![](/public/journals/5/"http:/118.97.208.182/ojs/public/site/images/admin/logo-RJI-supported-by3.jpg){kind=logo}
A Comparative Analysis of Fuzzy Clustering and K-means Clustering for 15-day Rainfall Datain DIY Province.
Abstract
This study aimed to compare performance two clustering methods (fuzzy c-means and K-means) on the basis validity index. The study used daily rainfall data for twenty five years (1985 to 2009) from 22 stations covering the DIY Province. PCA is used to reduce number 15-day rainfall variables and transform into new variable. Four validity index clustering: Xie-Beni index (XB), sum squared error (SSE), silhouette (Si) and standard deviation ratio (Sw/Sb) are used for compare performance of two clustering methods. The optimal number of cluster is determined using XBindex, and result SSE, Si and ratio (Sw/Sb) are compared to find appropriate clustering algoritms to 15-day rainfall data. The study results showed that the first 4 PCs explains more than 82 % of total variance, than are used for data input in fuzzy c-means and K-means algorithms. The optimal number clusters according XB index are sixand five forK-means and FCM methode, respectively.The average ratio Sw/Sb K-means methode (0.243) was smaller than fuzzy c-means methode (0.289). The Silhouette dan SSE index are 0.46 and 76, 0.24 and 254 for K-means and FCM respectively. The result indicated that K-means methodeto be better than fuzzy c-means for clustering 15-day rainfall data in DIY Province.
Keywords
References
Cebeci, Z. dan Yildiz, F. 2015. Comparison of K-Means and Fuzzy C-Means Algoritms on Different Cluster Structures. Journal of Agricultural Informatics. Vol. 6 No. 3 pp 13-23.
Dikbas, F., Firat, M., Koc, A.C., dan Gungor, M. 2012. Classification of Precipitation Series Using Fuzzy Cluster Method. Int. Journal of Climatology. 32. pp 1596 – 1603.
Goncalves, M.F., Blanco, C.J.C., Santos, V.,C., Oliveira, L.,L.,S., dan Pessoa, F.C.L. 2016. Identification of Rainfall Homogenous Regions Taking into Account El-Nino and La-Nina and Rainfall Decrease in the State of Para, Brazilian Amazon. Acta Scientiarum Technology. Vol.38. No.2. pp. 209 – 2016.
Gudono. 2016. Analisis Data Multivariat. Cetakan Kedua Edisi 4. BPFE. Yogyakarta.
Haqiqi, B.N. dan Kurniawan, R. 2015. Analisis Perbandingan Metode C-Means dan Subtractive Fuzzy C-Means. Media Statistika. Vol.8 No. 2 pp 59-67.
Junaeni, Ina. 2014. Dampak Penerapan Principal Component Anaysis (PCA) Dalam Clustering Curah Hujan di Pulau Jawa, Bali, Dan Lombok. Jurnal Sains Dirgantara Vol. 11 no. 2. pp; 82-99.
Junaeni, I., Yuliani, D., Ayahbi, R., Noersomadi, Hardjana, T., dan Nurzaman. 2010. Pengelompokkan Wilayah Curah Hujan Kalimantan Barat Berbasis Metode Ward dan Fuzzy Clustering. Jurnal Sains Dirgantara Vol. 7 no. 2. pp; 82-99
Kusumadewi, S., dan Hartati,. 2006. Neuro Fuzzy: Integrasi Sistem Fuzzy dan Jaringan Syaraf. Graha Ilmu. Yogyakarta.
Modarres, Reza.2006. Regional Precipitation Climates of Iran. Journal of Hydrology. Vol 45. No. 1. pp 15 – 29.
Morissette, L. dan Chartier, S.2013. The K-means Clustering Technique: Consideration and Implementation in Mathematica. Tutorials in Quantitative Methods for Psychology. Vol 9 No. 1. pp 15 – 24.
Ningsih, S., Sri, W., dan Nasution, Y.N. 2016. Perbandingan Kinerja Metode Complete Linkage dan Average Linkage dalam Menentukan Hasil Analisis Cluster: Studi Kasus Produksi Palawija Provinsi Kalimantan Timur 2014/2015. Prosiding Seminar Sains dan Teknologi FMIPA UNMUL. Vol. 1 No. 1 pp. 46 – 50.
Nurwidodo, W.R.S. dan Hariadi, M. 2013. Sistem Pendukung Keputusan Multidimensi Menggunakan K-means Clustering Berbasis Mahalobis Distance. Prosiding Seminar Nasional Manajemen Teknologi XVII. pp. 20-4–20-11.
Pravitasari, A.A. 2009. Penentuan Banyaknya Kelompok dalam Fuzzy C-means Cluster Berdasarkan Proporsi Eigen Value Dari Matriks Similarity dan Indeks XB (Xie dan Beni). Prosiding Seminar Nasional Matematika dan Pendidikan Matematika. FMIPA UNY. pp. 623 - 632.
Puspitasari, N. dan Haviluddin. 2016. Penerapan Metode K-Means dalam Pengelompokkan Curah Hujan di Kalimantan Timur. Proceding Seminar Nasional Riset Ilmu Komputer (SNRIK 2016) Universitas Muslim Indonesia. Makasar.
Rahmatika, L., Suparti, dan Safitri, D. 2015. Analisis Kelompok Dengan Algoritma Fuzzy C-means dan Gustafson Kessel Clustering pada Indeks LQ45. Jurnal Gaussian. Vol 4 No. 3. pp. 543 – 552.
Rizal, A. S. dan Hakim R.B. F. .2015. Metode K-means Cluster dan Fuzzy C-means Cluster; Studi Kasus Indeks Pembangunan Manusia di Kawasan Indonesia Timur Tahun 2012. Prosiding Seminar Nasional Matematika dan Pendidikan Matematika UMS 2015. pp. 643 – 657.
Sari, H. L.2014. Fuzzy Clustering Dalam Pengclusteran Data Curah Hujan Kota Bengkulu Dengan Algoritma C-Means. Jurnal Ilmiah MATRIK. Vol 16. No. 2. pp. 115 – 124.
Susanto, E.B.2016. Evaluasi Hasil Klaster pada Dataset Iris, Soybean-small, Wine menggunakan Algoritma Fuzzy C-means dan K-means++. Surya Informatika. Vol.2 No.. pp 6 – 13.
Thinsungnoen, T., Kaoungku, N., dan Durongdumronchai, P. 2015. The Clustering Validity with Silhaouette and Sum Squared Error. Proceedings of the 3rd International Conference on Industrial Application Engineering. The Institute of Industrial Applications Engineering. Japan. pp 44 – 51.
Tian, W., Zheng, Y., Yang, R., Ji, S., dan Wang. J., 2014. Research on lustering based Meteorological Data Mining Methods. Advance Science and Tecchnology Letters. Vol 179. pp 06 – 112.
Tjasyono, Bayong.2004. Klimatologi. Edisi ke-2. ITB.Bandung.
Widodo, A.,dan Purhadi. 2013. Perbandingan Metode Fuzzy C-means Clustering dan Fuzzy C-shell Clustering; studi Kasus Kabupaten/Kota di Pulau Jawa Berdasarkan Variabel Pembentuk Indeks Pembangunan Manusia. IndoMS Journal on Statistics. Vol. 1 No 2. pp. 61 – 71.
Xie, N., Hu, L.,Luktarhan, N., dan Zhao, K. 2011. A Clasification of Cluster Validity Indexes Based on Membership Degree and Applications. WISM 2011, part I LNCS. pp. 43 – 50.
DOI: https://doi.org/10.31293/af.v16i2.2905
Refbacks
- There are currently no refbacks.
Copyright (c) 2017 AGRIFOR
AGRIFOR : Jurnal Ilmu Pertanian dan Kehutanan is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
