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All Journal Journal of Computer Networks, Architecture and High Performance Computing
Hasugian, Aldi Ridwansyah
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Computer Science & IT Education

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Diving Into Public Sentiments: SVM-Based Analysis of Water Service Opinions Hasugian, Aldi Ridwansyah; Zufria, Ilka
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5231

Abstract

Customer satisfaction is a crucial measure of a company's success, particularly for service-oriented businesses such as regional water utilities. In the case of PDAM Tirta Nauli Sibolga, key service aspects including pricing, water quality, water flow, and responsiveness to customer complaints significantly influence customer satisfaction. Despite ongoing efforts to provide quality services, challenges in areas such as water quality, quantity, and customer communication persist, leading to lower satisfaction levels among consumers. This study seeks to evaluate the implementation of regional regulations related to company management and clean water services, with a focus on improving service quality. Additionally, it applies the Support Vector Machine (SVM) algorithm to perform sentiment analysis of public opinions regarding the company's services. The analysis yielded an impressive accuracy of 98%, with precision, recall, and F1-score values consistently above 90%, indicating a high level of effectiveness in sentiment classification. By leveraging the results of this sentiment analysis, PDAM Tirta Nauli can gain valuable insights into the issues facing the community and implement targeted improvements. Ultimately, this research aims to provide a comprehensive evaluation of customer satisfaction and offer actionable recommendations to enhance service quality, ensuring better customer experiences and supporting the overall development of the region.
Co-Authors Ilka Zufria