Menghitung Nilai Penambangan Data Dengan Microsoft Excel

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, 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 47 10 94, 97, 36, 88, 37, 96, 53, 38, 39, 42 20 94, 97, 36, 88, 37, 96, 64, 53, 56, 69, 87, 38, 39, 42, 92, 73, 23, 24, 55, 25 30 94, 97, 36, 88, 37, 96, 64, 53, 56, 69, 87, 38, 39, 42, 92, 73, 23, 24, 55, 27, 25, 26, 65, 90, 45, 76, 80, 82, 91, 43 40 94, 97, 36, 88, 37, 96, 64, 53, 56, 69, 87, 38, 39, 42, 92, 73, 23, 24, 55, 27, 25, 26, 65, 90, 45, 76, 80, 82, 91, 43, 51, 83, 66, 70, 18, 14, 16, 3, 11, 12 50 94, 97, 36, 88, 37, 96, 64, 53, 56, 69, 87, 38, 39, 42, 92, 73, 23, 24, 55, 27, 25, 26, 65, 90, 45, 76, 80, 82, 91, 43, 51, 83, 66, 70, 18, 14, 16, 63, 71, 62, 34, 44, 49, 60, 35, 3, 17, 11, 12, 19 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 . Untuk dapat mengetahui pengaruh k dan top N dalam proses deteksi outlier menggunakan algoritma ECODB, dapat dilihat dari perubahan nilai COF berdasarkan masukan k dan top N yang berubah – ubah. Karena nilai COF bergantung pada masukan top N , maka untuk memudahkan perbandingan dari hasil deteksi, nilai COF ditampilkan dalam bentuk rata – rata means . Kolom min COF dan max COF dapat digunakan untuk melihat seberapa jauh jarak nilai means terhadap min COF dan max COF . Dari tabel 4.8, dapat dilihat bahwa semakin tinggi nilai masukan k dan top N , maka semakin tinggi pula nilai COF. Tabel 4.4 Nilai rata – rata COF berdasarkan masukan k dan top N yang berubah - ubah k Top N Min COF Max COF Means COF 7 10 0.99 2.84 1.77 20 0.99 3.14 2.06 30 0.99 4.81 2.78 40 0.99 5.55 3.445 50 0.99 6.22 3.89 17 10 2.39 5.7 4.15 20 2.39 9.95 5.6 30 2.39 33.62 12.69 40 2.39 35.93 18.31 50 2.39 38.43 21.89 27 10 3.82 8.56 6.8 20 3.82 16.31 10.13 30 3.82 84.47 31.71 40 3.82 92.06 46.02 50 3.82 92.15 55.23 37 10 10.53 16.39 14.01 20 10.53 26.66 18.51 30 10.53 163.44 59.07 40 10.53 168.64 85.81 50 10.53 168.72 102.38 47 10 13.53 27.73 22.53 20 13.53 33.78 26.44 30 13.53 254.62 93.35 40 13.53 261.42 135.35 50 13.53 268.13 161.76

Dokumen yang terkait

Deteksi outlier menggunakan Algoritma Local Outlier Probability : studi kasus data akademik mahasiswa Program Studi Teknik Informatika Universitas Sanata Dharma.

0 5 265

Deteksi outlier menggunakan Algoritma Connectivity Based Outlier Factor : studi kasus data akademik mahasiswa Teknik Informatika Universitas Sanata Dharma.

0 4 252

Deteksi outlier pada data campuran numerik dan kategorikal menggunakan algoritma Enhanced Class Outlier Distance Based (ECODB) : studi kasus data kredit BPR XYZ.

0 4 106

Deteksi Outlier menggunakan algoritma Block-Based Nested-Loop : studi kasus data akademik mahasiswa Program Studi Teknik Informatika Universitas Sanata Dharma.

0 2 202

Deteksi outlier menggunakan algoritma Block-based Nested Loop (studi kasus: data akademik mahasiswa prodi PS Universitas XYZ).

1 5 6

Deteksi outlier menggunakan algoritma Naive Nested Loop (studi kasus : data akademik mahasiswa program studi PS Universitas XYZ).

0 0 4

Deteksi outlier menggunakan Algoritma Connectivity Based Outlier Factor studi kasus data akademik mahasiswa Teknik Informatika Universitas Sanata Dharma

1 8 250

Deteksi outlier menggunakan Algoritma Local Outlier Probability studi kasus data akademik mahasiswa Program Studi Teknik Informatika Universitas Sanata Dharma

1 9 263

Penerapan metode enhanced class outlier distance based untuk identifikasi outlier pada data hasil ujian nasional, indeks integritas dan akreditasi sekolah menengah atas

1 6 143

Deteksi outlier menggunakan algoritma local outlier factor : studi kasus data akademik mahasiswa TI Universitas Sanata Dharma - USD Repository

0 0 241