Implementation of KNN Algorithm for Occupancy Classification of Rehabilitation Houses

  • Nurhadi Wijaya Universitas Respati Yogyakarta
  • Joko Aryanto Universitas Teknologi Yogyakarta
  • Kasmawaru Kasmawaru Universitas Dipa Makassar
  • Anang Faktchur Rachman Universitas Madura
DOI: https://doi.org/10.35842/ijicom.v4i2.36

Abstract

The 2010 eruption of Mount Merapi and the resulting rain lava in Central Java's Kab. Sleman DIY and Magelang Regency damaged homes and infrastructure. According to the Head of BNPB Regulation No. 5, the Community Rehabilitation and Reconstruction and Community-Based Settlement program plan is utilized to repair and rebuild properties damaged by the 2011 Merapi eruption. Two thousand five hundred sixteen residences that will stay in the area have been built permanently due to this initiative. Occupancy rates (permanent occupancy) are used by the World Bank's Key Performance Indicators (KPI) to gauge a program's effectiveness. The database has information on how the software was used and proved successful. Databases, essential tools for introducing new data patterns and revealing previously hidden information, are used in data mining. This study applies the KNN algorithm to classify the house's occupancy status data after Mount Merapi's eruption. The accuracy results obtained from the classification of 82.03%, and the performance of the results through the AUC obtained a value of 0.935.

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Published
2022-12-31
How to Cite
WIJAYA, Nurhadi et al. Implementation of KNN Algorithm for Occupancy Classification of Rehabilitation Houses. International Journal of Informatics and Computation, [S.l.], v. 4, n. 2, p. 7-15, dec. 2022. ISSN 2714-5263. Available at: <https://ijicom.respati.ac.id/index.php/ijicom/article/view/36>. Date accessed: 19 may 2024. doi: https://doi.org/10.35842/ijicom.v4i2.36.
Citation Formats
Issue
Vol 4 No 2 (2022): International Journal of Informatics and Computation
Section
Articles
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