Main Article Content

Widya Surya Ningsih
Eko Haryanto


Acquired Immunodeficiency Syndrome or Acquired Immune Deficiency Syndrome (AIDS shortened) is a combination of symptoms and diseases caused by the HIV virus's damage to the human immune system. This study examines the WEKA Application for K-means Clustering Data Mining in Grouping AIDS Cases by Province. The increasing number of AIDS patients in Indonesia is a matter that never escapes the government's notice. People are concerned about the spread of the AIDS virus due to the persistently rising death rate. Documents supplied by the Social Security Administering Body describing the number of villages/subdistricts with health facilities were mined for data and study. This research utilizes data from the years 2008-2011 for a total of 34 provinces. There are two assessment criteria: 1) the average number of AIDS cases and 2) the average number of AIDS-related deaths controlled by three clusters: high cluster level (C1), medium cluster level (C2), and low cluster level (C3) (C3). So that the C1 cluster evaluation for AIDS cases is based on four provinces, Papua, DKI Jakarta, West Java, and East Java, nine provinces for the C2 cluster, and twenty provinces for the C3 cluster. This information can be sent to provinces who are concerned about the number of AIDS cases.


Download data is not yet available.

Article Details

How to Cite
Ningsih, W. S., & Eko Haryanto. (2022). Application of rapidminer for clustering aids cases by province using data mining k-means clustering. Journal of Intelligent Decision Support System (IDSS), 5(3), 89-98.
[1] Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.
[2] Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., & Akinyelu, A. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, 104743.
[3] Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9(8), 1295.
[4] Fränti, P., & Sieranoja, S. (2018). K-means properties on six clustering benchmark datasets. Applied Intelligence, 48(12), 4743-4759.
[5] Kuswandi, D., Surahman, E., Thaariq, Z. Z. A., & Muthmainnah, M. (2018, October). K-Means clustering of student perceptions on project-based learning model application. In 2018 4th International Conference on Education and Technology (ICET) (pp. 9-12). IEEE.