Browsing by Author "Malaya, Ara Rivina N."

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    Information management system for research of Don Mariano Marcos Memorial State University–South La Union Campus
    (Institute of Advanced Engineering and Science (IAES), 2022) Malaya, Ara Rivina N.; Munar, Emely A.; Cuison, Floribeth Panay; Dacanay, Enrico G.
    The study aimed to develop an Information Management System for Research (IMSR) for the South La Union Campus of Don Mariano Marcos Memorial State University. A descriptive-developmental research design was used, together with the Rapid Application Development model. Interviews and an ISO/IEC 25010-based questionnaire were used, with 37 faculty members and five research-office personnel participating. The resulting system was evaluated as very acceptable in terms of functional suitability, reliability, usability, efficiency, maintainability, portability, compatibility, and security. The researchers concluded that the IMSR is a deployable functional information management system that can enhance research operations.
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    Remote sensing and machine learning algorithms in geospatial mapping and temporal forecasting of mangrove density distribution
    (Don Mariano Marcos Memorial State University - South La Union Campus, 2023-12) Malaya, Ara Rivina N.
    This study aimed to develop models for forecasting vegetation indices of the selected mangrove sites in Region 1. The researcher identified the data that can be used for visualization, prediction, and analysis; applied the preprocessing techniques in the dataset; and evaluated the best-performing machine learning algorithms in forecasting mangrove density distribution. Descriptive and applied research designs were employed in the study. Field and satellite data acquired were utilized in forecasting vegetation indices. Application software including Google Earth Pro and Google Earth Engine were utilized in mapping and determining the exact location of the target sites. Programming was also done in Google Earth Engine to extract the satellite data for forecasting and WEKA was used to generate time series analysis for forecasting of vegetation indices. The salient findings of the study are as follows: (1) the mean average of Pangasinan, La Union, Ilocos Sur, and Ilocos Norte are 0.866, 0.592, 0.543, and 0.974, respectively; (2) data cleaning and data transformation were performed to organize the data; and (3) two performance evaluation metrics were applied to identify the best-performing base learner in forecasting vegetation indices. SMOReg had an accuracy of 99.53 and a MAPE score of 2.56, outperforming Linear Regression, Gaussian Processes, and Multilayer Perceptron.