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Paper ID: UIJRTV5I60016
Volume:05
Issue:06
Pages:229-252
Date:April 2024
ISSN:2582-6832
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Arsenio N. Arellano Jr. and Dr. Mia V. Villarica, 2024. Community-Based Welfare Services System: Utilizing Machine Learning with Data Visualization to Improve Access and Service Delivery. United International Journal for Research & Technology (UIJRT). 5(6), pp229-252.
Abstract
Community, from the global perspective, serves as an essential service for the welfare of every person and nurtures their well-being in every nation. This research was categorically the mixed-method approach, known as qualitative descriptive and quantitative experimental or developmental research design. In this study, a purposive sampling method was utilized to select 300 respondents who met the established criteria set by the researcher. Purposive sampling ensured that individuals selected for the study possessed specific characteristics or experiences relevant to the research objectives. The study designed and implemented a web-based application for a community-based welfare services system, integrating machine learning algorithms with data visualization techniques to enhance access and efficiency in service delivery. Likewise, the study aimed to gather insights from diverse perspectives within the community, enriching the depth and breadth of the research findings. Hence, to effectively address the social welfare needs and overcome barriers faced by senior citizens and single parents in Barangay Canlubang, Calamba City, Laguna, Specifically, it sought to answer the following research sub-questions such: How can technology be leveraged to enhance the access and quality of immediate care for senior citizens? How can data visualization and machine learning techniques be harnessed to optimize the efficiency, accessibility, and efficacy of the community welfare services system? What is the level of acceptability perceived by respondents in evaluating the community-based welfare services system using the ISO/IEC 25010 standard? As the results implied, the test case scenario showed that the system passed all the tests performed by the respondents and the general mean weighted average of 4.04 with the verbal interpretation of "Acceptable" to the group of respondents. Furthermore, the assessment summary highlights the system's strong performance across various dimensions, as perceived by different respondent groups. It demonstrates the system's ability to effectively support diverse user needs, ensuring functionality, efficiency, usability, reliability, security, maintainability, and portability in line with ISO/IEC 25010 standards.

Keywords: Welfare Services, Service System, Data Visualization, Predictive Analytics


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