klasterisasi objek wisata yang ada di Kabupaten Pamekasan menggunakan algoritma K-means dimana metode DBI digunakan untuk menentukan klaster yang optimal sehingga menghasilkan pola diagram kunjungan wisatawan di Kabupaten Pamekasan. Metode K-means merupakan algoritma klasterisasi yang paling tua dan paling banyak digunakan dalam berbagai aplikasi karena kemudahan implementasinya. Hasil perhitungan aplikasi klasterisasi data historical kunjungan wisatawan pada DISPORABUD Kabupaten Pamekasan telah berhasil dibangun menggunakan metode K�Means yaitu set cluster 2 dengan nilai DBI 3,751, set cluster 3 nilai DBI 3,7887, set cluster 4 dengan nilai DBI 3,371, set cluster 5 dengan nilai DBI 3,046 set cluster yang mempunyai nilai DBI paling minimum adalah pada set cluster 6 yang bernilai 2,791. Semakin kecil nilai DBI, maka semakin optimal sebuah set cluster. Terdapat 6 kelompok objek wisata pada set cluster 6, dimana cluster 1 sebanyak 3 anggota, cluster 2 sebanyak 13 anggota, cluster 3 sebanyak 3 anggota, cluster 4 sebanyak 6 anggota, cluster 5 sebanyak 11 anggota, cluster 6 sebanyak 17 angota.

The clustering of tourist objects in Pamekasan Regency used the K-means algorithm where the DBI method is used to determine the optimal cluster so as to produce a diagrammatic pattern of tourist visits in Pamekasan Regency. The K-means method is the oldest and most widely used clustering algorithm in various applications because of its ease of implementation. The results of the calculation of the application of clustering historical data on tourist visits at the DISPORABUD of Pamekasan Regency have been successfully built using the K-Means method, namely set cluster 2 with a DBI value of 3.751, set cluster 3 with a DBI value of 3.7887, set cluster 4 with a DBI value of 3.371, set cluster 5 with a DBI value is 3,046 cluster set which has the minimum DBI value is in cluster 6 set with a value of 2,791. The smaller the DBI value, the more optimal a cluster set is. There are 6 groups of attractions, where cluster 1 has 3 members, cluster 2 has 13 members, cluster 3 has 3 members, cluster 4 has 6 members, cluster 5 has 11 members, and cluster 6 has 17 members.