Ace C. Lagman
95 Publications
Scopus ID: 85199917118
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-5
Conference Paper | Published: January 1, 2023
Abstract
In the context of higher education, ensuring timely and successful graduation for students is a pivotal objective, necessitating a comprehensive understanding of their academic performance and tailored interventions. Evaluating ongoing academic records is crucial for effective pedagogical interventions, but limited research on student performance in completing degrees has introduced challenges. To address these, academic institutions are adopting flexible curricula designs, prompting the need for diverse course offerings. Amidst this, two student categories emerge: regular and irregular, each presenting unique challenges. A Rule-Based Suggestion Platform for Academic Program Completion was conceptualized, designed, developed, and rigorously evaluated through the use of the Technology Acceptance Model (TAM). This innovative platform, which harnesses the power of rule-based decision-making, was created to address the intricate challenges surrounding students' timely and successful program completion within the academic landscape. The platform's underlying architecture and functionality were crafted to provide students with personalized and optimized recommendations, guiding them towards informed decisions in shaping their educational journey. The development process involved the integration of advanced rule-building mechanisms, enabling the system to analyze individual student profiles, academic progress, and program requirements. This data-driven approach empowers the platform to generate customized study plans that not only consider the students' academic ambitions but also adhere to predefined constraints and parameters. By evaluating the platform's performance through the Technology Acceptance Model, this study assesses the users' perception and acceptance of this novel tool, shedding light on its effectiveness and potential impact on enhancing the academic planning process.
Scopus ID: 85199923471
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-5
Conference Paper | Published: January 1, 2023
Abstract
Sleep is critical, particularly for young adults aged 18 to 25. Young adults should get at least 7 to 8 hours of sleep per night. Most young adults struggle to get enough sleep due to a variety of factors such as anxiety, depression, and excessive use of technology. As a result of continuing the same sleepless night routine in their daily lives, young adults become sleep deprived. The most common side effects of sleep deprivation are health issues such as heart disease, obesity, kidney failure, high blood pressure, and many more. Since this issue is very timely and relevant nowadays, the researchers develop a major project, an 2D Animated Interactive Video, and a minor project, a Podcast that can help young adults to be relaxed in order to get a better sleep. The 2D animated interactive video will present two environments from which users can choose. It will be uploaded to a Wix site, and each option will be unlisted so that users can interact with the animated video. The podcast, on the other hand, will be storytelling that is related to the story of the major project. It will consist of four episodes excluding the pilot episode. This project will also be uploaded to YouTube for easy and free access.
Scopus ID: 85199916542
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-6
Conference Paper | Published: January 1, 2023
Abstract
Ensuring students' timely and satisfactory graduation requires evaluating their future performance based on ongoing academic records and implementing pedagogical interventions. Within an educational context, students can be categorized as regular or irregular, each subject to distinct academic rules. Regular students follow a predetermined curriculum, enjoying a clear path to graduation and improved access to required courses, facilitating efficient progress toward degree completion. Conversely, irregular students face challenges such as disruptions and delays, necessitating additional time and support to meet degree requirements. Guiding both regular and irregular students and enhancing their study plans requires proper guidance and academic intervention. To bridge the existing research gap, this study introduces a Decision-based Recommendation System towards Program Completion Using Forward Chaining Algorithm. This system automatically generates a study plan by considering defined constraints and parameters, enabling students to assess the term and year of their degree program completion. Leveraging the forward chaining algorithm with fuzzy IF-THEN-ELSE rules, the system's predictive model captures intricate relationships and dependencies within the data, yielding valuable insights and predictions. This adaptive approach refines predictions with the availability of new data, enhancing accuracy and usefulness in guiding decision-making processes related to generating a study plan.
Scopus ID: 85199903161
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-5
Conference Paper | Published: January 1, 2023
Abstract
The Philippines has unique and rich culture; thus, it is also part of its identity. A preliminary test the researchers conducted revealed that the consumers do not consider street food as part of the culture. The Hybrid 2D Animation Documentary tackles the role of street food and street food vendors in the cultural aspect. The evaluation process used in the study is a comparative analysis of the set of pre-assessment and post-assessment evaluations from 30 respondents. The result showed that there had been an increase in value in recognition and acknowledgment of street food after consuming the material. The researchers recommended that street food vendors start acknowledging their cultural role, that multimedia art students create more content featuring the cultural role of street foods, that the teachers introduce the representation of street foods, and that future researchers explore the sanitation issues of street foods.
Scopus ID: 85199914523
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-6
Conference Paper | Published: January 1, 2023
Abstract
The assessment of students' academic performance based on ongoing records plays a vital role in enabling timely interventions for successful graduation. Variances in degree program completion times emphasize the need to tailor subjects and courses to individual student requirements. However, limited research exists on student degree completion, presenting challenges related to diverse student backgrounds, information gaps in courses, and accommodating evolving progress in predictions. Educational institutions are adapting flexible curricula, necessitating a diverse range of courses and informed decision-making for academic planning. Structured course sequences in universities ensure a gradual buildup of knowledge and skills, aiding students in tackling more complex subjects progressively. A decision-based recommendation system for predicting graduation time was developed and evaluated using the ISO/IEC 25010 Software Quality Model. This model included parameters like functionality, performance, compatibility, usability, reliability, security, maintainability, and portability. Each parameter was assessed to ensure the system's functionality, performance optimization, cross-platform compatibility, user-friendliness, reliability, security, ease of maintenance, and adaptability to different environments. Through this evaluation, the system's quality and efficacy were comprehensively validated, confirming its ability to achieve intended objectives and meet user requirements.
Scopus ID: 85166667236
2023 IEEE World AI IoT Congress (AIIoT), (2023), pp. 0421-0426
Pitz Gerald G. Lagrazon, Jennifer Edytha E. Japor, ... Arnold B. Platon
Conference Paper | Published: January 1, 2023
Abstract
Rainfall is crucial for flood prevention and comprehending the correlation between rainfall and flooding. Cavite province in the Philippines is vulnerable to flooding caused by heavy rainfall and climate change impacts. Early detection of flooding through early warning systems can prevent excessive damage loss and potentially save lives. It can also provide major savings in terms of monetary benefit and increased interagency coordination for rapid decision-making. Machine learning is an important tool for predicting rainfall which can be used to predict rainfall in the province. The objective of this study is to conduct a comparative analysis of various models for predicting daily rainfall, using relevant atmospheric features such as maximum, minimum, and mean temperature, relative humidity, wind speed, wind direction, cloud cover, pressure, and evaporation. The study seeks to identify the most effective model for accurately predicting rainfall in the Cavite Province to benefit the local community. Among the five machine learning models evaluated, the Gaussian Process Regression model demonstrated the highest accuracy in predicting daily rainfall. The findings of this study can be leveraged to mitigate the damage caused by flooding in the Cavite Province and serve as a useful reference for similar studies in other regions prone to flooding.
Scopus ID: 85190068979
2023 1st IEEE International Conference on Smart Technology (ICE-SMARTec), (2023), pp. 72-77
Conference Paper | Published: January 1, 2023
Abstract
Relative humidity is an important environmental parameter and is widely used in various fields. Prediction of humidity levels is crucial for climate modeling, heat stress, air quality forecasting, and public health. Machine learning techniques have shown potential for predicting humidity due to their nonlinear nature. However, there is a research gap in humidity prediction in the Philippines, specifically the lack of studies utilizing the available parameters provided by PAGASA, presenting an opportunity for further investigation and development of models for predicting humidity levels in the country. In this study, the researchers used a publicly available dataset from PAGASA containing weather measurements from 2000 to 2022 in the Philippines. Various machine learning models were trained and tested, with hyperparameter tuning performed using Bayesian optimization. The Gaussian Process Regression model with optimized hyperparameters achieved the best performance in predicting relative humidity, with the lowest RMSE and highest R-squared values. This study provides a reliable way to predict humidity levels in the Philippines based on weather parameters.
Scopus ID: 85174292970
2023 International Conference on Information Technology Research and Innovation (ICITRI), (2023), pp. 118-122
Conference Paper | Published: January 1, 2023
Abstract
Wind power is a challenge in power generation. The tortuous process stages in generating voltage become a significant problem to be solved properly. One indicator of the process is the determination of the right wind speed because it always changes at any time and under circumstances. For this reason, accurate predictions are needed so as to maintain the smooth integration of wind power into the overall system. Machine learning is used as a promising approach to dealing with wind intermittent power because wind speed prediction methods have been developed in recent years. This study explores climate patterns in the Philippines using data collected from PAGASA. The data is trained and tested with a machine learning model to predict wind speed. This research resulted in the Gaussian Process Regression (GPR) model outperforming other models and is very suitable for datasets in achieving accurate and reliable predictions.
Scopus ID: 85148011429
Lecture Notes in Networks and Systems, (2023), pp. 83-91
Book Chapter | Published: January 1, 2023
Abstract
E-commerce plays an essential role in selling products or services online because it can reach more customers than traditional retail. If the customer data is appropriately mishandled, it disrupts the business’ data organization and poor customer relationship management. The study focuses on creating an e-commerce website that efficiently handles the data and integrates a personalized hybrid recommender system. Content-based and collaborative filtering methods were used in the recommender system to improve customer relationship management, streamline procedures, organize inventory and sales, and increase profits. Sales forecasting using ARIMA was also added to use the customer data for efficient business decisions. ISO 9126 was the software quality model used to evaluate the developed system using the software quality characteristics functionality, usability, maintainability, and efficiency. The system got an overall mean score of 4.57, which is excellent, which means the system can perform smooth transactions from ordering up to the checkout and organized products, sales, and inventory. The integration of the recommender systems was able to give recommendations based on the customer's preferences, which enhances the user experience that may lead to an increase in sales of the business since the suggestions are tailored recommendations to the users.
Scopus ID: 85126615287
2022 4th Asia Pacific Information Technology Conference, (2022), pp. 138-143
Conference Paper | Published: January 14, 2022
Abstract
The Learning Management System is an innovative tool to facilitate online learning using technology. It monitors students’ learning progress and actions. As most academic institutions are already shifted from the traditional learning to online and blended learning approaches, analysis of students’ learning behaviors is empirical to design necessary and suited academic intervention programs. With this, the researchers aimed to identify significant attributes affecting student academic performance in an online education environment. The knowledge discovery in databases (KDD) was used to provide step by step process in extracting and evaluating the predictive and cluster models which aim to classify students who will have academic learning difficulty based on sets of parameters and constraints. The study reveals that students with low engagement in online learning are those with problems in terms of their academic performance. Therefore, the study reaffirmed that there is a strong relationship between student behaviors in LMS and academic achievement.