Decission Tree NIM 43 66 69
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DECISSION TREE Moh. Islah Junaidillah 110411100043 Achmad Hidayat 110411100066 Ach. Mufti 110411100069
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Transcript of Decission Tree NIM 43 66 69
DECISSION TREE
Moh. Islah Junaidillah 110411100043Achmad Hidayat 110411100066Ach. Mufti 110411100069
Penjualan RumahNo Harga Tipe Ukura
nLetak Statu
sTaman Garas
iMinat
1 Mahal Clasc Besar Perkotaan
Baru Ada Ada Tidak
2 Murah Modern
Besar Perumahan
Baru Tidak Ada Menarik
3 Mahal Modern
Besar Perumahan
Baru ada Ada Menarik
4 Murah Clasic
Minimalis
Perkotaan
Bekas Tidak Ada Menarik
5 Mahal Clasic
Minimalis
Perumahan
Bekas Tidak Tidak Tidak
6 Murah Clasic
Minimalis
Perkotaan
Baru Tidak Tidak Menarik
7 Murah Modern
Minimalis
Perumahan
Bekas Tidak Ada Menarik
8 Mahal Clasic
Besar Perkotaan
Bekas Ada Ada Tidak
9 Sedang
Clasic
Minimalis
Perkotaan
Baru Ada Tidak Menarik
10 Mahal modern
Besar Perkotaan
Baru Ada Ada Tidak
Entropy (Total)• Jumlah data = 10• Number of “Menarik” Classes = 6 P(“Yes”)=6/10
• Number of “Tidak” Classes = 4 P(“No”)=4/10
9710.0 1062log*10
61042log*10
4)(
TotalEntropy
Entropy (“Mahal”)• Number of data = 5• Number of “No” classes = 4 ,P(“tidak mahal”) = 4/5
• number of “Yes” classes = 1, P(“menarik mahal”)=1/5
0.7219
)5/4(2log*5/4)5/1(2log*5/1)"("
MahalEntropy
Entropy (“Sedang”)• Number of data = 1• Number of “No” Classes = 0, P(“No Sedang”)=0/1
• Number of “Yes” Classes =, P(“Menarik Sedang”)=1/1
0
)1(2log*1)0(2log*0)"("
SedangEntropy
Entropy (“Murah”)• Number of data = 4• Number of “No” Classes = 0, P(“Tidak Murah”)=0/4
• Number of “Yes” Classes = 4/4, P(“Menari Murah”)=4/4
0
)1(2log*1)0(2log*0)"("
MurahEntropy
Information gain For “Harga”
0.6100
0*1040*10
17219.0*1059710.0
)arg(*arg)()arg,(3
1
aHEntropyTotal
aHTotalEntropyahTotalGaini
Entropy (“Clasic”)• Number of data = 6• Number of “No” Classes = 3, P(“No clasic”)=3/6
• Number of “Yes” Classes = 3, P(“Yes clasic”)=3/6
1
)6/3(2log*6/3)6/3(2log*6/3)"("
ClasicEntropy
Entropy (“Modern”)• Number of data = 4• Number of “No” Classes = 1, P(“No Modern”)=1/4
• Number of “Yes” Classes = 3, P(“Yes Modern”)=3/4
0.8113
)4/3(2log*4/3)4/1(2log*4/1)"("
ModernEntropy
Information Gain for “Tipe”
0.0465
8113.0*1041*10
69710.0
)(*)(),(2
1
TipeEntropyTotalTipeTotalEntropyTipeTotalGain
i
Entropy (“Besar”)• Number of data = 5• Number of “No” Classes = 3, P(“No Besar”)=3/5
• Number of “Yes” Classes = 2, P(“Yes Besar”)=2/5
0.9710
)5/3(2log*5/3)5/2(2log*5/2)"("
BesarEntropy
Entropy (“Minimalis”)• Number of data = 5• Number of “No” Classes = 1, P(“No Minimalis”)=1/5
• Number of “Yes” Classes = 3, P(“Yes Minimalis”)=4/5
0.7219
)5/4(2log*5/4)5/1(2log*5/1)"("
MinimalisEntropy
Information Gain for “Ukuran”
0.1246
7219.0*1059710.0*10
59710.0
)(*)(),(2
1
UkuranEntropyTotalUkuranTotalEntropyUkuranTotalGain
i
Entropy (“Perkotaan”)• Number of data = 6• Number of “No” Classes = 3, P(“No Perkotaan”)=3/6
• Number of “Yes” Classes = 3, P(“Yes Perkotaan”)=3/6
1
)6/3(2log*6/3)6/3(2log*6/3)"("
PerkotaanEntropy
Entropy (Perumahan)• Number of data = 4• Number of “No” Classes = 3, P(“No Perumahan”)=1/4
• Number of “Yes” Classes = 3, P(“Yes Perumahan”)=3/4
0.8113
)4/3(2log*4/3)4/1(2log*4/1)"("
PerumahanEntropy
Information Gain for “Letak”
0.0465
8113.0*1041*10
69710.0
)(*)(),(2
1
LetakEntropyTotalLetakTotalEntropyLetakTotalGain
i
Entropy (“Baru”)• Number of data = 6• Number of “No” Classes = 2, P(“No Baru”)=2/6
• Number of “Yes” Classes = 4, P(“Yes Baru”)=4/6
0.9183
)6/4(2log*6/4)6/2(2log*6/2)"("
BaruEntropy
Entropy (“Bekas”)• Number of data = 4• Number of “No” Classes = 2, P(“No Perumahan”)=2/4
• Number of “Yes” Classes = 3, P(“Yes Perumahan”)=2/4
1
)4/2(2log*4/2)4/2(2log*4/2)"("
BekasEntropy
Information Gain for “Status”
0.0200
9183.0*1061*10
49710.0
)(*)(),(2
1
StatusEntropyTotalstatusTotalEntropyStatusTotalGain
i
Entropy (“Ada”)• Number of data = 5• Number of “No” Classes = 3, P(“No Ada”)=3/5
• Number of “Yes” Classes = 2, P(“Yes Ada”)=2/5
0.9710
)5/3(2log*5/3)5/2(2log*5/2)"("
AdaEntropy
Entropy (Tidak)• Number of data = 5• Number of “No” Classes = 1, P(“No Tidak”)=1/5
• Number of “Yes” Classes = 4, P(“Yes Tidak”)=4/5
0.7219
)5/4(2log*5/4)5/1(2log*5/1)"("
TidakEntropy
Information Gain for “Taman”
0.1246
9710.0*1057219.0*10
59710.0
)(*)(),(2
1
TamanEntropyTotalTamanTotalEntropyTamanTotalGain
i
Entropy (“Ada”)• Number of data = 7• Number of “No” Classes = 3, P(“No Ada”)=3/7
• Number of “Yes” Classes = 4, P(“Yes Ada”)=4/7
0.9852
)7/4(2log*7/4)7/3(2log*7/3)"("
AdaEntropy
Entropy (“Tidak”)• Number of data = 3• Number of “No” Classes = 1, P(“No Tidak”)=1/3
• Number of “Yes” Classes = 3, P(“Yes Perumahan”)=2/3
0.9183
)3/2(2log*3/2)3/1(2log*3/1)"("
TidakEntropy
Information Gain for “Garasi”
0.0059
9183.0*1039852.0*10
79710.0
)(*)(),(2
1
GarasiEntropyTotalGarasiTotalEntropyGarasiTotalGain
i
All of Information Gain• Harga = 0.6100• Letak = 0.0465• Ukuran = 0.1246• Status = 0.0200• Tipe = 0.0465• Taman = 0.1246• Garasi = 0.0059• Maximum Information Gain = 0.6100
Harga• If Harga==“Mahal” then Minat=“Tidak”• If Harga==“Murah” then Minat=“menarik”• If Harga==“Sedang” then Minat=“menarik”
Minat=“tidak”Minat=“menarik”
The First TreeHARGA
MENARIK
MENARIK?
MAHAL MURAHSEDANG
The 1st New Query• SELECT penjualan.[no], penjualan.[harga], penjualan.[letak], penjualan.[ukuran], penjualan.[status], penjualan.[tipe], penjualan.[taman], penjualan.[garasi]FROM Tennis where penjualan.[Harga]=“mahal"
No Harga Tipe Ukuran
Letak Status
Taman Garasi
Minat
1 Mahal Clasc Besar Perkotaan
Baru Ada Ada Tidak
2 Mahal Modern
Besar Perumahan
Baru ada Ada Menarik
3 Mahal Clasic
Minimalis
Perumahan
Bekas Tidak Tidak Tidak
4 Mahal Clasic
Besar Perkotaan
Bekas Ada Ada Tidak
5 Mahal modern
Besar Perkotaan
Baru Ada Ada Tidak
Attributes
Instances
Number of Data
Tidak
menarik
Entropy
Information Gain
5 1 4Harga
Mahal 5 4 1Sedang 0 0Murah 0 0
tipeClassic
3 3 0
Modern 2 1 1ukuran
Besar 4 3 1Minimalis
1 1 0
Letakperumahan
2 1 1
perkotaan
3 3 0
statusbaru 3 2 1bekas 2 1 1
tamanada 4 3 1tidak 1 1 0
garasiada 4 3 1tidak 1 1 0
Entropy (Total)
7219.0 54log2*5
4+51log2*5
1-ahal""arg
MaHEntropy
Entropy (“Classic”)• Number of Data = 3• Number of “no Clasic” = 3 • Number of “yes clasic” = 0
0 302log*3
0332log*3
3)(
clasicEntropy
Entropy (Modern)• Number of Data = 2• Number of “no modern” = 1 • Number of “yes modern” = 1
1 212log*2
1212log*2
1)(
ModernEntropy
Information Gain
3219.0
1*520*5
37219.0,""arg
TipeMahalaHGain
Entropy(“Besar”)• Number of Data = 4• Number of “no besar” = 3 • Number of “yes besar” = 1
0.8113 432log*4
3412log*4
1)(
BesarEntropy
Entropy (Minimalis)• Number of Data = 1• Number of “no minimalis” = 1 • Number of “yes minimalis” = 0
0 102log*1
0112log*1
1)(
MinimalisEntropy
Information Gain
0729.0
8113.0*540*5
17219.0,""arg
UkuranMahalaHGain
Entropy (“Perumahan”)• Number of Data = 2• Number of “no perumahan” = 1 • Number of “yes perumahan” = 1
1 212log*2
1212log*2
1)(
PerumahanEntropy
Entropy (“Perkotaan”)• Number of Data = 3• Number of “no perkotaan” = 3 • Number of “yes perkotaan” = 0
0 302log*3
0332log*3
3)(
PerkotaanEntropy
Information Gain
3219.0
1*520*5
37219.0,""arg
LetakMahalaHGain
Entropy (Baru)• Number of Data = 3• Number of “no baru” = 2• Number of “yes baru” = 1
0.9183 322log*3
2312log*3
1)(
BaruEntropy
Entropy (“Bekas”)• Number of Data = 2• Number of “no bekas” = 1 • Number of “yes bekas” = 1
1 212log*2
1212log*2
1)(
BekasEntropy
Information Gain
2291.0
1*529183.0*5
37219.0,""arg
StatusMahalaHGain
Entropy (“Ada”)• Number of Data = 4• Number of “no ada” = 3 • Number of “yes ada” = 1
0.8113 432log*4
3412log*4
1)(
AdaEntropy
Entropy (“Tidak”)• Number of Data = 1• Number of “no tidak” = 1 • Number of “yes tidak” = 0
0 102log*1
0112log*1
1)(
TidakEntropy
Information Gain
0729.0
8113.0*540*5
17219.0,""arg
TamanMahalaHGain
Entropy (“Ada”)• Number of Data = 4• Number of “no garasi” = 3 • Number of “yes garasi” = 1
0.8113 432log*4
3412log*4
1)(
AdaEntropy
Entropy (“Tidak”)• Number of Data = 1• Number of “no garasi” = 1 • Number of “yes garasi” = 0
0 102log*1
0112log*1
1)(
tidakEntropy
Information Gain
3219.0
8113.0*540*5
17219.0,""arg
garasiMahalaHGain
All of Information Gain for Harga=“Mahal”• Tipe = 0.3219• Ukuran = 0.0729• Letak = 0.3219• Status = 0.2291• Taman = 0.0729• Garasi = 0.3219
Tipe attribute• If Tipe==“Classic” then Minat=“Menarik”• If Tipe ==“Modern”then Minat =“tidak”
Minat=“Menarik”Minat=“tidak”
The Second Tree
MENARIK
MAHAL MURAHSEDANG
HARGA
MENARIKTIPE
CLASSICMODERN
MENARIK?
The 2nd New Query• SELECT penjualan.[no], penjualan.[harga], penjualan.[letak], penjualan.[ukuran], penjualan.[status], penjualan.[tipe], penjualan.[taman], penjualan.[garasi]FROM penjualan where penjualan.[tipe]=“modern“ and penjualan.[minat]=“menarik”
The 2nd Query ResultsNo Harga Tipe Ukura
nLetak Statu
sTaman Garas
iMinat
1 Mahal Clasc Besar Perkotaan
Baru Ada Ada Tidak
2 Mahal Clasic
Minimalis
Perumahan
Bekas Tidak Tidak Tidak
2 Mahal Clasic
Besar Perkotaan
Bekas Ada Ada Tidak
4 Mahal modern
Besar Perkotaan
Baru Ada Ada Tidak
Data QueryAttributes
Instances
Number of Data
Tidak
menarik
Entropy
Information Gain
4 4 0tipe
Classic
3 3 0
Modern 1 1 0ukuran
Besar 3 3 0Minimalis
1 1 0
Letakperumahan
1 1 0
perkotaan
3 3 0
statusbaru 2 2 0bekas 2 2 0
tamanada 3 2 0tidak 1 1 0
garasiada 3 2 0tidak 1 1 0
All information gainBesar==“minat”Menarik=0Tidak=3Minimalis==“minat”Menarik=0Tidak=1
MENARIK
MAHAL MURAHSEDANG
HARGA
MENARIKTIPE
CLASSICMODERN
MENARIKUkuran
TIDAKTIDAK
BESAR MINIMALIS
The 3rd TREE
Final Conditon• If Harga==“Murah” then Minat=“Menarik”• If Harga ==“Sedang”then Minat =“Menarik”• If Harga==“Mahal” and Tipe ==“Modern” then Minat=“Menarik”
• If Harga==“Mahal” and Tipe ==“Classic” and Ukuran==“Minimalis” then Minat=“Tidak”
• If Harga==“Mahal” and Tipe ==“Classic” and Ukuran==“Besar” then Minat=“Tidak”
Minat=“Menarik”Minat=“tidak”
Ket. Pembagian Tugas• Moh. Islah Junaidillah • select data Query• keputusan root dan leaf
• Achmad Hidayat• data sampling• Entropy
• Ach. Mufti• information gain • progress and final decission tree
