Patient Data Clustering using Fuzzy C-Means (FCM) and Agglomerative Hierarchical Clustering (AHC)


Agglomerative Hierarchical Clustering
Fuzzy C-Means
Patient data

How to Cite

Susilowati, R., Yazid, A. S., & Uyun, S. (2019). Patient Data Clustering using Fuzzy C-Means (FCM) and Agglomerative Hierarchical Clustering (AHC). IJID (International Journal on Informatics for Development), 3(1), 17–24.


Generally, the current system development only include the input, view, and reports. At Jogja Hospital, a system with a patient database can only provide information about the percentage of male and female patients. Its unable to extract more specific information, even though medical record data has a lot of information. The complete information should be used as a reference for the authorities to make a decision. This information can be obtained by analyzing and processing the medical record data. One way to extract information from this data is clustering. The domain of this study is patient data. Before the data is clustered, preprocessing is needed through name standardization, numeration, and data normalization. During the clustering process, the algorithms used are Fuzzy C-Means (FCM) and Agglomerative Hierarchical Clustering (AHC). Two algorithms are implemented to determine which algorithm is the most appropriate and fast to handle the processing of patient data. The results of the study show that the processing time required to do clustering with FCM algorithm is relatively faster than AHC algorithm. For data with small volumes, the iteration of FCM algorithm is more than AHC algorithm, however, the results of the clustering using FCM algorithm are easier to interpret than AHC algorithm. From the visualization of clustering results, found that the cluster pattern with FCM algorithm is better based on the three variables used as references. So the most suitable algorithm to use is Fuzzy C-Means (FCM) for processing patient data.


M. & A. A. Hanafiah, Etika Kedokteran dan Hukum Kesehatan. Jakarta: EGC, 2009.

A. & T. C. T. Kadir, Pengenalan Teknologi Informasi. Yogyakarta: Andi, 2003.

K. & E. T. Luthfi, Algoritma Data Mining. Yogyakarta: Andi, 2009.

E. Pramudiono, “Pengantar Data Mining: Menambang Permata Pengetahuan di Gunung Data,” 2003. [Online]. Available: [Accessed: 07-Feb-2012].

A. G. Mabrur, “Penerapan Data Mining di Bidang Marketing untuk Memprediksi Potensi Kriteria Nasabah Menggunakan Metode Decision Tree di PD BPR Kabupaten Bandung Cabang Batujajar,” Universitas Komputer Indonesia, 2011.

S. & H. P. Kusumadewi, Aplikasi Logika Fuzzy untuk Pendukung Keputusan. Yogyakarta: Graha Ilmu, 2010.

E. Irdiansyah, “Penerapan Data Mining pada Penjualan Produk Minuman di PT. Pepsi Cola Indobeverages Menggunakan Metode Clustering,” Universitas Komputer Indonesia, 2010.

A. Budiarti, “Aplikasi dan Analisis Data Mining pada Data Akademik,” Universitas Indonesia, 2006.

R. H. Tamba, “Penerapan Data MIning Menggunakan Algoritma Agglomerative Hierarchical Clustering untuk Segmentasi Data,” Universitas Gadjah Mada, 2009.

Creative Commons License
IJID (International Journal on Informatics for Development) is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License