Gambar 4.12 Contoh meranking kecil ke besar berdasarkan nilai COF terkecil
D. Hasil Deteksi Outlier Berdasarkan Algoritma ECODB Dengan Microsoft
Excel
Hasil deteksi outlier berdasarkan algoritma ECODB menggunakan Microsoft Excel dengan masukan k dan top N yang berubah
– ubah dapat ditampilkan dalam bentuk tabel
– tabel di bawah. Dimana k adalah jumlah tetangga terdekat dari suatu instances, sedangkan top N adalah jumlah
instances yang dideteksi sebagai outlier yang diurutkan secara kecil ke besar
berdasarkan nilai COF Class Outlier Factor. COF adalah nilai probabilitasderajat sebuah instance dapat menjadi outlier. Outlier adalah data
dengan nilai COF terendah. Class outlier adalah instances yang mempunyai derajat tinggi sebagai outlier. Jumlah class outlier ditentukan berdasarkan
masukan top N, jika top N = 10 maka akan ada 10 instances yang yang mempunyai derajat tinggi sebagai outlier.
Tabel 4.3
Hasil deteksi outlier dengan masukan k dan top N yang berubah – ubah
k Top N
Min COF 7
10 36, 92, 53, 96, 39, 64, 56, 24, 23, 37
20 36, 92, 53, 96, 65, 39, 64, 73, 97, 56, 24, 23, 88, 69,
87, 37, 27, 26, 25, 38 30
36, 92, 53, 96, 65, 39, 64, 73, 97, 56, 24, 23, 88, 69, 87, 37, 90, 27, 26, 25, 38, 55, 84, 63, 83, 15,, 70,
41, 1, 42
40 36, 92, 53, 96, 65, 39, 64, 73, 97, 56, 24, 23, 88, 69,
87, 37, 90, 27, 26, 25, 38, 55, 84, 63, 83, 15, 70, 41, 1, 42, 13, 46, 14, 33, 45, 60, 30, 44, 49, 32
50 36, 39, 53, 92, 96, 23, 24, 37, 56, 64, 65, 69, 73, 87,
88, 97, 25, 26, 27, 38, 55, 90, 42, 1, 15, 41, 63,70, 83,84, 13, 14, 30, 32, 33, 44, 45, 46, 49, 60, 61, 77,
80, 81, 82, 91, 94, 95, 2, 3
17 10
36,53, 39, 73, 64, 24, 56, 23, 69, 37 20
36, 53, 39, 92, 73, 64, 94, 97, 24, 56, 88, 23, 69, 87, 37, 96, 38, 55, 25, 26
30 36, 53, 39, 92, 73, 64, 94, 97, 24, 56, 88, 23, 69, 87,
37, 96, 38, 55, 27, 25, 26, 42, 65, 90, 17, 18, 13, 15, 11, 14
40 36, 53, 39, 92, 73, 64, 94, 97, 24, 56, 88, 23, 69, 87,
37, 96, 38, 55, 27, 25, 26, 42, 65, 90, 17, 18, 13, 15, 82, 70, 11, 14, 80, 19, 45, 12, 41, 16, 75, 1
50 36, 53, 39, 92, 73, 64, 94, 97, 24, 56, 88, 23, 69, 87,
37, 96, 38, 55, 27, 25, 26, 42, 65, 90, 17, 18, 13, 15, 82, 70, 11, 14, 80, 19, 45, 12, 41, 16, 75, 1, 83, 50,
81, 31, 52, 34, 84, 28, 29, 35
27 10
36, 53, 92, 64, 73, 94, 97, 24, 37, 39 20
36, 53, 92, 64, 73, 94, 97, 24, 37, 39, 88, 23, 69, 87, 96, 38, 55, 25, 26, 42
30 36, 53, 92, 64, 73, 94, 97, 24, 37, 39, 88, 23, 69, 87,
96, 38, 55, 56, 25, 26, 42, 27, 65, 90, 17, 11, 13, 14, 15, 16
40 36, 53, 92, 64, 73, 94, 97, 24, 37, 39, 88, 23, 69, 87,
96, 38, 55, 56, 25, 26, 42, 27, 65, 90, 17, 11, 18, 13, 14, 15, 16, 91, 8, 70, 19, 10, 2, 28, 12, 1
50 36, 53, 92, 64, 73, 94, 97, 24, 37, 39, 88, 23, 69, 87,
96, 38, 55, 56, 25, 26, 42, 27, 65, 90, 17, 11, 18, 13, 14, 15, 16, 91, 8, 70, 19, 10, 2, 50, 63, 31, 34, 59,
41, 45, 48, 28, 29, 35, 12, 1
37 10
36, 53, 92, 64, 73, 94, 97, 24, 37, 39 20
36, 53, 92, 64, 73, 94, 97, 24, 37, 39, 88, 23, 69, 87, 96, 38, 55, 25, 26, 42
30 36, 53, 92, 64, 73, 94, 97, 24, 37, 39, 88, 23, 69, 87,
96, 38, 55, 56, 25, 26, 42, 27, 65, 90, 17, 11, 13, 14, 15, 16
40 36, 53, 92, 64, 73, 94, 97, 24, 37, 39, 88, 23, 69, 87,
96, 38, 55, 56, 25, 26, 42, 27, 65, 90, 17, 11, 18, 13,
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