design was employed, using Simple Random Sampling to select 297 student respondents from the records provided by the university registrar’s office. The study measured teaching effectiveness using the university’s standardized School Form (SF) 7, while students’ academic performance was gauged via their general weighted average for the 2022-2023 academic year. Data analysis included calculating the mean and standard deviation to determine the levels of teaching effectiveness and academic performance, with Pearson’s r used to assess the correlation between these variables. The results indicated that the learning environment aspect of teaching effectiveness scored the highest, while instructional and assessment practices scored the lowest. Overall, teaching effectiveness was rated "Very Satisfactory." A significant positive correlation was identified between teaching effectiveness and student academic performance, thus contradicting the null hypothesis. The findings underscore a strong, consistent positive relationship among the various dimensions of teaching effectiveness, revealing that effective teaching in one area is linked with higher effectiveness in others. This interconnectedness highlights the importance of a comprehensive approach to enhancing teaching practices and suggests that improvements in teaching effectiveness can lead to better academic outcomes in blended learning contexts.
Performance of Dye-Sensitized Solar Cell Utilizing the Extract of Cassava (Manihot Esculenta) Leaves, Guava (Psidium Guajava) Leaves, and Mango (Mangifera Indica) Leaves Kathleen E. Espina; Alberto D. Yazon; Karen D. Manaig; Sherwin B. Sapin; Lerma P. Buenvinida
Journal of Healthcare and Biomedical Science Vol. 3 No. 1 (2024): December Issue
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/jhbs.v3i1.2660

Abstract

Dye- sensitized solar cell (DSSC) is a third-generation solar cells that utilize natural dyes from leaves extract to absorb sunlight and generate electricity. This study aimed to investigate the performance of DSSCs using the leaf extracts of cassava, guava, and mango as natural dyes in terms of UV-Vis absorption and energy output. The experimental method was applied in which the researcher constructed three DSSCs, with each treatment involving the same fabrication and construction. The UV-Vis Spectrum peak value and UV absorption was obtained from UV-Vis Analysis. A multimeter was used to record each voltage to determine the energy output produced by the DSSCs and the commercial solar cell. One-way ANOVA was used to determine the significant difference in the UV absorption of the natural dyes. To determine the significant difference between the three treatments and control in pairwise comparison in terms of energy output, One-Way ANOVA Analysis and Post Hoc Tukey were used. The results showed no significant difference in UV absorption among the three natural dyes. This result signified that the same pigment content gave almost the same UV absorbance at a common UV spectrum peak wavelength. DSSCs with natural dyes produce less electrical energy than commercial solar cells. There was a significant difference in the energy output between the three treatments and the control. DSSCs utilizing natural dyes produced electrical energy in smaller amounts.
Co-Authors Alberto D. Yazon Bryan A. Alibudbud Chester Alexis C. Buama Jefferson L. Lerios Jocelyn O. Padallan John Frederick B. Tesoro John Frederick B. Tessoro Jonardo R. Asor Karen A. Manaig Karen D. Manaig Kathleen E. Espina Lerma P. Buenvinida Maureen B. Veluz Monique E. Malabayabas Paulo B. Molleno