klasifikasi data knn dan bayes
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TUGAS DATA MINING
KLASIFIKASI DATA MENGGUNAKAN METODE K-NEAREST NEIGHBOUR DAN TEOREMA BAYES
Oleh :
Dewi Rokhmah Pyriana (0810680002)
Februliana Dwi Darwanti (0810680003)
Gita Ayu Anjayani (0810680004)
Kusumaning Hati P. (0810680007)
PROGRAM STUDI TEKNIK INFORMATIKA
FAKULTAS TEKNIK
UNIVERSITAS BRAWIJAYA MALANG
MEI 2011
BAB I
PENDAHULUAN
1.1 Latar BelakangDalam beberapa tahun terakhir, jumlah dokumen dalam bentuk digital telah
berkembang sangat besar dari segi ukuran. Sebagai konsekuensi, sangatlah penting untuk bisa mengorganisir dan mengklasifikasi dokumen secara otomatis. Penelitian pada pengklasifikasian data bertujuan untuk mempartisi himpunan dokumen yang tidak terstruktur ke dalam kelompok-kelompok yang menggambarkan isi dari dokumen. Ada dua varian utama dalam pengklasifikasian suatu data, yaitu clustering dan pengkategorian. Clustering berhubungan dengan menemukan sebuah struktur kelompok yang belum kelihatan dari sekumpulan dokumen. Sedangkan pengklasifikasian dapat dianggap sebagai task untuk membentuk struktur dari penyimpanan dokumen berdasarkan pada struktur kelompok yang sudah diketahui sebelumnya.
Dengan semakin meningkatnya kebutuhan untuk pengklasifikasian dokumen, pencarian akan algoritma untuk membantu melakukan aktivitas tersebut juga semakin dikembangkan. Terdapat beberapa metode yang ada untuk pengklasifikasian dokumen yang muncul dan digunakan dalam aplikasi yang berbeda-beda. Beberapa metode pengklasifikasian dokumen yang cukup sering digunakan adalah Tree, Naïve Bayes Classifier, K-Nearest Neighbor dan Neural Network. Dalam suatu pengkategorian, dibutuhkan suatu metode klasifikasi yang memberikan performa yang tinggi.
1.2 Tujuan
1. Untuk mengetahui klasifikasi data yang belum diketahui class-nya menggunakan metode K-Nearest Neighbour (KNN) dan Teorema Bayes
2. Untuk membandingkan tingkat keakurasian hasil dari pengklasifikasian data menggunakan K-Nearest Neighbour (KNN) dan Teorema Bayes
BAB II
METODOLOGI
2.1 K-NEAREST NEIGHBOUR
K-Nearest Neighbour (K-NN) merupakan salah satu metode untuk mengklasifikasi objek berdasarkan jarak terdekat dengan tetangganya pada data training. K-NN adalah jenis algoritma yang sederhana dibandingakan algoritma lain dalam machine learning. Suatu obyek diklasifikasikan dengan class mayoritas dari class tetangga, dimana diambil class yang paling banyak muncul dalam batasan k tertentu dari tetangganya sehingga diperoleh class baru sesuai dengan tetangga yang paling umum. Jika k = 1, maka objek ditetapkan sesuai dengan class tetangga terdekatnya.
Pada fase klasifikasi, k adalah sebuah konstanta yang ditetapkan pengguna. Label / class baru dari suatu data test diklasifikasikan dengan menetapkan label / class yang paling sering muncul di antara sampel k ke titik data training yang ada.Biasanya jarak Euclidean digunakan sebagai jarak metrik, namun ini hanya berlaku untuk variabel kontinyu. Dapat juga dilakukan normalisasi min-max, namun sering terjadi penyimpangan yang dilakukan oleh normalisasi min-max. Dalam kasus seperti klasifikasi teks atau data binner, metrik lainnya seperti jarak Hamming dapat digunakan.
Kelemahan dari algoritma ini, dimana klasifikasi berdasarkan "suara mayoritas" adalah bahwa class dengan frekuensi lebih banyak akan cenderung mendominasi prediksi dari vektor baru, karena mereka cenderung muncul dalam k tetangga terdekat sehingga ketika klasifikasi dilakukan maka class dihitung karena banyaknya frekuensi mereka. Salah satu cara untuk mengatasi masalah ini adalah mempertimbangkan jarak dari titik uji untuk masing-masing k tetangga terdekatnya. Nilai k yang tinggi akan mengurangi efek noise pada klasifikasi, tetapi membuat batasan antara setiap klasifikasi menjadi lebih kabur.
Dibawah ini merupakan contoh klasifikasi dengan metode KNN menggunakan jarak euclidean pada 38 Data training dan 16 data test.
DATA TRAINING
PERIOD
SEEDED SEASON TE TW NC SC NWC
1 SAUTUMN 1.69 3.73 1.65 1.8 3.33
2 UAUTUMN 0.74 0.78 1.09 0.79 1.59
3 S WINTER 0.81 0.86 2.39 0.36 2.064 U WINTER 1.44 2.01 2.96 1.27 4.055 S WINTER 2.48 4.61 4.16 2.16 66 U WINTER 0.84 2.39 2.76 0.87 4.177 U WINTER 0.37 1.37 1.08 0.85 3.458 S SPRING 0.37 0.84 0.26 0.47 0.99 U SPRING 1.33 2.31 2.53 1.08 3.65
10 S SPRING 3.38 5.56 2.76 3.1 5.0611 S SPRING 0.69 1.46 1.07 0.64 1.9512 U SPRING 1.42 2.79 1.42 1.08 1.2213 S SPRING 0.44 1.05 0.24 0.44 0.9414 U SPRING 0.76 1.24 0.7 0.67 0.94
15 SSUMMER 1.13 2.28 0.97 1.66 2.21
16 USUMMER 0.88 1.58 1.06 1.13 1.46
17 SSUMMER 0.17 0.55 0.13 0.27 0.35
18 USUMMER 0.25 0.77 0.1 0.3 0.34
19 USUMMER 0.78 1.45 0.38 0.58 0.67
20 SSUMMER 0.4 0.34 0.45 0.43 0.44
21 SAUTUMN 0.52 0.79 0.42 0.47 0.53
22 UAUTUMN 2.73 2.09 2.24 4.02 2.52
23 UAUTUMN 0.9 2.45 0.52 1.32 2.18
24 SAUTUMN 1.62 2.54 0.94 1.59 1.73
25 UAUTUMN 0.93 2.11 1.19 0.85 2.31
26 SAUTUMN 0.63 1.31 0.76 0.71 1.28
27 S WINTER 0.42 1.23 0.13 0.59 0.9128 U WINTER 0.64 0.43 1.5 0.24 1.1529 U WINTER 0.3 0.69 1.03 0.22 1.8830 S WINTER 0.88 1.32 1.87 0.58 2.9731 U WINTER 0.76 1.25 1.85 1.36 2.1732 S WINTER 1.25 1 2.04 0.71 2.2233 U WINTER 1.08 0.99 1.44 1 1.6434 S WINTER 1.11 0.8 1.46 1.48 0.435 S SPRING 3.43 2.55 5.08 1.77 4.236 U SPRING 0.54 0.43 0.66 0.73 0.9137 S SPRING 0.39 0.44 0.49 0.55 0.5138 U SPRING 2.53 3.18 3.27 2.68 3.6
DATA TESTING
PERIOD
SEEDED
SEASONTE TW NC SC NWC
39 ? SPRING 0.81 0.89 1.33 0.43 2.1840 ? SPRING 0.39 1.22 0.25 0.46 0.8941 ? SUMME
R 0.86 1.24 0.69 0.49 0.6942 ? SUMME
R 2.16 2.29 2.12 0.95 1.8243 ? SPRING 1.7 2.18 1.45 1.47 2.244 ? SPRING 1.22 2 2.13 1.13 2.3345 ? SPRING 0.07 0.22 0.02 0.08 0.2446 ? SPRING 0.49 1.07 0.36 0.87 0.5747 ? SPRING 0.71 1.73 0.72 0.99 0.9848 ? SPRING 1.67 3.46 1.02 1.89 2.4749 ? SUMME
R 0.73 1.51 0.18 1.42 0.7150 ? SUMME
R 1.79 3.13 1.83 1.82 3.1151 ? SUMME
R 0.19 1.05 0.08 0.40 0.5752 ? SUMME
R 0.00 0.15 0.00 0.04 0.0453 ? SUMME
R 0.44 0.89 0.83 0.38 0.7054 ? SUMME
R 0.31 1.15 0.01 0.44 0.66
COMPUTATION
Untuk mendapatkan klasifikasi KNN pada data test diatas, langkah –langkahnya :
1. Menghitung jarak total semua variabel yang ada pada data test dengan semua data pada data training Dihitung dengan menggunakan rumus Euclidean
d Euclidean ( x , y )=∑i
¿¿
Untuk mendapat hasil jarak total semua variabel adalah dengan menjumlahkan jarak Euclidean dari masing-masing variable baik itu data kontinyu maupun data biner / categorical.
Untuk jarak pada data kontinyu, dihitung dengan rumus Euclidean diatas. Sedangkan untuk jarak antara 2 variabel binner / categorical tetap dihitung dengan
rumus Hamming,dimana nilai yang dimasukkan merupakan beda nilai digit yang terdapat pada variabel. Pada data diatas, dapat dibuat nilai biner digit sebagai berikut :
AUTUMN = (1, 0, 0, 0)
WINTER = (0, 1, 0, 0)
SPRING = (0, 0, 1, 0)
SUMMER = (0, 0, 0, 1)
Dapat dirumuskan, untuk mencari jarak total dari data test terhadap data trining adalah sebagai berikut :
distance total ¿¿
2. Mengurutkan hasil perhitungan pada poin 1 sehingga diperoleh data dengan jarak yang urut dari terkecil sampai terbesar.
3. Menentukan k , jumlah rekord yang akan diambil classnya.4. Melihat class dari data sesuai k yang diambil dihitung dari data terkecil.5. Class yang paling banyak muncul sesuai k yang diambil, maka data tersebut masuk
dalam class itu.
PENGUJIAN
Data test ke-39
PERIOD
SEEDED
SEASON TE TW NC SC NWC
39 ? SPRING 0.81 0.89 1.33 0.43 2.18
distance total = ¿
PERIOD SEASON DISTANCE
1 S 3.625162
2 U 1.245913
3 S 1.464172
4 U 3.083942
5 S 6.574306
6 U 3.074004
7 U 1.809917
8 S 1.995244
9 U 2.512011
10 S 6.773625
11 S 0.709859
12 U 2.30961
13 S 1.6995
14 U 1.455026
15 S 2.162845
16 U 1.600719
17 S 2.517876
18 U 2.49868
19 U 2.125935
20 S 2.296214
21 S 2.155528
22 U 4.46746
23 U 2.211312
24 S 2.465522
25 U 1.647938
26 S 1.55631
27 S 2.084754
28 U 1.538311
29 U 1.234585
30 S 1.458767
31 U 1.505822
32 S 1.337834
33 U 1.308243
34 S 2.320754
35 S 5.527974
36 U 1.853726
37 S 2.210837
38 U 4.477164
K = 1
K PERIOD SEASON DISTANCE1 11 S 0.709859
Maka dapat diambil keputusan klasifikasi bahwa data test ke 39 masuk dalam class S (Seeded).
Data test ke-40
PERIOD
SEEDED
SEASON TE TW NC SC NWC
40 ? SPRING 0.39 1.22 0.25 0.46 0.89
PERIOD SEASON DISTANCE1 S 4.324269
2 U 1.618827
3 S 2.695274
4 U 4.551088
5 S 7.819872
6 U 4.449494
7 U 2.901293
8 S 1.070093
9 U 3.907953
10 S 7.644233
11 S 1.405703
12 U 2.321207
13 S 0.185472
14 U 0.621611
15 S 2.524282
16 U 1.673201
17 S 1.356245
18 U 1.254073
19 U 1.133446
20 S 1.420528
21 S 1.166362
22 U 5.150058
23 U 2.291201
24 S 2.591119
25 U 2.26623
26 S 1.241129
27 S 1.016218
28 U 1.834421
29 U 1.713213
30 S 2.866234
31 U 2.479153
32 S 2.612183
33 U 1.949154
34 S 2.106988
35 S 6.92904
36 U 1.374045
37 S 1.348518
38 U 5.551045
K = 1
K PERIOD SEASON DISTANCE1 13 S 0.185472
Maka dapat diambil keputusan klasifikasi bahwa data test ke 40 masuk dalam class S (Seeded).
Data test ke-41
PERIOD
SEEDED
SEASONTE TW NC SC NWC
41 ? SUMMER 0.86 1.24 0.69 0.49 0.69
PERIOD SEASON DISTANCE1 S 4.182858
2 U 1.511952
3 S 2.435303
4 U 4.356627
5 S 7.616246
6 U 4.343109
7 U 3.025938
8 S 1.276519
9 U 3.85501
10 S 7.497113
11 S 1.682795
12 U 2.208619
13 S 1.216553
14 U 1.05119
15 S 2.431502
16 U 1.120446
17 S 1.195742
18 U 1.048666
19 U 0.393827
20 S 1.070187
21 S 0.645755
22 U 4.84063
23 U 2.324564
24 S 2.371012
25 U 2.183071
26 S 1.207974
27 S 1.251279
28 U 1.623176
29 U 1.794631
30 S 2.757843
31 U 2.302824
32 S 2.328411
33 U 1.684043
34 S 1.70681
35 S 6.523159
36 U 1.365796
37 S 1.391726
38 U 5.241479
K = 1
K PERIOD SEASON DISTANCE1 19 U 0.393827
Maka dapat diambil keputusan klasifikasi bahwa data test ke 41 masuk dalam class U (Unseeded).
Data test ke-42
PERIOD
SEEDED
SEASONTE TW NC SC NWC
42 ? SUMMER 2.16 2.29 2.12 0.95 1.82
PERIOD SEASON DISTANCE1 S 2.553037
2 U 2.536908
3 S 2.312142
4 U 2.716192
5 S 5.439016
6 U 2.948033
7 U 2.966311
8 S 3.292871
9 U 2.285432
10 S 5.358265
11 S 2.250622
12 U 1.63233
13 S 3.172523
14 U 2.632812
15 S 2.0099
16 U 1.851513
17 S 3.683897
18 U 3.556937
19 U 2.66402
20 S 3.444387
21 S 3.118349
22 U 3.360684
23 U 2.332316
24 S 1.77882
25 U 1.913191
26 S 2.549529
27 S 3.173169
28 U 2.84735
29 U 2.957059
30 S 2.258584
31 U 2.098833
32 S 1.927745
33 U 2.086552
34 S 2.656219
35 S 4.216503
36 U 3.176807
37 S 3.476838
38 U 3.068029
K = 1
K PERIOD SEASON DISTANCE1 12 U 1.63233
Maka dapat diambil keputusan klasifikasi bahwa data test ke 42 masuk dalam class U (Unseeded).
Data test ke-43
PERIOD
SEEDED
SEASON TE TW NC SC NWC
43 ? SPRING 1.7 2.18 1.45 1.47 2.2
PERIOD SEASON DISTANCE1 S 2.1973622 U 2.2012953 S 2.3811344 U 2.6151675 S 5.456516 U 2.7822117 U 2.3470838 S 2.9445889 U 1.890714
10 S 5.17681411 S 1.56022412 U 1.2505613 S 2.6414214 U 2.134783
15 S 1.26550416 U 1.68751317 S 3.55002818 U 3.42660219 U 2.58015520 S 3.35481721 S 3.02891122 U 3.04926223 U 1.61266924 S 1.27726325 U 1.43593226 S 2.19132427 S 2.77881328 U 2.79356429 U 2.64979230 S 1.99333931 U 1.7092432 S 1.87643333 U 1.82622634 S 2.54807835 S 68.9190636 U 2.69144937 S 3.06068638 U 2.902654
K = 1
K PERIOD SEASON DISTANCE1 12 U 1.25056
Maka dapat diambil keputusan klasifikasi bahwa data test ke 43 masuk dalam class U (Unseeded).
Data test ke-44
PERIOD
SEEDED
SEASON TE TW NC SC NWC
44 ? SPRING 1.22 2 2.13 1.13 2.33
PERIOD SEASON DISTANCE1 S 2.4275712 U 2.1127233 S 1.7891624 U 2.171497
5 S 5.2962636 U 2.2686127 U 2.1341748 S 3.0075749 U 1.418838
10 S 5.39165111 S 1.44242912 U 1.55009713 S 2.73700614 U 2.23109815 S 1.65148416 U 1.78712117 S 3.58678118 U 3.49624119 U 2.75983720 S 3.36340621 S 3.09092222 U 3.41884523 U 1.98886924 S 1.85453525 U 1.43478226 S 2.23159127 S 2.92248228 U 2.52640129 U 2.40896230 S 1.5354831 U 1.38960432 S 1.48239733 U 1.73458934 S 2.59776835 S 4.21848336 U 2.69529237 S 3.07585838 U 2.902327
2.427571
K = 1
K PERIOD SEASON DISTANCE1 31 U 1.389604
Maka dapat diambil keputusan klasifikasi bahwa data test ke 44 masuk dalam class U (Unseeded).
Data test ke-45
PERIO SEEDE SEASO TE TW NC SC NWC
D D N45 ? SPRING 0.07 0.22 0.02 0.08 0.24
PERIOD SEASON DISTANCE
1 S 5.577446
2 U 2.287794
3 S 3.311329
4 U 5.536858
5 S 8.984642
6 U 5.466114
7 U 3.799092
8 S 1.455919
9 U 4.988376
10 S 8.906857
11 S 2.502439
12 U 3.513944
13 S 1.222211
14 U 1.678392
15 S 3.694185
16 U 2.679216
17 S 1.08591
18 U 1.183089
19 U 1.892485
20 S 1.212724
21 S 1.386939
22 U 6.101057
23 U 3.49471
24 S 3.759189
25 U 3.399765
26 S 2.127863
27 S 1.692247
28 U 2.100738
29 U 2.236761
30 S 3.740521
31 U 3.353983
32 S 3.376166
33 U 2.726866
34 S 2.545034
35 S 7.801269
36 U 1.243382
37 S 0.815782
38 U 6.589788
K = 1
K PERIOD SEASON DISTANCE
1 37 S 0.815782
Maka dapat diambil keputusan klasifikasi bahwa data test ke 45 masuk dalam class S (Seeded).
Data test ke-46
PERIOD
SEEDED
SEASON TE TW NC SC NWC
46 ? SPRING 0.49 1.07 0.36 0.87 0.57
PERIOD SEASON DISTANCE
1 S 4.434208
2 U 1.651151
3 S 2.783451
4 U 4.670814
5 S 7.942336
6 U 4.645955
7 U 3.149222
8 S 1.160259
9 U 4.059877
10 S 7.707477
11 S 1.628957
12 U 2.326693
13 S 0.582323
14 U 0.627933
15 S 2.561152
16 U 1.661897
17 S 1.354289
18 U 1.262141
19 U 1.150217
20 S 1.326499
21 S 1.115572
22 U 4.931268
23 U 2.427488
24 S 2.57647
25 U 2.447877
26 S 1.329248
27 S 1.130221
28 U 1.861451
29 U 1.941134
30 S 3.055945
31 U 2.474975
32 S 2.674509
33 U 1.919036
34 S 1.751656
35 S 6.862893
36 U 0.798311
37 S 0.727874
38 U 5.434961
K = 1
K PERIOD SEASON DISTANCE1 13 S 0.582323
Maka dapat diambil keputusan klasifikasi bahwa data test ke 46 termasuk dalam class S (Seeded).
Data test ke-47
PERIOD
SEEDED
SEASON TE TW NC SC NWC
47 ? SPRING 0.71 1.73 0.72 0.99 0.98
PERIOD SEASON DISTANCE
1 S 3.606092
2 U 1.566014
3 S 2.473681
4 U 4.016491
5 S 7.12953
6 U 3.9755
7 U 2.737755
8 S 1.547934
9 U 3.33675
10 S 6.859876
11 S 1.122141
12 U 1.477633
13 S 1.034311
14 U 0.589067
15 S 1.871684
16 U 1.190378
17 S 1.986806
18 U 1.844804
19 U 1.209587
20 S 1.925175
21 S 1.575627
22 U 4.367253
23 U 1.772964
24 S 1.85879
25 U 1.789469
26 S 1.163099
27 S 1.359081
28 U 1.973499
29 U 1.936156
30 S 2.578313
31 U 2.015639
32 S 2.276598
33 U 1.624377
34 S 1.774542
35 S 6.169052
36 U 1.339776
37 S 1.494624
38 U 4.651656
K = 1
K PERIOD SEASON DISTANCE1 14 U 0.589067
Maka dapat diambil keputusan klasifikasi bahwa data test ke 47 masuk dalam class U (Unseeded).
Data test ke-48
PERIOD
SEEDED
SEASON TE TW NC SC NWC
48 ? SPRING 1.67 3.46 1.02 1.89 2.47
PERIOD SEASON DISTANCE
1 S 1.489262
2 U 3.322138
3 S 3.589638
4 U 3.130463
5 S 5.037063
6 U 3.129185
7 U 3.017234
8 S 3.822735
9 U 2.401395
10 S 4.305102
11 S 2.606876
12 U 1.7
13 S 3.517499
14 U 3.112587
15 S 1.67541
16 U 2.599577
17 S 4.430463
18 U 4.261913
19 U 3.346326
20 S 4.350253
21 S 3.948595
22 U 3.166749
23 U 1.808867
24 S 1.578892
25 U 2.122781
26 S 3.09519
27 S 3.528612
28 U 3.99213
29 U 3.699581
30 S 2.981996
31 U 2.787831
32 S 3.118221
33 U 3.017681
34 S 3.610512
35 S 4.83907
36 U 3.79033
37 S 4.083736
38 U 2.789534
K = 1
K PERIOD SEASON DISTANCE1 1 S 1.489262
Maka dapat diambil keputusan klasifikasi bahwa data test ke 48 masuk dalam class S (Seeded).
Data test ke-49
PERIOD
SEEDED
SEASONTE TW NC SC NWC
49 ? SUMMER 0.73 1.51 0.18 1.42 0.71
PERIOD SEASON DISTANCE
1 S 4.002462
2 U 1.879468
3 S 3.042877
4 U 4.545393
5 S 7.618701
6 U 4.551593
7 U 3.129169
8 S 1.588553
9 U 4.035059
10 S 7.26817
11 S 1.985497
12 U 2.242276
13 S 1.520724
14 U 1.399857
15 S 1.919531
16 U 1.203495
17 S 1.640061
18 U 1.475025
19 U 0.867929
20 S 1.613598
21 S 1.598437
22 U 4.429007
23 U 2.049146
24 S 2.121297
25 U 2.303259
26 S 1.488422
27 S 1.380543
28 U 2.345826
29 U 2.321357
30 S 3.118958
31 U 2.447979
32 S 2.788243
33 U 2.005443
34 S 1.840272
35 S 6.759009
36 U 1.717265
37 S 1.77581
38 U 5.149437
K = 1
K PERIOD SEASON DISTANCE1 19 U 0.867929
Maka dapat diambil keputusan klasifikasi bahwa data test ke 49 masuk dalam class U (Unseeded).
Data test ke-50
PERIOD
SEEDED
SEASONTE TW NC SC NWC
50 ? SUMMER 1.79 3.13 1.83 1.82 3.11
PERIOD SEASON DISTANCE
1 S 1.204658
2 U 3.397632
3 S 3.265119
4 U 2.199977
5 S 4.190835
6 U 2.311082
7 U 2.780827
8 S 4.175165
9 U 1.79254
10 S 3.967216
11 S 2.883487
12 U 2.353784
13 S 4.036124
14 U 3.597402
15 S 1.653269
16 U 2.649925
17 S 4.710722
18 U 4.573554
19 U 3.665542
20 S 4.547703
21 S 4.30761
22 U 2.884684
23 U 2.255105
24 S 2.031354
25 U 2.184147
26 S 3.374004
27 S 3.966081
28 U 4.007418
29 U 3.726473
30 S 2.581279
31 U 2.586677
32 S 2.810125
33 U 3.011495
34 S 3.806297
35 S 3.972292
36 U 4.153252
37 S 4.512715
38 U 2.145554
K = 1
K PERIOD SEASON DISTANCE1 1 S 1.204658
Maka dapat diambil keputusan klasifikasi bahwa data test ke 50 masuk dalam class S (Seeded).
Data test ke-51
PERIOD
SEEDED
SEASONTE TW NC SC NWC
51 ? SUMMER 0.19 1.05 0.08 0.40 0.57
PERIOD SEASON DISTANCE
1 S 5.772062
2 U 2.469332
3 S 3.472046
4 U 5.734789
5 S 9.184966
6 U 5.664504
7 U 4.009476
8 S 1.614032
9 U 5.286691
10 S 9.15828
11 S 2.888806
12 U 3.804786
13 S 1.741034
14 U 2.112487
15 S 3.753398
16 U 2.673462
17 S 0.595651
18 U 0.783901
19 U 1.769548
20 S 0.84303
21 S 1.51043
22 U 6.262979
23 U 3.686245
24 S 3.934298
25 U 3.596053
26 S 2.303606
27 S 1.849081
28 U 2.238325
29 U 2.420847
30 S 3.939124
31 U 3.534035
32 S 3.556389
33 U 2.902964
34 S 2.643747
35 S 8.039266
36 U 1.743158
37 S 1.399035
38 U 6.849664
K = 1
K PERIOD SEASON DISTANCE1 17 S 0.595651
Maka dapat diambil keputusan klasifikasi bahwa data test ke 51 masuk dalam class S (Seeded).
Data test ke-52
PERIOD
SEEDED
SEASONTE TW NC SC NWC
52 ? SUMMER 0.00 0.15 0.00 0.04 0.04
PERIOD SEASON DISTANCE
1 S 4.74077
2 U 1.894202
3 S 2.996381
4 U 4.964454
5 S 8.255095
6 U 4.856068
7 U 3.260629
8 S 1.105758
9 U 4.454043
10 S 8.133185
11 S 2.088109
12 U 2.867229
13 S 1.10752
14 U 1.398285
15 S 2.732362
16 U 1.744248
17 S 0.564092
18 U 0.381182
19 U 0.800312
20 S 0.838391
21 S 1.139561
22 U 5.486866
23 U 2.663869
24 S 2.960794
25 U 2.670843
26 S 1.524402
27 S 1.113328
28 U 1.991306
29 U 1.947486
30 S 3.247384
31 U 2.824783
32 S 2.964169
33 U 2.269405
34 S 2.23799
35 S 69.09461
36 U 1.437985
37 S 1.267399
38 U 5.963883
K = 1
K PERIOD SEASON DISTANCE1 18 U 0.381182
Maka dapat diambil keputusan klasifikasi bahwa data test ke 52 masuk dalam class U (Unseeded).
Data test ke-53
PERIOD
SEEDED
SEASONTE TW NC SC NWC
53 ? SUMMER 0.44 0.89 0.83 0.38 0.70
PERIOD SEASON DISTANCE
1 S 4.4982
2 U 1.459418
3 S 2.32839
4 U 4.450382
5 S 7.832452
6 U 4.406348
7 U 3.013503
8 S 1.174904
9 U 3.986352
10 S 7.847637
11 S 1.753596
12 U 2.584357
13 S 1.197873
14 U 1.176223
15 S 2.519186
16 U 1.364808
17 S 0.901721
18 U 0.848175
19 U 0.820122
20 S 0.720139
21 S 1.105215
22 U 5.121933
23 U 2.610613
24 S 2.766225
25 U 2.381407
26 S 1.289457
27 S 1.301614
28 U 1.386579
29 U 1.586695
30 S 2.766406
31 U 2.322434
32 S 2.356183
33 U 1.7492
34 S 1.775922
35 S 6.703753
36 U 1.19042
37 S 1.177115
38 U 5.501436
K = 1
K PERIOD SEASON DISTANCE1 20 S 0.720139
Maka dapat diambil keputusan klasifikasi bahwa data test ke 53 masuk dalam class S (Seeded).
Data test ke-54
PERIOD
SEEDED
SEASONTE TW NC SC NWC
54 ? SUMMER 0.31 1.15 0.01 0.44 0.66
PERIOD SEASON DISTANCE
1 S 4.607483
2 U 1.864296
3 S 2.994144
4 U 4.888763
5 S 8.147208
6 U 4.783931
7 U 3.185765
8 S 1.104853
9 U 4.368764
10 S 7.99194
11 S 2.016978
12 U 2.762788
13 S 1.076197
14 U 0.904434
15 S 2.600346
16 U 1.651787
17 S 0.720417
18 U 0.527352
19 U 0.68374
20 S 0.951998
21 S 1.166019
22 U 5.384134
23 U 2.52646
24 S 2.824978
25 U 2.566905
26 S 1.465537
27 S 1.057308
28 U 2.031625
29 U 1.94651
30 S 3.188903
31 U 2.779316
32 S 2.921216
33 U 2.222026
34 S 2.239241
35 S 7.259463
36 U 1.463011
37 S 1.332479
38 U 5.859872
K = 1
K PERIOD SEASON DISTANCE1 18 U 0.527352
Maka dapat diambil keputusan klasifikasi bahwa data test ke 54 masuk dalam class U (Unseeded).
HASIL KLASIFIKASI
PERIOD
SEEDED
SEASONTE TW NC SC NWC
39 S SPRING 0.81 0.89 1.33 0.43 2.1840 S SPRING 0.39 1.22 0.25 0.46 0.8941 U SUMME
R 0.86 1.24 0.69 0.49 0.6942 U SUMME
R 2.16 2.29 2.12 0.95 1.8243 U SPRING 1.7 2.18 1.45 1.47 2.2
44 U SPRING 1.22 2 2.13 1.13 2.3345 S SPRING 0.07 0.22 0.02 0.08 0.2446 S SPRING 0.49 1.07 0.36 0.87 0.5747 U SPRING 0.71 1.73 0.72 0.99 0.9848 S SPRING 1.67 3.46 1.02 1.89 2.4749 U SUMME
R 0.73 1.51 0.18 1.42 0.7150 S SUMME
R 1.79 3.13 1.83 1.82 3.1151 S SUMME
R 0.19 1.05 0.08 0.40 0.5752 U SUMME
R 0.00 0.15 0.00 0.04 0.0453 S SUMME
R 0.44 0.89 0.83 0.38 0.7054 U SUMME
R 0.31 1.15 0.01 0.44 0.66
DATA SESUNGGUHNYA
PERIOD SEEDED SEASON TE TW NC SC NWC 39 U SPRING 0.81 0.89 1.33 0.43 2.18 40 S SPRING 0.39 1.22 0.25 0.46 0.89 41 S SUMMER 0.86 1.24 0.69 0.49 0.69 42 U SUMMER 2.16 2.29 2.12 0.95 1.82 43 U SPRING 1.70 2.18 1.45 1.47 2.20 44 S SPRING 1.22 2.00 2.13 1.13 2.33 45 S SPRING 0.07 0.22 0.02 0.08 0.24 46 U SPRING 0.49 1.07 0.36 0.87 0.57 47 U SPRING 0.71 1.73 0.72 0.99 0.98 48 S SPRING 1.67 3.46 1.02 1.89 2.47 49 U SUMMER 0.73 1.51 0.18 1.42 0.71 50 S SUMMER 1.79 3.13 1.83 1.82 3.11 51 U SUMMER 0.19 1.05 0.08 0.40 0.57 52 S SUMMER 0.00 0.15 0.00 0.04 0.04 53 S SUMMER 0.44 0.89 0.83 0.38 0.70 54 U SUMMER 0.31 1.15 0.01 0.44 0.66
EVALUASI
Akurasi data=∑ hasil benar
∑ data seluruhx100 %
Akurasi data=1016
x100 %=62.5 %
PENGUJIAN ULANG DENGAN k BERBEDA
Untuk k = 3
PERIOD
SEEDED
SEASONTE TW NC SC NWC
39 U SPRING 0.81 0.89 1.33 0.43 2.1840 S SPRING 0.39 1.22 0.25 0.46 0.8941 S SUMME
R 0.86 1.24 0.69 0.49 0.6942 U SUMME
R 2.16 2.29 2.12 0.95 1.8243 U SPRING 1.7 2.18 1.45 1.47 2.244 U SPRING 1.22 2 2.13 1.13 2.3345 S SPRING 0.07 0.22 0.02 0.08 0.2446 S SPRING 0.49 1.07 0.36 0.87 0.5747 U SPRING 0.71 1.73 0.72 0.99 0.9848 U SPRING 1.67 3.46 1.02 1.89 2.4749 S SUMME
R 0.73 1.51 0.18 1.42 0.7150 U SUMME
R 1.79 3.13 1.83 1.82 3.1151 S SUMME
R 0.19 1.05 0.08 0.40 0.5752 S SUMME
R 0.00 0.15 0.00 0.04 0.0453 S SUMME
R 0.44 0.89 0.83 0.38 0.7054 S SUMME
R 0.31 1.15 0.01 0.44 0.66
Akurasi data= 916
x100 %=56.25 %
Untuk k = 5
PERIOD
SEEDED
SEASONTE TW NC SC NWC
39 U SPRING 0.81 0.89 1.33 0.43 2.1840 S SPRING 0.39 1.22 0.25 0.46 0.8941 U SUMME
R 0.86 1.24 0.69 0.49 0.6942 U SUMME
R 2.16 2.29 2.12 0.95 1.8243 S SPRING 1.7 2.18 1.45 1.47 2.244 U SPRING 1.22 2 2.13 1.13 2.3345 S SPRING 0.07 0.22 0.02 0.08 0.2446 S SPRING 0.49 1.07 0.36 0.87 0.5747 S SPRING 0.71 1.73 0.72 0.99 0.9848 S SPRING 1.67 3.46 1.02 1.89 2.4749 U SUMME
R 0.73 1.51 0.18 1.42 0.7150 S SUMME
R 1.79 3.13 1.83 1.82 3.11
51 S SUMMER 0.19 1.05 0.08 0.40 0.57
52 S SUMMER 0.00 0.15 0.00 0.04 0.04
53 S SUMMER 0.44 0.89 0.83 0.38 0.70
54 U SUMMER 0.31 1.15 0.01 0.44 0.66
Akurasi data=1016
x100 %=62.5 %
Untuk k = 9
PERIOD
SEEDED
SEASONTE TW NC SC NWC
39 U SPRING 0.81 0.89 1.33 0.43 2.1840 S SPRING 0.39 1.22 0.25 0.46 0.8941 S SUMME
R 0.86 1.24 0.69 0.49 0.6942 U SUMME
R 2.16 2.29 2.12 0.95 1.8243 U SPRING 1.7 2.18 1.45 1.47 2.244 U SPRING 1.22 2 2.13 1.13 2.3345 S SPRING 0.07 0.22 0.02 0.08 0.2446 S SPRING 0.49 1.07 0.36 0.87 0.5747 U SPRING 0.71 1.73 0.72 0.99 0.9848 U SPRING 1.67 3.46 1.02 1.89 2.4749 S SUMME
R 0.73 1.51 0.18 1.42 0.7150 U SUMME
R 1.79 3.13 1.83 1.82 3.1151 S SUMME
R 0.19 1.05 0.08 0.40 0.5752 S SUMME
R 0.00 0.15 0.00 0.04 0.0453 S SUMME
R 0.44 0.89 0.83 0.38 0.7054 S SUMME
R 0.31 1.15 0.01 0.44 0.66
Akurasi data= 916
x100 % = 56.25 %
Untuk k = 11
PERIOD
SEEDED
SEASONTE TW NC SC NWC
39 U SPRING 0.81 0.89 1.33 0.43 2.1840 S SPRING 0.39 1.22 0.25 0.46 0.8941 S SUMME
R 0.86 1.24 0.69 0.49 0.6942 U SUMME
R 2.16 2.29 2.12 0.95 1.8243 U SPRING 1.7 2.18 1.45 1.47 2.244 U SPRING 1.22 2 2.13 1.13 2.3345 S SPRING 0.07 0.22 0.02 0.08 0.2446 S SPRING 0.49 1.07 0.36 0.87 0.5747 S SPRING 0.71 1.73 0.72 0.99 0.9848 U SPRING 1.67 3.46 1.02 1.89 2.4749 S SUMME
R 0.73 1.51 0.18 1.42 0.7150 U SUMME
R 1.79 3.13 1.83 1.82 3.1151 U SUMME
R 0.19 1.05 0.08 0.40 0.5752 S SUMME
R 0.00 0.15 0.00 0.04 0.0453 S SUMME
R 0.44 0.89 0.83 0.38 0.7054 S SUMME
R 0.31 1.15 0.01 0.44 0.66
Akurasi data= 916
x100 % = 56.25 %
Untuk k = 13
PERIOD
SEEDED
SEASONTE TW NC SC NWC
39 U SPRING 0.81 0.89 1.33 0.43 2.1840 S SPRING 0.39 1.22 0.25 0.46 0.8941 S SUMME
R 0.86 1.24 0.69 0.49 0.6942 U SUMME
R 2.16 2.29 2.12 0.95 1.8243 U SPRING 1.7 2.18 1.45 1.47 2.244 U SPRING 1.22 2 2.13 1.13 2.3345 S SPRING 0.07 0.22 0.02 0.08 0.2446 S SPRING 0.49 1.07 0.36 0.87 0.5747 S SPRING 0.71 1.73 0.72 0.99 0.9848 U SPRING 1.67 3.46 1.02 1.89 2.4749 S SUMME
R 0.73 1.51 0.18 1.42 0.7150 U SUMME
R 1.79 3.13 1.83 1.82 3.1151 U SUMME
R 0.19 1.05 0.08 0.40 0.57
52 S SUMMER 0.00 0.15 0.00 0.04 0.04
53 S SUMMER 0.44 0.89 0.83 0.38 0.70
54 S SUMMER 0.31 1.15 0.01 0.44 0.66
Akurasi data= 916
x100 % = 56.25 %
ANALISIS
Dataset diatas terdiri dari 6 atribut dengan komposisi 5 data kontinyu dan 1 data categorical.
Pada dataset diatas, jenis season pada data sangat mempengaruhi nilai jarak karena nilai jarak pada atribut season bernilai besar (1) atau kecil sekali (0) sedangkan nilai jarak pada data kontinyu yang ada tidak terlalu besar.
Pemilihan jumlah k yang paling tepat perlu dijajaki agar error rate bisa diperkecil dan akurasi data semakin besar.
Nilai k umumnya ditentukan dalam jumlah ganjil untuk menghindari munculnya jumlah jarak yang sama dalam proses pengklasifikasian
Diberikan nilai k = 1, dengan asumsi dilakukan complete storage k-nearest neighbor, dimana seluruh data pada training set dipakai sebagai prototype. Pakai training set untuk klasifikasi data pada validation-set dengan k=1. Catat score-nya, misalnya classification rate pada validation set. Kemudian dilakukan update k = k+2. Hasil k dengan nilai akurasi terbaik maka dapat digunakan untuk menguji data test selanjutnya dengan hasil yang lebih baik.
Pada hasil pengujian diatas, akurasi terbaik diperoleh pada nilai k = 1 atau k = 5
2.2 KLASIFIKASI BERDASAR TEOREMA BAYES
Bayesian classification adalah pengklasifikasi statistik yang dapat digunakan untuk memprediksi probabilitas keanggotaan suatu class. Bayesian classification didasarkan pada teorema bayes yang memiliki kemampuan klasifikasi serupa dengan decision tree dan neural network.
Teorema Bayes memiliki bentuk umum sebagai berikut :
yang mana : X = data dengan class yang belum diketahui H = hipotesis data X merupakan suatu class spesifik
P (H|X )=P ( X|H )P (H )
P( X )
P(H|X) = probabilitas hipotesis H berdasar kondisi X (posteriori probability) P(H) = probabilitas hipotesis H (prior probability) P(X|H) = probabilitas X berdasar kondisi pada hipotesis H P(X) = probabilitas dari X
X termasuk dalam kelas Ci jika peluang P(Ci|X) merupakan tertinggi diantara semua P(Ck|X) untuk semua klas k.
Untk klasifikasi dengan data kontinyu digunakan rumus:
exp2
1 2
2
2
2
ij
ijix
ijjii yYxXP
Keterangan: P menyatakan peluang Xi menyatakan atribut ke i. xi menyatakan nilai atribut ke i. Y menyatakan kelas yang dicari. yi menyatakan sub kelas Y yang dicari. µ menyatakan rata-rata dari seluruh atribut ( ) . menyatakan varian dari seluruh atribut ( ).
Data Testing
PERIOD SEEDED SEASON TE TW NC SC NWC39 ? SPRING 0.81 0.89 1.33 0.43 2.1840 ? SPRING 0.39 1.22 0.25 0.46 0.8941 ? SUMME
R 0.86 1.24 0.69 0.49 0.6942 ? SUMMER 2.16 2.29 2.12 0.95 1.8243 ? SPRING 1.7 2.18 1.45 1.47 2.244 ? SPRING 1.22 2 2.13 1.13 2.3345 ? SPRING 0.07 0.22 0.02 0.08 0.2446 ? SPRING 0.49 1.07 0.36 0.87 0.5747 ? SPRING 0.71 1.73 0.72 0.99 0.9848 ? SPRING 1.67 3.46 1.02 1.89 2.4749 ? SUMMER 0.73 1.51 0.18 1.42 0.7150 ? SUMMER 1.79 3.13 1.83 1.82 3.1151 ? SUMMER 0.19 1.05 0.08 0.40 0.5752 ? SUMMER 0.00 0.15 0.00 0.04 0.0453 ? SUMMER 0.44 0.89 0.83 0.38 0.70
54 ? SUMMER 0.31 1.15 0.01 0.44 0.66
Untuk Data kontinyu, digunakan rumus :
P(Xi=xi | y=yi)= 1
√2 π σ ij2
exp
−(x i−µij)2
2 σij2
P(TE|CLASS=SEEDED)
µ =
1.69+0.81+2.48+0.37+3.38+0.69+0.44+1.13+0.17+0.40+0.52+1.62+0.63+0.42+0.88+1.25+1.11+3.43+0.3919
=1,03
σ 2=
(1.69−1.03 )2+(0.81−1.03 )2+ (2.48−1.03 )2+(0.37−1.03 )2+(3.38−1.03 )2+…+(0.39−1.03 )2
18
= 0,97 √0,97=0,99
P(TE|CLASS=UNSEEDED)
µ
¿ 0.74+1.44+0.84+0.37+1.33+1.42+0.76+0.88+0.25+0.78+2.73+0.90+0.93+0.64+0.30+0.76+1.08+0.54+2.5319
=1,13
σ 2=
(0.74−1.13 )2+(1.44−1.13 )2+ (0.37−1.13 )2+ (1.33−1.13 )2+(1.42−1.13 )2+…+(2.53−1.13 )2
18
= 0,44 √0,42=0,66
P(TW|CLASS=SEEDED)
µ
¿ 3.73+0.86+4.61+0.84+5.56+1.46+1.05+2.28+0.55+0.34+0.79+2.54+1.31+1.23+1.32+1.00+0.8+2.55+0.4419
= 1,75
σ 2=
(3.73−1.75 )2+( 4.61−1.75 )2+(0.84−1.75 )2+(5.56−1.75 )2+(1.46−1.75 )2+…+(1.05−1.75 )2
18
= 1,34 √1,34=1,16
P(TW|CLASS=UNSEEDED)
µ
¿ 0.78+2.01+2.39+1.37+2.31+2.79+1.24+1.58+0.77+1.45+2.09+2.45+2.11+0.43+0.69+1.25+0.99+0.43+3.1819
= 1,60
σ 2=
(0.78−1.60 )2+ (2.01−1.60 )2+(2.39−1.60 )2+ (1.37−1.60 )2+(2.31−1.60 )2+…+(3.18−1.60 )2
18
= 0,80 √0,80=0,89
P(NC|CLASS=SEEDED)
µ
¿ 1.65+2.39+4.16+0.26+2.76+1.07+0.24+0.97+0.13+0.45+0.42+0.94+0.76+0.13+1.87+2.04+1.46+5.08+0.4919
=1.44
σ 2=
(1.65−1.44 )2+ (2.39−1.44 )2+ (4.16−1.44 )2+(0.26−1.44 )2+(2.76−1.44 )2+…+(0.49−1.44 )2
18
= 0,97 √0,97=0,99
P(NC|CLASS=UNSEEDED)
µ
¿ 1.09+2.96+2.76+1.08+2.53+1.42+0.7+1.06+0.1+0.38+2.24+0.52+1.19+1.5+1.03+1.85+1.44+0.66+3.2719
=1,46
σ 2=
(1.09−1.46 )2+(2.96−1.46 )2+(2.76−1.46 )2+(1.08−1.46 )2+(2.53−1.46 )2+…+(3.27−1.46 )2
18
= 0,45 √0,45=0,67
P(SC|CLASS=SEEDED)
µ
¿ 1.8+0.36+2.16+0.47+3.1+0.64+0.44+1.66+0.27+0.43+0.47+1.59+0.71+0.59+0.58+0.71+1.48+1.77+0.5519
= 1,04
σ 2=
(1.8−1.04 )2+ (0.36−1.04 )2+(2.16−1.04 )2+(0.47−1.04 )2+(3.1−1.04 )2+…+ (0.55−1.04 )2
18
= 0,97 √0,97=0,99
P(SC|CLASS=UNSEEDED)
µ
¿ 0.79+1.27+0.87+0.85+1.08+1.08+0.67+1.13+0.3+0.58+4.02+1.32+0.85+0.24+0.22+1.36+1+0.73+2.6819
= 1,11
σ 2=
(0.79−1.11 )2+ (1.27−1.11 )2+ (0.87−1.11 )2+ (0.85−1.11 )2+ (1.08−1.11 )2+…+(2.68−1.11 )2
18
= 0,45 √0,45=0,67
P(NWC|CLASS=SEEDED)
µ
¿ 3.33+2.06+6+0.9+5.06+1.95+0.94+2.21+0.35+0.44+0.53+1.73+1.28+0.91+2.97+2.22+0.4+4.2+0.5119
= 2,00
σ 2= (0.79−2.00 )2+ (2.06−2.00 )2+(6−2.00 )2+(0.9−2.00 )2+(5.06−2.00 )2+…+2.002
18
= 1,73 √1,73=1,31
P(NWC|CLASS=UNSEEDED)
µ
¿ 1.59+4.05+4.17+3.45+3.65+1.22+0.94+1.46+0.34+0.67+2.52+2.18+2.31+1.15+1.88+2.17+1.64+0.91+3.619
= 2.10
σ 2=
(1.59−2.10 )2+( 4.05−2.10 )2+ (4.17−2.10 )2+(3.45−2.10 )2+ (3.65−2.10 )2+…+ (3.6−2.10 )2
18
= 1,69 √1,69=1,30
39 ? SPRING 0.81 0.89 1.33 0.43 2.18
P (UNSEEDED ) = 1938
=0,5
P(SEASON =SPRING | CLASS = UNSEEDED) = 5
19 = 0,2632
P ( TE=0,81 | CLASS=UNSEEDED) = 1
√2 π 0,66 exp
−(0,81−1,03)2
2 x 0,44 = 0,6 exp−0,055 = 0,57
P (TW=0,89 | CLASS=UNSEEDED ) = 1
√2 π 0,89 exp
−(0,89−1,6)2
2 x0,8 = 0,45 exp−0,315 = 0,33
P ( NC=1,33 | CLASS=UNSEEDED) = 1
√2 π 0,81 exp
−(1,33−1,46)2
2 x0,65 = 0,494 exp−0,013 = 0,49
P (SC=0,43 | CLASS=UNSEEDED ) = 1
√2 π 0,67 exp
−(0,43−1,11)2
2 x 0,45 = 0,597 exp−0,514 = 0,357
P (NWC=2,18 | CLASS=UNSEEDED) = 1
√2 π 1,3 exp
−(2,18−2,1)2
2 x 1,69 = 0,31 exp−0,002 = 0,31
P ( UNSEEDED ) x P(SPRING | UNSEEDED) x P(TE = 0,81 | CLASS = UNSEEDED) x
P(TW = 0,89 | CLASS = UNSEEDED) x P(NC = 1,33 | CLASS = UNSEEDED) x P(SC =
0,43 | CLASS = UNSEEDED) x P(NWC = 2,18 | CLASS = UNSEEDED) = 0,5 x 0,2632 x
0,57 x 0,33 x 0,49 x 0,357 x 0, 31 = 0,0013
P (SEEDED ) = 1938
=0,5
P(SEASON =SPRING | CLASS = SEEDED) = 5
19 = 0,2632
P ( TE=0,81 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(0,81−1,13)2
2 x0,97 = 0,4 exp−0,0528 = 0,38
P (TW=0,89 | CLASS=SEEDED ) = 1
√2 π 1,16 exp
−(0,89−1,75)2
2 x1,34 = 0,345 exp−0,276 = 0,262
P ( NC=1,33 | CLASS=SEEDED) = 1
√2 π 1,02 exp
−(1,33−1,44)2
2 x1,05 = 0,392 exp−0,0058 = 0,39
P (SC=0,43 | CLASS=SEEDED ) = 1
√2 π 0,99 exp
−(0,43−1,04)2
2 x0,97 = 0,4 exp−0,19 = 0,33
P (NWC=2,18 | CLASS=SEEDED) = 1
√2 π 1,31 exp
−(2,18−2,0)2
2 x 1,73 = 0,3 exp−0,0094 = 0,297
P ( SEEDED ) x P(SPRING | SEEDED) x P(TE = 0,81 | CLASS = SEEDED) x P(TW =
0,89 | CLASS = SEEDED) x P(NC = 1,33 | CLASS = SEEDED) x P(SC = 0,43 | CLASS =
SEEDED) x P(NWC = 2,18 | CLASS = SEEDED) = 0,5 x 0,2632 x 0,38 x 0,262 x 0,39 x
0,33 x 0, 297 = 0,0005
Jadi dapat disimpulkan bahwa data ke – 39 termasuk kelas UNSEEDED
40 ? SPRING 0.39 1.22 0.25 0.46 0.89
P(SEEDED ) = 1938
= 0,5
P(SEASON =SPRING | CLASS = SEEDED) = 5
19 = 0,2632
P (TE=0,39 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(0,39−1,13)2
2 x0,97 = 0,4 exp−0,28 = 0,3
P (TW=1,22 | CLASS=SEEDED) = 1
√2 π 1,16 exp
−(1,22−1,75)2
2 x 1,34 = 0,345 exp−0,1 = 0,312
P (NC=0,25 | CLASS=SEEDED) = 1
√2 π 1,02 exp
−(0,25−1,44)2
2 x1,05 = 0,39 exp−0,67 = 0,2
P (SC=0,46 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(0,46−1,04 )2
2 x0,97 = 0,4 exp−0,17 = 0,337
P (NWC=0,89 | CLASS=SEEDED) = 1
√2 π 1,31 exp
−(0,89−2,0)2
2 x 1,73 = 0,3 exp−0,356 = 0,21
P(SEEDED ) x P(SPRING | SEEDED) x P(TE = 0,39 | CLASS = SEEDED) x P(TW =
1,22 | CLASS = SEEDED) x P(NC = 0,25 | CLASS = SEEDED) x P(SC = 0,46 | CLASS =
SEEDED) x P(NWC = 0,89 | CLASS = SEEDED) = 0,5 x 0,2632 x 0,3x 0,312 x 0,2 x
0,337 x 0,21 = 0,0002
P(UNSEEDED ) = 1938
= 0,5
P(SEASON =SPRING | CLASS = UNSEEDED) = 5
19 = 0,2632
P (TE=0,39 | CLASS=UNSEEDED) = 1
√2 π 0,66 exp
−(0,39−1,03)2
2 x 0,44 = 0,6 exp−0,465 = 0,376
P (TW=1,22 | CLASS=UNSEEDED) = 1
√2 π 0,89 exp
−(1,22−1,6)2
2 x0,8 = 0,45 exp−0,09 = 0,167
P (NC=0,25 | CLASS=UNSEEDED) = 1
√2 π 0,81 exp
−(0,25−1,46)2
2 x0,65 = 0,494 exp−1,126 = 0,16
P (SC=0,46 | CLASS=UNSEEDED) = 1
√2 π 0,67 exp
−(0,46−1,11)2
2 x 0,45 = 0,597 exp−0,47 = 0,373
P (NWC=0,89 | CLASS=UNSEEDED) = 1
√2 π 1,3 exp
−(0,89−2,1)2
2 x 1,69 = 0,31 exp−0,433 = 0,2
P(UNSEEDED ) x P(SPRING | UNSEEDED) x P(TE = 0,39 | CLASS = UNSEEDED) x
P(TW = 1,22 | CLASS = UNSEEDED) x P(NC = 0,25 | CLASS = UNSEEDED) x P(SC =
0,46 | CLASS = UNSEEDED) x P(NWC = 0,89 | CLASS = UNSEEDED) = 0,5 x 0,2632 x
0,376x 0,167 x 0,16 x 0,373 x 0,2 = 0,0006
Jadi dapat disimpulkan bahwa data ke – 40 termasuk kelas UNSEEDED
41 ? SUMMER 0.86 1.24 0.69 0.49 0.69
P(SEEDED ) = 1938
= 0,5
P(SEASON =SUMMER | CLASS = SEEDED) = 3
19 = 0,1579
P (TE=0,86 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(0,86−1,13)2
2 x0,97 = 0,4 exp−0,037 = 0,38
P (TW=1,24 | CLASS=SEEDED) = 1
√2 π 1,16 exp
−(1,24−1,75)2
2 x1,34 = 0,345 exp−0,09 = 0,315
P (NC=0,69 | CLASS=SEEDED) = 1
√2 π 1,02 exp
−(0,69−1,44)2
2 x1,05 = 0,39 exp−0,268 = 0,29
P (SC=0,49 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(0,49−1,04)2
2 x0,97 = 0,4 exp−0,156 = 0,342
P (NWC=0,69 | CLASS=SEEDED) = 1
√2 π 1,31 exp
−(0,69−2,0)2
2 x 1,73 = 0,3 exp−0,49 = 0,18
P(SEEDED ) x P(SUMMER | SEEDED ) x P(TE = 0,86 | CLASS = SEEDED) x P(TW =
1,24 | CLASS = SEEDED) x P(NC = 0,69 | CLASS = SEEDED) x P(SC = 0,49 | CLASS =
SEEDED) x P(NWC = 0,69 | CLASS = SEEDED) = 0,5 x 0,1579 x 0,38 x 0,315 x 0,29x
0,342 x 0,18 = 0,0002
P(UNSEEDED ) = 1938
= 0,5
P(SEASON =SUMMER | CLASS = UNSEEDED) = 3
19 = 0,157
P (TE=0,86 | CLASS=UNSEEDED) = 1
√2 π 0,66 exp
−(0,86−1,03)2
2 x0,44 = 0,6 exp−0,033 = 0,58
P (TW=1,24 | CLASS=UNSEEDED) = 1
√2 π 0,89 exp
−(1,24−1,6)2
2 x0,8 = 0,45 exp−0,081 = 0,415
P (NC=0,69 | CLASS=UNSEEDED) = 1
√2 π 0,81 exp
−(0,69−1,46)2
2 x0,65 = 0,494 exp−0,456 = 0,313
P (SC=0,49 | CLASS=UNSEEDED) = 1
√2 π 0,67 exp
−(0,49−1,11)2
2 x 0,45 = 0,597 exp−0,427 = 0,39
P (NWC=0,69 | CLASS=UNSEEDED) = 1
√2 π 1,3 exp
−(0,69−2,1)2
2 x 1,69 = 0,31 exp−0,59 = 0,17
P(UNSEEDED ) x P(SUMMER | UNSEEDED) x P(TE = 0,86 | CLASS = UNSEEDED) x
P(TW = 1,24 | CLASS = UNSEEDED) x P(NC = 0,69 | CLASS = UNSEEDED) x P(SC =
0,49 | CLASS = UNSEEDED) x P(NWC = 0,69 | CLASS = UNSEEDED) = 0,5 x 0,157 x
0,58x 0,415 x 0,313 x 0,39 x 0,17 = 0,0004
Jadi dapat disimpulkan bahwa data ke – 41 termasuk kelas UNSEEDED
42 ? SUMMER 2.16 2.29 2.12 0.95 1.82
P (UNSEEDED ) = 1938
=0,5
P(SEASON =SUMMER | CLASS = UNSEEDED) = 3
19 = 0,157
P (TE=2,16 | CLASS=UNSEEDED) = 1
√2 π 0,66 exp
−(2,16−1,03)2
2 x0,44 = 0,6 exp−1,45 = 0,14
P (TW=2,29 | CLASS=UNSEEDED) = 1
√2 π 0,89 exp
−(2,29−1,6)2
2 x0,8 = 0,45 exp−0,29 = 0,34
P (NC=2,12 | CLASS=UNSEEDED) = 1
√2 π 0,81 exp
−(2,12−1,46)2
2 x0,65 = 0,494 exp−0,335 = 0,353
P (SC=0,95 | CLASS=UNSEEDED) = 1
√2 π 0,67 exp
−(0,95−1,11)2
2 x 0,45 = 0,597 exp−0,028 = 0,58
P (NWC=1,82 | CLASS=UNSEEDED) = 1
√2 π 1,3 exp
−(1,82−2,1)2
2 x 1,69 = 0,31 exp−0,023 = 0,3
P(UNSEEDED ) x P(SUMMER | UNSEEDED) x P(TE = 2,16 | CLASS = UNSEEDED) x
P(TW = 2,29 | CLASS = UNSEEDED) x P(NC = 2,12 | CLASS = UNSEEDED) x P(SC =
0,95 | CLASS = UNSEEDED) x P(NWC = 1,82 | CLASS = UNSEEDED) = 0,5 x 0,157 x
0,14x 0,34 x 0,353 x 0,58 x 0,3 = 0,000229
P(SEEDED ) = 1938
= 0,5
P(SEASON =SUMMER | CLASS = SEEDED) = 3
19 = 0,157
P (TE=2,16 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(2,16−1,13)2
2 x0,97 = 0,4 exp−0,55 = 0,23
P (TW=2,29 | CLASS=SEEDED) = 1
√2 π 1,16 exp
−(2,29−1,75)2
2 x1,34 = 0,345 exp−0,11 = 0,31
P (NC=2,12 | CLASS=SEEDED) = 1
√2 π 1,02 exp
−(2,12−1,44)2
2 x1,05 = 0,39 exp−0,22 = 0,313
P (SC=0,95 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(0,95−1,04)2
2 x0,97 = 0,4 exp−0,0042 = 0,399
P (NWC=1,82 | CLASS=SEEDED) = 1
√2 π 1,31 exp
−(1,82−2,0)2
2 x 1,73 = 0,3 exp−0,0094 = 0,297
P(SEEDED ) x P(SUMMER | SEEDED ) x P(TE = 2,16 | CLASS = SEEDED) x P(TW =
2,29 | CLASS = SEEDED) x P(NC = 2,12 | CLASS = SEEDED) x P(SC = 0,95 | CLASS =
SEEDED) x P(NWC = 1,82 | CLASS = SEEDED) = 0,5 x 0,157 x 0,23 x 0,31 x 0,313 x
0,399 x 0,297 = 0,000207
Jadi dapat disimpulkan bahwa data ke – 42 termasuk kelas UNSEEDED
43 ? SPRING 1.70 2.18 1.45 1.47 2.20
P (UNSEEDED ) = 1938
=0,5
P(SEASON =SPRING | CLASS = UNSEEDED) = 5
19 = 0,2632
P (TE=1,70 | CLASS=UNSEEDED) = 1
√2 π 0,66 exp
−(1,70−1,03)2
2 x 0,44 = 0,6 exp−0,51 = 0,36
P (TW=2,18 | CLASS=UNSEEDED) = 1
√2 π 0,89 exp
−(2,18−1,6)2
2 x0,8 = 0,45 exp−0,21 = 0,36
P (NC=1,45 | CLASS=UNSEEDED) = 1
√2 π 0,81 exp
−(1,45−1,46)2
2 x0,65 = 0,494 exp−0,00007 = 0,49
P (SC=1,47 | CLASS=UNSEEDED) = 1
√2 π 0,67 exp
−(1,47−1,11)2
2 x 0,45 = 0,597 exp−0,144 = 0,51
P (NWC=2,20 | CLASS=UNSEEDED) = 1
√2 π 1,3 exp
−(2,20−2,1)2
2 x 1,69 = 0,31 exp−0,003 = 0,31
P(UNSEEDED ) x P(SPRING | UNSEEDED) x P(TE = 1,7 | CLASS = UNSEEDED) x
P(TW = 2,18 | CLASS = UNSEEDED) x P(NC = 1,45 | CLASS = UNSEEDED) x P(SC =
1,47 | CLASS = UNSEEDED) x P(NWC = 2,20 | CLASS = UNSEEDED) = 0,5 x 0,2632 x
0,36x 0,36 x 0,49 x 0,51 x 0,31 = 0,00132
P(SEEDED ) = 1938
= 0,5
P(SEASON =SPRING | CLASS = SEEDED) = 5
19 = 0,2632
P (TE=1,7 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(1,7−1,13)2
2 x 0,97 = 0,4 exp−0,167 = 0,338
P (TW=2,18 | CLASS=SEEDED) = 1
√2 π 1,16 exp
−(2,18−1,75)2
2 x1,34 = 0,345 exp−0,069 = 0,322
P (NC=1,45 | CLASS=SEEDED) = 1
√2 π 1,02 exp
−(1,45−1,44)2
2 x1,05 = 0,39 exp−0,0000476 = 0,39
P (SC=1,47 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(1,47−1,04)2
2 x0,97 = 0,4 exp−0,095 = 0,364
P (NWC=2,20 | CLASS=SEEDED) = 1
√2 π 1,31 exp
−(2,20−2,0)2
2 x 1,73 = 0,3 exp−0,0115 = 0,296
P(SEEDED ) x P(SPRING | SEEDED ) x P(TE = 1,7 | CLASS = SEEDED) x P(TW = 2,18
| CLASS = SEEDED) x P(NC = 1,45 | CLASS = SEEDED) x P(SC = 1,47 | CLASS =
SEEDED) x P(NWC = 2,20 | CLASS = SEEDED) = 0,5 x 0,2632 x 0,338 x 0,322 x 0,39 x
0,364 x 0,296 = 0,0006018
Jadi dapat disimpulkan bahwa data ke – 43 termasuk kelas UNSEEDED
44 ? SPRING 1.22 2.00 2.13 1.13 2.33
P(SEEDED ) = 1938
= 0,5
P(SEASON =SPRING | CLASS = SEEDED) = 5
19 = 0,2632
P (TE=1,22 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(1,22−1,13)2
2 x 0,97 = 0,4 exp−0,004 = 0,4
P (TW=2,00 | CLASS=SEEDED) = 1
√2 π 1,16 exp
−(2,00−1,75)2
2 x1,34 = 0,345 exp−0,023 = 0,34
P (NC=2,13 | CLASS=SEEDED) = 1
√2 π 1,02 exp
−(2,13−1,44)2
2 x1,05 = 0,39 exp−0,23 = 0,3
P (SC=1,13 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(1,13−1,04)2
2 x0,97 = 0,4 exp−0,04 = 0,38
P (NWC=2,33 | CLASS=SEEDED) = 1
√2 π 1,31 exp
−(2,33−2,0)2
2 x 1,73 = 0,3 exp−0,095 = 0,27
P(SEEDED ) x P(SPRING | SEEDED ) x P(TE = 1,22 | CLASS = SEEDED) x P(TW =
2,00 | CLASS = SEEDED) x P(NC = 2,13 | CLASS = SEEDED) x P(SC = 1,13 | CLASS =
SEEDED) x P(NWC = 2,33 | CLASS = SEEDED) = 0,5 x 0,2632 x 0,4 x 0,34 x 0,3x 0,38
x 0,27 = 0,00055
P (UNSEEDED ) = 1938
=0,5
P(SEASON =SPRING | CLASS = UNSEEDED) = 5
19 = 0,2632
P (TE=1,22 | CLASS=UNSEEDED) = 1
√2 π 0,66 exp
−(1,22−1,03)2
2 x 0,44 = 0,6 exp−0,041 = 0,576
P (TW=2,00 | CLASS=UNSEEDED) = 1
√2 π 0,89 exp
−(2,00−1,6)2
2 x0,8 = 0,45 exp−0,1 = 0,407
P (NC=2,13 | CLASS=UNSEEDED) = 1
√2 π 0,81 exp
−(2,13−1,46)2
2 x0,65 = 0,494 exp−0,345 = 0,35
P (SC=1,13 | CLASS=UNSEEDED) = 1
√2 π 0,67 exp
−(1,13−1,11)2
2 x 0,45 = 0,597 exp−0,00044 = 0,596
P (NWC=2,33 | CLASS=UNSEEDED) = 1
√2 π 1,3 exp
−(2,33−2,1)2
2 x 1,69 = 0,31 exp−0,0156 = 0,305
P(UNSEEDED ) x P(SPRING | UNSEEDED) x P(TE = 1,22 | CLASS = UNSEEDED) x
P(TW = 2,00 | CLASS = UNSEEDED) x P(NC = 2,13 | CLASS = UNSEEDED) x P(SC =
1,13 | CLASS = UNSEEDED) x P(NWC = 2,33 | CLASS = UNSEEDED) = 0,5 x 0,2632 x
0,576x 0,407 x 0,35 x 0,596 x 0,35 = 0,00225
Jadi dapat disimpulkan bahwa data ke – 44 termasuk kelas UNSEEDED
45 ? SPRING 0.07 0.22 0.02 0.08 0.24
P(SEEDED ) = 1938
= 0,5
P(SEASON =SPRING | CLASS = SEEDED) = 5
19 = 0,2632
P (TE=0,07 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(0,07−1,13)2
2 x0,97 = 0,4 exp−0,58 = 0,22
P (TW=0,22 | CLASS=SEEDED) = 1
√2 π 1,16 exp
−(0,22−1,75)2
2 x1,34 = 0,345 exp−0,873 = 0,14
P (NC=0,02 | CLASS=SEEDED) = 1
√2 π 1,02 exp
−(0,02−1,44)2
2 x1,05 = 0,39 exp−0,96 = 0,15
P (SC=0,08 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(0,08−1,04)2
2 x0,97 = 0,4 exp−0,475 = 0,25
P (NWC=0,24 | CLASS=SEEDED) = 1
√2 π 1,31 exp
−(0,24−2,0)2
2 x 1,73 = 0,3 exp−0,895 = 0,12
P(SEEDED ) x P(SPRING | SEEDED ) x P(TE = 0,07 | CLASS = SEEDED) x P(TW =
0,22 | CLASS = SEEDED) x P(NC = 0,02 | CLASS = SEEDED) x P(SC = 0,08 | CLASS =
SEEDED) x P(NWC = 0,24 | CLASS = SEEDED) = 0,5 x 0,2632 x 0,22 x 0,14 x 0,15x
0,25 x 0,12 = 0,0000182
P (UNSEEDED ) = 1938
=0,5
P(SEASON =SPRING | CLASS = UNSEEDED) = 5
19 = 0,2632
P (TE=0,07 | CLASS=UNSEEDED) = 1
√2 π 0,66 exp
−(0,07−1,03)2
2 x0,44 = 0,6 exp−1,047 = 0,21
P (TW=0,22 | CLASS=UNSEEDED) = 1
√2 π 0,89 exp
−(0,22−1,6)2
2 x0,8 = 0,45 exp−1,19 = 0,137
P (NC=0,02 | CLASS=UNSEEDED) = 1
√2 π 0,81 exp
−(0,02−1,46)2
2 x0,65 = 0,494 exp−1,595 = 0,1
P (SC=0,08 | CLASS=UNSEEDED) = 1
√2 π 0,67 exp
−(0,08−1,11)2
2 x 0,45 = 0,597 exp−1,178 = 0,184
P (NWC=0,24 | CLASS=UNSEEDED) = 1
√2 π 1,3 exp
−(0,24−2,1)2
2 x 1,69 = 0,31 exp−1,023 = 0,11
P(UNSEEDED ) x P(SPRING | UNSEEDED) x P(TE = 0,07 | CLASS = UNSEEDED) x
P(TW = 0,22 | CLASS = UNSEEDED) x P(NC = 0,02 | CLASS = UNSEEDED) x P(SC =
0,08 | CLASS = UNSEEDED) x P(NWC = 0,24 | CLASS = UNSEEDED) = 0,5 x 0,2632 x
0,21x 0,137 x 0,1 x 0,184 x 0,11 = 0,00000766
Jadi dapat disimpulkan bahwa data ke – 45 termasuk kelas SEEDED
46 ? SPRING 0.49 1.07 0.36 0.87 0.57
P (UNSEEDED ) = 1938
=0,5
P(SEASON =SPRING | CLASS = UNSEEDED) = 5
19 = 0,2632
P (TE=0,49 | CLASS=UNSEEDED) = 1
√2 π 0,66 exp
−(0,49−1,03)2
2 x 0,44 = 0,6 exp−0,33 = 0,43
P (TW=1,07 | CLASS=UNSEEDED) = 1
√2 π 0,89 exp
−(1,07−1,6)2
2 x0,8 = 0,45 exp−0,175 = 0,37
P (NC=0,36 | CLASS=UNSEEDED) = 1
√2 π 0,81 exp
−(0,36−1,46)2
2 x0,65 = 0,494 exp−0,93 = 0,19
P (SC=0,87 | CLASS=UNSEEDED) = 1
√2 π 0,67 exp
−(0,87−1,11)2
2 x 0,45 = 0,597 exp−0,064 = 0,56
P (NWC=0,57 | CLASS=UNSEEDED) = 1
√2 π 1,3 exp
−(0,57−2,1)2
2 x 1,69 = 0,31 exp−0,69 = 0,15
P(UNSEEDED ) x P(SPRING | UNSEEDED) x P(TE = 0,49 | CLASS = UNSEEDED) x
P(TW = 1,07 | CLASS = UNSEEDED) x P(NC = 0,36 | CLASS = UNSEEDED) x P(SC =
0,87 | CLASS = UNSEEDED) x P(NWC = 0,57 | CLASS = UNSEEDED) = 0,5 x 0,2632 x
0,43x 0,37 x 0,19 x 0,56 x 0,15 = 0,000334
P(SEEDED ) = 1938
= 0,5
P(SEASON =SPRING | CLASS = SEEDED) = 5
19 = 0,2632
P (TE=0,49 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(0,49−1,13)2
2 x0,97 = 0,4 exp−0,21 = 0,324
P (TW=1,07 | CLASS=SEEDED) = 1
√2 π 1,16 exp
−(1,07−1,75)2
2 x1,34 = 0,345 exp−0,172 = 0,29
P (NC=0,36 | CLASS=SEEDED) = 1
√2 π 1,02 exp
−(0,36−1,44 )2
2 x1,05 = 0,39 exp−0,55 = 0,225
P (SC=0,87 | CLASS=SEEDED) = 1
√2 π 0,99 exp
−(0,87−1,04 )2
2 x0,97 = 0,4 exp−0,015 = 0,394
P (NWC=0,57 | CLASS=SEEDED) = 1
√2 π 1,31 exp
−(0,57−2,0)2
2 x 1,73 = 0,3 exp−0,59 = 0,166
P(SEEDED ) x P(SPRING | SEEDED ) x P(TE = 0,49 | CLASS = SEEDED) x P(TW =
1,07 | CLASS = SEEDED) x P(NC = 0,36 | CLASS = SEEDED) x P(SC = 0,87 | CLASS =
SEEDED) x P(NWC = 0,57 | CLASS = SEEDED) = 0,5 x 0,2632 x 0,324 x 0,29 x 0,225 x
0,394 x 0,166 = 0,000182
Jadi dapat disimpulkan bahwa data ke – 46 termasuk kelas UNSEEDED
47 ? SPRING 0.71 1.73 0.72 0.99 0.98
P (UNSEEDED ) = 1938
=0,5
P(SEASON =SPRING | CLASS = UNSEEDED) = 5
19 = 0,2632
P(TE = 0,71 | CLASS = UNSEEDED) = 1
√2.3,14 (0.66)2exp
−(0.71−1.03)2
2 .(0.66)2
=0,5375
P(TW = 1,73 | CLASS = UNSEEDED) = 1
√2.3,14 .(0,89)2exp
−(1,73−1,60)2
2 (0,89)2
=¿ 0,4436
P(NC = 0,72 | CLASS = UNSEEDED) = 1
√2.3,14 . ( 0,81 )2exp
−( 0,72−1,46 )2
2 (0,81 )2 =0,3245
P(SC = 0,99 | CLASS = UNSEEDED) = 1
√2.3,14 .(0,67)2exp
−(0,99−1,11)2
2 (0,67)2
=0,5861
P(NWC = 0,98 | CLASS = UNSEEDED) = 1
√2.3,14 . (1,30 )2exp
−( 0,98−2,1)2
2 (1,3 )2 =0,2118
Sehingga
P ( UNSEEDED ) x P(SPRING | UNSEEDED) x P(TE = 0,71 | CLASS = UNSEEDED) x
P(TW = 1,73 | CLASS = UNSEEDED) x P(NC = 0,72 | CLASS = UNSEEDED) x P(SC =
0,99 | CLASS = UNSEEDED) x P(NWC = 0,98 | CLASS = UNSEEDED) = 0,5 x 0,2632 x
0,5375 x 0,4436 x 0,3245 x 0,5861 x 0, 2118 = 0,0013
P(SEEDED ) = 1938
= 0,5
P(SEASON =SPRING | CLASS = SEEDED) = 5
19 = 0,2632
P(TE = 0,71 | CLASS = SEEDED) = 1
√2.3,14 (0.99)2exp
−(0.71−1.13)2
2 .(0.99)2
=0,3684
P(TW = 1,73 | CLASS = SEEDED) = 1
√2.3,14 .(0,89)2exp
−(1,73−1,75)2
2 (0,89)2
=¿ 0,4483
P(NC = 0,72 | CLASS = SEEDED) = 1
√2.3,14 . (1,02 )2exp
−( 0,72−1,44 )2
2 (1,02 )2 =0 , 3049
P(SC = 0,99 | CLASS = SEEDED) = 1
√2.3,14 .(0,99)2exp
−(0,99−1,04)2
2(0,99)2
=0,4026
P(NWC = 0,98 | CLASS = SEEDED) = 1
√2.3,14 . (1,31 )2exp
−( 0,98−2 )2
2 (1,31 )2 =0,2249
Sehingga
P(SEEDED ) x P(SPRING | SEEDED) x P(TE = 0,71 | CLASS = SEEDED) x P(TW =
1,73 | CLASS = SEEDED) x P(NC = 0,72 | CLASS = SEEDED) x P(SC = 0,99 | CLASS =
SEEDED) x P(NWC = 0,98 | CLASS = SEEDED) = 0,5 x 0,2632 x 0,3684x 0,4483 x
0,3049 x 0,4026 x 0,2249 = 0,0006
Jadi dapat disimpulkan bahwa data ke – 47 termasuk kelas UNSEEDED
48 ? SPRING 1.67 3.46 1.02 1.89 2.47
P (UNSEEDED ) = 1938
=0,5
P(SEASON =SPRING | CLASS = UNSEEDED) = 5
19 = 0,2632
P(TE = 1,67 | CLASS = UNSEEDED) = 1
√2.3,14 (0.66 )2exp
−(1,67−1.03 )2
2 . ( 0.66)2 =0,3778
P(TW = 3,46 | CLASS = UNSEEDED) = 1
√2.3,14 .(0,89)2exp
−(3,46−1,60)2
2 (0,89)2
=¿ 0,0505
P(NC = 1,02 | CLASS = UNSEEDED) = 1
√2.3,14 . ( 0,81 )2exp
−( 1,02−1,46 )2
2 (0,81 )2 =¿ 0,4250
P(SC = 1,89 | CLASS = UNSEEDED) = 1
√2.3,14 . ( 0,67 )2exp
−(1,89−1,11)2
2 (0,67 )2 =0,3024
P(NWC = 2,47 | CLASS = UNSEEDED) = 1
√2.3,14 . (1,30 )2exp
−( 2,47−2,1)2
2 (1,3 )2 =0,2947
Sehingga
P ( UNSEEDED ) x P(SPRING | UNSEEDED) x P(TE = 1,67 | CLASS = UNSEEDED) x
P(TW = 3,46 | CLASS = UNSEEDED) x P(NC = 1,02 | CLASS = UNSEEDED) x P(SC =
1,89 | CLASS = UNSEEDED) x P(NWC = 2,47 | CLASS = UNSEEDED) = 0,5 x 0,2632 x
0,3778x 0,0505 x 0,4250 x 0,3024 x 0,2947 = 0,000095
P(SEEDED ) = 1938
= 0,5
P(SEASON =SPRING | CLASS = SEEDED) = 5
19 = 0,2632
P(TE = 1,67 | CLASS = SEEDED) = 1
√2.3,14 (0.99)2exp
−(1,67−1.13)2
2 .(0.99)2
=0,4678
P(TW = 3,46 | CLASS = SEEDED) = 1
√2.3,14 .(0,89)2exp
−(3,46−1,75)2
2 (0,89)2
=¿ 0,0708
P(NC = 1,02 | CLASS = SEEDED) = 1
√2.3,14 . (1,02 )2exp
−(1,02−1,44 )2
2 (1,02 )2 =0,3594
P(SC = 1,89 | CLASS = SEEDED) = 1
√2.3,14 .(0,99)2exp
−(1,89−1,04)2
2(0,99)2
=0,2788
P(NWC = 2,47 | CLASS = SEEDED) = 1
√2.3,14 . (1,31 )2exp
−(2,47−2 )2
2 (1,31 )2 =0,2856
Sehingga
P(SEEDED ) x P(SPRING | SEEDED) x P(TE = 1,67 | CLASS = SEEDED) x P(TW =
3,46 | CLASS = SEEDED) x P(NC = 1,02 | CLASS = SEEDED) x P(SC = 1,89 | CLASS =
SEEDED) x P(NWC = 2,47 | CLASS = SEEDED) = 0,5 x 0,2632 x 0,4678 x 0,0708 x
0,3594 x 0,2788 x 0,2856 = 0,000125
Jadi dapat disimpulkan bahwa data ke – 48 termasuk kelas SEEDED
49 ? SUMMER 0.73 1.51 0.18 1.42 0.71
P (UNSEEDED ) = 1938
=0,5
P(SEASON =SUMMER | CLASS = UNSEEDED) = 3
19 = 0,1579
P(TE = 0,73 | CLASS = UNSEEDED) = 1
√2.3,14 (0.66 )2exp
−(0,73−1.03 )2
2 . ( 0.66)2 =0,5453
P(TW = 1,51 | CLASS = UNSEEDED) = 1
√2.3,14 .(0,89)2exp
−(1,51−1,60)2
2 (0,89)2
=0,4461
P(NC = 0,18 | CLASS = UNSEEDED) = 1
√2.3,14 . ( 0,81 )2exp
−( 0,18−1,46)2
2 ( 0,81)2 =¿ 0,1413
P(SC = 1,42 | CLASS = UNSEEDED) = 1
√2.3,14 . ( 0,67 )2exp
−(1,42−1,11)2
2 (0,67 )2 =0,5352
P(NWC = 0,71 | CLASS = UNSEEDED) = 1
√2.3,14 . (1,30 )2exp
−( 0,71−2,1)2
2 (1,3 )2 =0,1733
Sehingga
P ( UNSEEDED ) x P(SUMMER | UNSEEDED) x P(TE = 0,73 | CLASS = UNSEEDED)
x P(TW = 1,51 | CLASS = UNSEEDED) x P(NC = 0,18 | CLASS = UNSEEDED) x P(SC
= 1,42 | CLASS = UNSEEDED) x P(NWC = 0,71 | CLASS = UNSEEDED) = 0,5 x
0,1579 x 0,5453 x 0,4461 x 0,1413 x 0,5352 x 0,1733 = 0,00025
P(SEEDED ) = 1938
= 0,5
P(SEASON =SUMMER | CLASS = SEEDED) = 3
19 = 0,1579
P(TE = 0,73 | CLASS = SEEDED) = 1
√2.3,14 (0.99)2exp
−(0,73−1.13)2
2 .(0.99)2
=0,3715
P(TW = 1,51 | CLASS = SEEDED) = 1
√2.3,14 .(0,89)2exp
−(1,51−1,75)2
2 (0,89 )2
=¿ 0,4324
P(NC = 0,18 | CLASS = SEEDED) = 1
√2.3,14 . (1,02 )2exp
−( 0,18−1,44 )2
2 (1,02 )2 =0,1824
P(SC = 1,42 | CLASS = SEEDED) = 1
√2.3,14 .(0,99)2exp
−(1,42−1,04)2
2 (0,99)2
=0,3744
P(NWC = 0,71 | CLASS = SEEDED) = 1
√2.3,14 . (1,31 )2exp
−( 0,71−2 )2
2 (1,31 )2 =0,1876
Sehingga
P(SEEDED ) x P(SUMMER | SEEDED ) x P(TE = 0,73 | CLASS = SEEDED) x P(TW =
1,51 | CLASS = SEEDED) x P(NC = 0,18 | CLASS = SEEDED) x P(SC = 1,42 | CLASS =
SEEDED) x P(NWC = 0,71 | CLASS = SEEDED) = 0,5 x 0,1579 x 0,3715 x 0,4324 x
0,1824 x 0,3744 x 0,1876 = 0,00016
Jadi dapat disimpulkan bahwa data ke – 49 termasuk kelas UNSEEDED
50 ? SUMMER 1.79 3.13 1.83 1.82 3.11
P (UNSEEDED ) = 1938
=0,5
P(SEASON =SUMMER | CLASS = UNSEEDED) = 3
19 = 0,1579
P(TE = 1,79 | CLASS = UNSEEDED) = 1
√2.3,14 (0.66 )2exp
−(1,79−1.03 )2
2 . ( 0.66)2 =0,3116
P(TW = 3,13 | CLASS = UNSEEDED) = 1
√2.3,14 .(0,89)2exp
−(3,13−1,60)2
2 (0,89)2
=1,9653
P(NC = 1,83 | CLASS = UNSEEDED) = 1
√2.3,14 . ( 0,81 )2exp
−( 1,83−1,46 )2
2 (0,81 )2 =¿ 0,4438
P(SC = 1,82 | CLASS = UNSEEDED) = 1
√2.3,14 . ( 0,67 )2exp
−(1,82−1,11)2
2 (0,67 )2 =0,3571
P(NWC = 3,11 | CLASS = UNSEEDED) = 1
√2.3,14 . (1,30 )2exp
−( 3,11−2,1 )2
2 (1,3 )2 =0,2269
Sehingga
P ( UNSEEDED ) x P(SUMMER | UNSEEDED) x P(TE = 1,79 | CLASS = UNSEEDED)
x P(TW = 3,13 | CLASS = UNSEEDED) x P(NC = 1,83 | CLASS = UNSEEDED) x P(SC
= 1,82 | CLASS = UNSEEDED) x P(NWC = 3,11 | CLASS = UNSEEDED) = 0,5 x
0,1579 x 0,3116 x 1,9653 x 0,4438 x 0,3571 x 0,2269 = 0,0017
P(SEEDED ) = 1938
= 0,5
P(SEASON =SUMMER | CLASS = SEEDED) = 3
19 = 0,1579
P(TE = 1,79 | CLASS = SEEDED) = 1
√2.3,14 (0.99)2exp
−(1,79−1.13)2
2 .(0.99)2
=0,5033
P(TW = 3,13 | CLASS = SEEDED) = 1
√2.3,14 .(0,89)2exp
−(3,13−1,75)2
2 (0,89)2
=¿ 0,1348
P(NC = 1,83 | CLASS = SEEDED) = 1
√2.3,14 . (1,02 )2exp
−(1,83−1,44 )2
2 (1,02 )2 =0,3636
P(SC = 1,82 | CLASS = SEEDED) = 1
√2.3,14 .(0,99)2exp
−(1,82−1,04)2
2 (0,99)2
=0,2955
P(NWC = 3,11 | CLASS = SEEDED) = 1
√2.3,14 . (1,31 )2exp
−(3,11−2)2
2 (1,31)2 =0,2127
Sehingga
P(SEEDED ) x P(SUMMER | SEEDED ) x P(TE = 1,79 | CLASS = SEEDED) x P(TE =
1,79 | CLASS = SEEDED) x P(NC = 1,83 | CLASS = SEEDED) x P(SC = 1,82 | CLASS =
SEEDED) x P(NWC = 3,11 | CLASS = SEEDED) = 0,5 x 0,1579 x 0,5033 x 0,1348 x
0,3636 x 0,2955 x 0,2127 = 0,00012
Jadi dapat disimpulkan bahwa data ke – 50 termasuk kelas UNSEEDED
51 ? SUMMER 0.19 1.05 0.08 0.40 0.57
P(SEEDED)= 19/38= 0,5
P(SEASON= SUMMER | CLASS=SEEDED)= 3/19=0.16
P(TE=0,19 | CLASS=SEEDED) = 1√2.3 .14 x 0.99
exp−(0.19−1.03 )2
2x 0.97 = 0,28
P(TW=1,05 | CLASS=SEEDED) = 1√2.3 .14 x 1.16
exp−(1.05−1.75)2
2 x1.34 = 0,28
P(NC=0,08 | CLASS=SEEDED) = 1√2.3 .14 x 0.99
exp−(0.08−1.44 )2
2x 0.97 = 0,15
P(SC=0,40 | CLASS=SEEDED) = 1√2.3 .14 x 0.99
exp−(0.40−1.04 )2
2 x 0.97 = 0,32
P(NWC=0,57 | CLASS=SEEDED) = 1√2.3 .14 x 1.31
exp−(0.57−2)2
2 x1.73 = 0,17
Sehingga
P(SEEDED) x P(SEASON= SUMMER | CLASS=SEEDED) x P(TE=0,19 |
CLASS=SEEDED) x P(TW=1,05 | CLASS=SEEDED) x P(NC=0,08 | CLASS=SEEDED)
x P(SC=0,40 | CLASS=SEEDED) x P(NWC=0,57 | CLASS=SEEDED) = 0,5 x 0,16 x 0,28
x 0,28 x 0,15 x 0,32 x 0,17= 0,00005
P(UNSEEDED)= 19/38 = 0,5
P(SEASON=SUMMER | CLASS=UNSEEDED)= 3/19= 0,16
P(TE=0,19 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.66
exp−(0.19−1.13 )2
2 x 0.44 = 0,23
P(TW=1,05 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.89
exp−(1.05−1.60)2
2x 0.80 = 0,38
P(NC=0,08 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.67
exp−(0.08−1.46)2
2 x 0.45 = 0,07
P(SC=0,40 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.67
exp−(0.40−1.11)2
2x 0.45 = 0,33
P(NWC=0,57 | CLASS=UNSEEDED) = 1√2.3 .14 x 1.30
exp−(0.57−2.10 )2
2 x 1.69 = 0,15
Sehingga
P(UNSEEDED) x P(SEASON= SUMMER | CLASS=UNSEEDED) x P(TE=0,19 |
CLASS=UNSEEDED) x P(TW=1,05 | CLASS=UNSEEDED) x P(NC=0,08 |
CLASS=UNSEEDED) x P(SC=0,40| CLASS=UNSEEDED) x P(NWC=0,57 |
CLASS=UNSEEDED)= 0,5 x 0,16 x 0,23 x 0,38 x 0,07 x 0,33 x 0,15 = 0,00002
Jadi dapat disimpulkan bahwa data ke – 51 termasuk kelas SEEDED
52 ? SUMMER 0.00 0.15 0.00 0.04 0.04
P(SEEDED)= 19/38= 0,5
P(SEASON= SUMMER | CLASS=SEEDED)= 3/19=0,16
P(TE=0,00 | CLASS=SEEDED) = 1√2.3 .14 x 0.99
exp−(0.00−1.03 )2
2x 0.97 = 0,23
P(TW=0,15 | CLASS=SEEDED) = 1√2.3 .14 x 1.16
exp−(0.15−1.75 )2
2 x1.34 = 0,13
P(NC=0,00 | CLASS=SEEDED) = 1√2.3 .14 x 0.99
exp−(0.00−1.44 )2
2x 0.97 = 0,13
P(SC=0,04 | CLASS=SEEDED) = 1√2.3 .14 x 0.99
exp−(0.04−1.04)2
2 x 0.97 = 0,24
P(NWC=0,04 | CLASS=SEEDED) = 1√2.3 .14 x 1.31
exp−(0.04−2)2
2 x1.73 = 0,09
Sehingga
P(SEEDED) x P(SEASON= SUMMER | CLASS=SEEDED) x P(TE=0,00 |
CLASS=SEEDED) x P(TW=0,15 | CLASS=SEEDED) x P(NC=0,00 | CLASS=SEEDED)
x P(SC=0,40 | CLASS=SEEDED) x P(NWC=0,40 | CLASS=SEEDED) = 0,5 x 0,16 x 0,23
x 0,13 x 0,13 x 0,24 x 0.09 = 0,000007
P(UNSEEDED)= 19/38 = 0,5
P(SEASON=SUMMER | CLASS=UNSEEDED)= 3/19= 0,16
P(TE=0,00 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.66
exp−(0.00−1.13)2
2 x 0.44 = 0,14
P(TW=0,15 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.89
exp−(0.15−1.60 )2
2x 0.80 = 1,30
P(NC=0,00 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.67
exp−(0.00−1.46)2
2 x 0.45 = 0,06
P(SC=0,04 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.67
exp−(0.04−1.11)2
2x 0.45 = 0,17
P(NWC=0,04 | CLASS=UNSEEDED) = 1√2.3 .14 x 1.30
exp−(0.04−2.10 )2
2 x 1.69 = 0,09
Sehingga
P(UNSEEDED) x P(SEASON= SUMMER | CLASS=UNSEEDED) x P(TE=0,00 |
CLASS=UNSEEDED) x P(TW=0,15 | CLASS=UNSEEDED) x P(NC=0,00 |
CLASS=UNSEEDED) x P(SC=0,40 | CLASS=UNSEEDED) x P(NWC=0,40 |
CLASS=UNSEEDED) = 0,5 x 0,16 x 0,14 x 1,30 x 0,06 x 0,17 x 0,09= 0,000013
Jadi dapat disimpulkan bahwa data ke – 52 termasuk kelas UNSEEDED
53 ? SUMMER 0.44 0.89 0.83 0.38 0.70
P(SEEDED)= 19/38= 0,5
P(SEASON= SUMMER | CLASS=SEEDED)= 3/19=0,16
P(TE=0,44 | CLASS=SEEDED) = 1√2.3 .14 x 0.99
exp−(0.44−1.03)2
2x 0.97 = 0,31
P(TW=0,89 | CLASS=SEEDED) = 1√2.3 .14 x 1.16
exp−(0.89−1.75 )2
2 x1.34 = 0,26
P(NC=0,83 | CLASS=SEEDED) = 1√2.3 .14 x 0.99
exp−(0.83−1.44 )2
2x 0.97 = 0,33
P(SC=0,38 | CLASS=SEEDED)= 1√2.3 .14 x 0.99
exp−(0.38−1.04 )2
2 x 0.97 = 0,32
P(NWC=0,70 | CLASS=SEEDED)= 1√2.3 .14 x 1.31
exp−(0.70−2)2
2 x1.73 = 0,18
Sehingga
P(SEEDED) x P(SEASON= SUMMER | CLASS=SEEDED) x P(TE=0,44 |
CLASS=SEEDED) x P(TW=0,89 | CLASS=SEEDED) x P(NC=0,83 | CLASS=SEEDED)
P(SC=0,38 | CLASS=SEEDED) x P(NWC=0,70 | CLASS= SEEDED) = 0,5 x 0,16 x 0,31
x 0,26 x 0,33 x 0,32 x 0,18 = 0,000122
P(UNSEEDED)= 19/38 = 0,5
P(SEASON=SUMMER | CLASS=UNSEEDED)= 3/19= 0,16
P(TE=0,44 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.66
exp−(0.44−1.13)2
2x 0.44 = 0,36
P(TW=0,89 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.89
exp−(0.89−1.60 )2
2 x 0.80 = 0,35
P(NC=0,83 | CLASS=UNSEEDED)= 1√2.3 .14 x 0.67
exp−(0.83−1.46)2
2x 0.45 = 0.38
P(SC=0,38 | CLASS=UNSEEDED)= 1√2.3 .14 x 0.67
exp−(0.38−1.11)2
2 x 0.45 = 0.33
P(NWC=0,70 | UNSEEDED) = 1√2.3 .14 x 1.30
exp−(0.70−2.10 )2
2x 1.69 = 0.17
Sehingga
P(UNSEEDED) x P(SEASON= SUMMER | CLASS=UNSEEDED) x P(TE=0,44 |
CLASS=UNSEEDED) x P(TW=0,89 | CLASS=UNSEEDED) x P(NC=0,83 |
CLASS=UNSEEDED) x P(SC=0,38 | CLASS=UNSEEDED) x P(NWC=0,70 |
CLASS=UNSEEDED) = 0,5 x 0,16 x 0,36 x 0,35 x 0,38 x 0,33 x 0,17= 0,000214
Jadi dapat disimpulkan bahwa data ke – 53 termasuk kelas UNSEEDED
54 ? SUMMER 0.31 1.15 0.01 0.44 0.66
P(SEEDED)= 19/38= 0,5
P(SEASON= SUMMER | CLASS=SEEDED)= 3/19=0,16
P(TE=0,31 | CLASS=SEEDED) = 1√2.3 .14 x 0.99
exp−(0.31−1.03)2
2 x 0.97 = 0,31
P(TW=1,15 | CLASS=SEEDED) = 1√2.3 .14 x 1.16
exp−(1.15−1.75)2
2x1.34 = 0,29
P(NC=0,01 | CLASS=SEEDED) = 1√2.3 .14 x 0.99
exp−(0.01−1.44 )2
2 x 0.97 = 0,14
P(SC=0,44 | CLASS=SEEDED)= 1√2.3 .14 x 0.99
exp−(0.44−1.04)2
2x 0.97 = 0,33
P(NWC=0,66 | CLASS=SEEDED)= 1√2.3 .14 x 1.31
exp−(0.66−2)2
2 x1.73 = 0,18
Sehingga
P(SEEDED) x P(SEASON= SUMMER | CLASS=SEEDED) x P(TE=0,31 |
CLASS=SEEDED) x P(TW=1,15 | CLASS=SEEDED) x P(NC=0,01 | CLASS=SEEDED)
x P(SC=0,44 | CLASS=SEEDED) x P(NWC=0,66 | CLASS=SEEDED) = 0,5 x 0,16 x 0,31
x 0,29 x 0,14 x 0,33 x 0,18 = 0,000059
P(UNSEEDED)= 19/38 = 0,5
P(SEASON=SUMMER | CLASS=UNSEEDED)= 3/19= 0,16
P(TE=0,31 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.66
exp−(0.31−1.13 )2
2x 0.44 = 0,28
P(TW=1,15 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.89
exp−(1.15−1.60)2
2 x 0.80 = 0,41
P(NC=0,01 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.67
exp−(0.01−1.46 )2
2x 0.45 = 0,06
P(SC=0,44 | CLASS=UNSEEDED) = 1√2.3 .14 x 0.67
exp−(0.44−1.11)2
2 x 0.45 = 0,36
P(NWC=0,66 | CLASS=UNSEEDED)= 1√2.3 .14 x 1.30
exp−(0.66−2.10 )2
2x 1.69 = 0,17
Sehingga
P(UNSEEDED) x P(SEASON= SUMMER | CLASS=UNSEEDED) x P(TE=0,31|
CLASS=UNSEEDED) x P(TW=1,15 | CLASS=UNSEEDED) x P(NC=0,01|
CLASS=UNSEEDED) x P(SC=0,44 | CLASS=UNSEEDED) x P(NWC=0,66 |
CLASS=UNSEEDED) = 0,5 x 0,16 x 0,28 x 0,41 x 0,06 x 0,36 x 0,17= 0,000033
Jadi dapat disimpulkan bahwa data ke – 54 termasuk kelas SEEDED
HASIL KLASIFIKASI
PERIOD SEEDED SEASON TE TW NC SC NWC39 U SPRING 0.81 0.89 1.33 0.43 2.18
40 U SPRING 0.39 1.22 0.25 0.46 0.8941 U SUMMER 0.86 1.24 0.69 0.49 0.6942 U SUMMER 2.16 2.29 2.12 0.95 1.8243 U SPRING 1.7 2.18 1.45 1.47 2.244 U SPRING 1.22 2 2.13 1.13 2.3345 S SPRING 0.07 0.22 0.02 0.08 0.2446 U SPRING 0.49 1.07 0.36 0.87 0.5747 U SPRING 0.71 1.73 0.72 0.99 0.9848 S SPRING 1.67 3.46 1.02 1.89 2.4749 U SUMMER 0.73 1.51 0.18 1.42 0.7150 U SUMMER 1.79 3.13 1.83 1.82 3.1151 S SUMMER 0.19 1.05 0.08 0.40 0.5752 U SUMMER 0.00 0.15 0.00 0.04 0.0453 U SUMMER 0.44 0.89 0.83 0.38 0.7054 S SUMMER 0.31 1.15 0.01 0.44 0.66
DATA SESUNGGUHNYA
PERIOD SEEDED SEASON TE TW NC SC NWC 39 U SPRING 0.81 0.89 1.33 0.43 2.18 40 S SPRING 0.39 1.22 0.25 0.46 0.89 41 S SUMMER 0.86 1.24 0.69 0.49 0.69 42 U SUMMER 2.16 2.29 2.12 0.95 1.82 43 U SPRING 1.70 2.18 1.45 1.47 2.20 44 S SPRING 1.22 2.00 2.13 1.13 2.33 45 S SPRING 0.07 0.22 0.02 0.08 0.24 46 U SPRING 0.49 1.07 0.36 0.87 0.57 47 U SPRING 0.71 1.73 0.72 0.99 0.98 48 S SPRING 1.67 3.46 1.02 1.89 2.47 49 U SUMMER 0.73 1.51 0.18 1.42 0.71 50 S SUMMER 1.79 3.13 1.83 1.82 3.11 51 U SUMMER 0.19 1.05 0.08 0.40 0.57 52 S SUMMER 0.00 0.15 0.00 0.04 0.04 53 S SUMMER 0.44 0.89 0.83 0.38 0.70 54 U SUMMER 0.31 1.15 0.01 0.44 0.66
EVALUASI
Akurasi data=∑ hasil benar
∑ data seluruhx100 %
Akurasi data= 816
x100 %=50 %
ANALISIS
1. Hasil dari metode bayes bergantung pada prior probability yang ada. Teorema Bayes menyediakan cara untuk menghitung probabilitas posterior dari masing-masing
hipotesis yang berupa data training, dimana kemudian didapatkan probabilitas untuk setiap hipotesis dan output yang paling mungkin.
2. Akurasi data yang diperoleh dari pengujian dengan metode bayes ini sebesar 50 %.
BAB III
PENUTUP
3.1 Kesimpulan
1. Terdapat berbagai macam metode untuk pengklasifikasian data, salah satunya yaitu metode K-Nearest Neighbour (KNN) dan Teorema Bayes.
2. Pada makalah ini, digunakan 54 dataset yang terdiri dari 38 data training dan 16 data testing. Pada pengujian klasifikasi menggunakan KNN, didapatkan hasil akurasi 62,5% untuk k=1 , 56,25% untuk k=3, 62,5% untuk k=5, 56,25% untuk k=9, 56,25% untuk k=11, 56,25% untuk k=13. Sedangkan untuk pengklasifikasian menggunakan Teorema Bayes, hasil akurasinya adalah 50%.
3. Hasil tersebut menunjukkan bahwa pengklasifikasian data menggunakan metode KNN lebih akurat daripada menggunakan Teorema Bayes.
DAFTAR PUSTAKA
http://people.revoledu.com/kardi/tutorial/KNN/HowTo_KNN.htmlhttp://people.revoledu.com/kardi/tutorial/Similarity/NominalVariables.htmlhttp://en.wikipedia.org/wiki/K-nearest_neighbor_algorithmhttp://en.wikipedia.org/wiki/Naive_Bayes_classifierhttp://kodokhejo.files.wordpress.com/2009/06/keoptimalan-naive-bayes.pdf
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