DATA MINING DALAM PENGELOMPOKAN JENIS DAN JUMLAH PEMBAGIAN ZAKAT DENGAN MENGGUNAKAN METODE CLUSTERING K-MEANS (STUDI KASUS: BADAN AMIL ZAKAT KOTA BENGKULU)

Prahasti Prahasti

Abstract


Abstrack - This research applies data mining by grouping the types and recipients of zakat. The application is done by the k-means clustering algorithm where the data to be entered is grouped by education and type of work in the distribution of zakat. Then a cluster is formed using the centroid value to determine the closest center point of distance between data. In the k-means clustering algorithm data processing is stopped in the iteration count of the data has not changed (fixed data) from the data that has been grouped. The test is done by using the RapidMiner software experiment conducted by the k-means clustering method which consists of input units, data processing units and output units, k-means clustering grouping data 1-2-1-1, 1-2-1-2 and 3-4-3-4. The results obtained from these tests are grouping the distribution of zakat with each cluster not the same. The test results are displayed in slatter graph.  

Keywords - Data Mining, K-Means Clusttering, Zakat


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References


Goldie Gunadi,et.al (2012). “Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori dan Frequent Pattern Growth (Fp-Growth)” Ed. Jurnal Telematika Mkom Vol 4 No.1

Afrisawati (2013). “Implementasi Data Mining Pemilihan Pelanggan Potensial Menggunakan Algoritma K-Means” Ed. Pelita Informatika Budi Darma, Volume: V No.3.

Gamsir Bachdim (2011) “Perilaku Muzaaki dalam membayar zakat mal (Studi Fenomenologi Pengaki di Kota Kediri” Ed. Jurnal Aplikasi Manajemen Vol.10..

Johan Oscar Ong (2003) “Implementasi Algoritma K-Means Clustering Untuk Menentukan Strategi Marketing Presiden University” Ed. Jurnal Ilmiah Nuswantoro. Vol. 12 No.1.

Ni Ketut Dewi Ari Jayanti (2014) “Analisa Pengelompokan Konstrasi Program Studi Menggunakan K-Means Clustering” Ed. Konferensi Nasional Sistem Informasi.

Rendi Handoyo, et.al (2014) “Perbandingan metode clustering menggunakan metode Single Linkage dan K-means pada Pengelompokan Dokumen” Ed. Issn Vol. 15.

Rima Dias Ramadhani (2013) “Data Mining Menggunakan Algoritma K-means Clustering Untuk menentukan Strategi Promosi Univeristas Dian Nuswantoro” Ed. Jurnal.

Tacbir Hendro Pudjiantoro, et.al(2011) “Penerapan Data Mining Untuk Menganalisa Kemungkinan Pengunduran Diri Calon Mahasiswa Baru” Ed. Konferensi Nasional Sistem dan Informatika.

Yudi Wibisono (2011) “Perbandingan Partition Around Medoids (PAM) dan K-Means Clustering Untuk Tweets” Ed. Jurnal Knsi.




DOI: https://doi.org/10.36294/jurti.v1i2.298

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