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All Journal Journal of Information Technology and its Utilization
Indah Kairupan
Universitas Katolik De La Salle Manado
Computer Science & IT Engineering

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Implementation of K-means Clustering Algorithm to Analyze the Familial Sentiments Towards COVID-19 Vaccination For Elementary School Students in Kalawat District Indah Kairupan; Liza Wikarsa; Audreyvia Kembuan
Journal of Information Technology and Its Utilization Vol 6 No 2 (2023): December 2023
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.6.2.5280

Abstract

Due to the Ministry of Health's policy, the Indonesian government mandates the public to receive the COVID-19 vaccination as a form of immunity against the coronavirus. This vaccination is not only for adults but also for children of a certain age. Regarding the provision of vaccination for elementary school students aged between 6 to 11 years, the families' responses to this predicament can cause significant barriers to those students being fully vaccinated. Thus, this research developed a web-based application that incorporated the K-means clustering method to group the sentiments of the families into three clusters, namely positive, neutral, and negative. The results showed that the application can identify and cluster the different familial responses from 279 respondents in Kalawat District toward the administration of COVID-19 vaccination to their underage children. The most dominant familial sentiment is positive followed by neutral and negative sentiments with the number of respondents as many as 120 respondents (43%), 113 respondents (41%), and 46 respondents (16%) respectively. This research can help the Health Office in North Minahasa Regency to evaluate public sentiments about vaccination for elementary school students as well as look for better ways to encourage vaccine trust and confidence in this district.
Geographical Information System for Mapping Flood-Prone Areas in Manado City Using the K-Means Clustering Method Aurelia Koagouw; Debby Paseru; Indah Kairupan
Journal of Information Technology and Its Utilization Vol 7 No 1 (2024): June 2024
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.7.1.5403

Abstract

Floods are natural events or phenomena that can cause environmental damage, loss of property, psychological effects, and death or casualties. One way to control flooding non-structurally is by mapping areas that are prone to flooding. This study builds a geographic-based information system to map flood-prone areas in Manado City using the K-Means Clustering algorithm. The main objective of this research is to identify and map areas with a high risk of flooding using spatial data. Slope, land cover type, soil type, water discharge (discharge), and rainfall are independent variables that will be used and processed using the K-Means Clustering algorithm. There are four clusters in the mapping results of flood-prone areas, namely: high vulnerability, medium vulnerability, low vulnerability, and not vulnerable. By using the K-Means method, the results obtained are Paal Dua and Wenang sub-districts are high-vulnerability groups, followed by Mapanget, Tuminting, and Singkil subdistricts with medium vulnerability groups. Tikala District is the only area with low vulnerability. Meanwhile, Bunaken, Sario, Wanea, and Malayayang sub-districts are areas that are not potentially prone to flooding.
Co-Authors Audreyvia Kembuan Aurelia Koagouw Debby Paseru Wikarsa, Liza