Data-Driven Analytical Model Using Machine Learning Algorithms: A Case Study on Clean and Healthy Living Behaviour in Surabaya City's Coastal Areas

Harun Al Azies (Study Program in Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia)
Noval Ariyanto (Research Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia)
Ishak Bintang Dikaputra (Study Program in Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia)

International Journal of Advances in Data and Information Systems
Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems

The objective of this article is to use machine learning technology, specifically the Support Vector Machine (SVM) approach with a linear kernel, to analyze and predict clean and healthy living behavior (CHLB) in coastal dwellings in Surabaya City. To train the SVM model, researchers collect health and environmental data from the region. As a result, our model predicts house CHLB status with an 83% accuracy rate. The most important variables in this prediction are the amount of community access to appropriate sanitary facilities, the health of households, and the sustainability of public areas that meet health requirements. These findings have crucial implications for attempts to improve CHLB in Surabaya's coastal areas in compliance with the National Medium-Term Development Plan (RPJMN) aims. Furthermore, the findings of this study can be used to build more targeted and long-term health policies in coastal communities.