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Knowledge Discovery

in Databases (IS704)

dan Data Mining

(CS704)

Kuliah #5:

Classification (Bagian 1)

GunawanJurusan Teknik Informatika

ekolah Tinggi Teknik ura!a"a

e$isi %& 'gustus %15

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November 23, 2015 Gunawan, Teknik Informatika STTS 2

Classification (1

Sebelum bicara terlalu !au", a#ala" baik !ika kitamencoba mem$er"atikan bebera$a ca$ture# sli#es %an&menarik untuk mema"ami "akikat #asar 'ac"ine

earnin& Sumber)

www*cs*ucr*e#u+eamonn+205+'ac"ineearnin&*$$t *

- ..T"e /i&eon /roblemT"e /i&eon /roblem

an&an lu$akan ba"wa ata 'inin& a#ala" irisan #arisala" satu sub#isi$lin rtificial Intelli&ence

- 'ac"ine earnin&'ac"ine earnin&*
http://www.cs.ucr.edu/~eamonn/205/MachineLearning.ppthttp://www.cs.ucr.edu/~eamonn/205/MachineLearning.ppt

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Classification (2

Termasuk #alam ata 'inin& Task)Predictive Modeling*

iberikan "im$unan examples (instances)%an& tela"#ipreklasifikasi(#ilabeli sebelumn%a, 4$1,t1, 4$2,t2, *****

4$n,tn, konstruksila" sebua" mo#el %an& #a$atmen%arikan $en&eta"uan #alam e6am$les tersebut #an#a$at #i&unakan untuk ke$erluan $re#iksi, %aitumem$re#iksi class+tar&et #ari instance %an& belum

#ilabeli*- $ia#ala" vektor input, se#an&kan tia#ala" target (class) terkait*- 76am$les %an& #i&unakan untuk membentuk mo#el #isebut

seba&ai training set*

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Classification (3

9kuran sukses klasifikasi biasan%a #iukur #en&anmen&u!i #ata baru (fres" #ata %an& ti#ak#i&unakan seba&ai trainin& set, teta$i labeln%a!u&a tela" #iketa"ui sebelumn%a*

:emu#ian #i"itun& $ro$orsi !umla" #ata+instance%an& #a$at #iklasifikasi secara benar (akurasin%a*

ikenal term;term se$erti :%atau *5:%5atau*:&%an& menun!ukkan $erban#in&an$ersentase (trainin& set ) testin& set*

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Classification (8

efinisi %an& lebi" mem$er!elas $eranclassification #alam minin& #atabase)- iberikan sebua" #atabase / < 4$1, $2, *****, $n #ari

se!umla" tu$les (atau items + recor#s + e6am$les +

instances #an "im$unan class T < 4t1, t2, *****, tm,classification $roblem a#ala" men#efinisikan suatu$emetaan f ) /T %an& men&ara"kan $ike #alamsalahsatuclass t!*

=alau$un ter#a$at mo#el lainn%a (mis* Na>ve

?a%es, mo#el %an& men#efinisikan $emetaantersebut biasan%a beru$a varian #ari)- Classification rules- Classification tree

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November 23, 2015 Gunawan, Teknik Informatika STTS @

Conto" Classification

iberikan e6am$les berikut)

?an&unla" sebua" mo#el %an& #a$at mem$re#iksi class #ari instanceoutlook=sunny, temperature=hot, humidity=low,#an windy=true*

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November 23, 2015 Gunawan, Teknik Informatika STTS A

'asala" $a#a enis tribut %an&

iklasifikasi Ter#a$at 2 (#ua ti$e utama atribut)

- Numerik - nilai;nilain%a a#ala" an&ka*

- Nominal - nilai;nilain%a termasuk #alam bebera$a kemun&kinan %an&

!umla"n%a terbatas #an #is$esifikasi sebelumn%a*

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November 23, 2015 Gunawan, Teknik Informatika STTS B

Termasuk $a#a Tar&et tributn%a

In$ut ttributes #an Tar&et trribute, semua numerik)Cycle

time (ns)Main memory

(Kb)Cache(Kb)

Channels Performance

MYCT MMIN MMAX CACH CHMIN CHMAX PRP

1 1! !" "### !" 1" 1$ 1%$

% $### ## & $ & "%'

#

'

1!

'

###

'

$###

'

#

'

'

1

'

!

#$ $# !1 $### & # # "

#% $# 1### ### # # # !

*ol+si ,Ti-a. *e-erhana/,Ti-a. *e-erhana/0inear Reression2

PRP = -55.9 + 0.0489 MYCT + 0.0153 MMIN + 0.0056 MMAX+ 0.6410 CACH - 0.2700 CHMIN + 1480 CHMAX

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November 23, 2015 Gunawan, Teknik Informatika STTS

1 i$erkenalkan ole" Dobert C* Eolte (1B3*(F

ikenal !u&a #en&an +1, -olte.*

1D sen#iri a#ala" ke$en#ekan #ari .1;ruleatau .Inferin& Du#imentar% Dule (

Hut$ut al&oritma 1D a#ala" one level decision tree%an& #a$at

#isa!ikan !u&a melalui sebua" classification rule sets* 1D ti#ak menekankan akurasi sem$urna atau 100 benar $a#a

rule sets %an& #i"asilkan*

1D ti#ak $erna" #i$ertimban&kan seba&ai sala" satu $en#ekatanformal #alamMachine Learning atau Data Mining*

Namun teta$ "arus #iin&at) Mengapa harus dipusingkan dengansebuah decision tree yang kompleks ketika sebuah rule setssederhana dapat melakukannya!

(F Eolte, Dobert C* 13* "ery simple classification rules perform #ell on most commonly used datasets*'ac"ine earnin&, 11)@3;0* Eolte beker!a #i Com$uter Science e$artement, 9niversit% of Httawa*

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'lgoritma /riginal 1


Sumber:

!"e Develo#ment o$ %olte&s ' Classi$ier Craig *evill+Manning ,eo$$rey %olmes

and Ian %- .itten- Ini bu/an #a#er ' yang ditulis %olte a/an teta#i #a#er yang

mengembang/an algoritma %olte-

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'lgoritma 1

FOR EACH at!"#t

FOR EACH $a%#& 'a! at!"#t !(!) "&(t#* &"#a,

#%& &t '&(a( aa

M&(,!t#( &"&a/a &!( &"#a, *&%a

'!,a!%*a( 'a! /aa(a( at!"#t=$a%#& !(! a/at*a( &"#a, *&%a a( /a%!( "a(a*

'!,a!%*a( at!"#t '&(a( $a%#& !(!

Ta"a,*a( &"#a, #%& IF atribut = value

THEN kelas*& 'a%a #%& &t

H!t#(%a, t!(*at *&a%a,a( 'a! #%& &t !(!

P!%!,%a, &"#a, #%& &t '&(a( t!(*at

*&a%a,a( t&*&!%

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0eather ro!lem 2atasetsOutlook Temperature Humidity Windy Play

Sunny Hot High Weak No

Sunny Hot High Strong No

Overcast Hot High Weak Yes

Rain Mild High Weak Yes

Rain Cool Normal Weak Yes

Rain Cool Normal Strong NoOvercast Cool Normal Strong Yes

Sunny Mild High Weak No

Sunny Cool Normal Weak Yes

Rain Mild Normal Weak Yes

Sunny Mild Normal Strong YesOvercast Mild High Strong Yes

Overcast Hot Normal Weak Yes

Rain Mild High Strong No

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1,-olte untuk 0eather ro!lem

!anda 1 2ili"an andom

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I3

i$erkenalkan ole" * Cen#rowska (1BA*(F

Termasuk kate&ori algoritma covering, berbe#a #en&anI3 %an& termasuk #alam kate&ori algoritma divide andcon$uer*

isebut #en&an $en#ekatan coverin&, karena $a#a setia$sta&e #ii#entifikasi rule %an& men&cover se!umla"instances*

Hut$ut al&oritma /DIS' a#ala" se!umla" classificationrules*

/DIS' "an%a men&"asilkan rule;rule %an& sem$urnaatau 100 benar**

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rinsi4 'lgoritma Co$ering

4ace untuk semua instancese6am4les

7ang 8ico$er rules se9auh ini

ules "ang akan 8itam!ahkan kemu8ian

Classi$ication ules untu/ Masala" yang Sama:

I3 51'-6 !%* /elas1b

I3 8'-6 9*D y86- !%* /elas1a

I3 8'-6 9*D y516- !%* /elas1b

;agaimana dengan Decision !reenya?7)- PRISM: an Algorithm for Inducing Modular Rules-

International =ournal o$ Man+Mac"ine Studies @ol- 67 *o- 4 ##- A4>+A70-

November 23, 2015 Gunawan, Teknik Informatika STTS 1@

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November 23, 2015 Gunawan, Teknik Informatika STTS 1A

'lgoritma I3

FOR EACH *&%a

I(!!a%!a! E '&(a( !(ta(& &t

HIE E &(a('#( !(ta(& 'a%a *&%a C

&(t#* &"#a, #%& '&(a( H *( a( &/&'!*! *&%a C

NTI R &/#(a ta* a'a at!"#t a( 'a/at '!/a*a!:) %a*#*a( FOR EACH at!"#t A a( t!'a* t&a#* R)'a( t!a/ (!%a! $)

P&t!"a(*a( #(t#* &(a"a, *('!! A=$ /a'a H 'a! R

P!%!, A 'a( $ #(t#* &a*!a%*a( a*#a! /;t

t!/< /!%!, *('!! '&(a( (!%a! P a( t&"&a:

Ta"a,*a( A=$ *& 'a%a R

Ha/#%a, a !(ta(& a( t&$& %&, R 'a! E

Catatan:

p = positive examples dari suatu kelas

t = total instances

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November 23, 2015 Gunawan, Teknik Informatika STTS 1B

Contact ens 2ataset

Berdasarkan kondisi apa seorang ahli optik akan menyarankan

seseorang yang mengalami gangguan mata: menggunakan softcontact-lense, menggunakan hard contact-lenses, atau justru

tidak disarankan menggunakan contact-lense?

i$erkenalkan !u&a ole" * Cen#rowska*

Catatan terms)

- s$ectacle $rescri$tion < $eruntukan kaca mata %an& #iketa"ui

- m%o$e < rabun #ekat

- "%$ermetro$e < rabun !au"

- asti&matism < $an#an&an kabur, %lack of point focus&'' an

astigmatism refers to an irregular curvature of the cornea'

- $resb%o$ic ( J K85 ta"un < batas usia laLimn%a untuk mam$u meli"at

ob%ek $a#a !arak !au" (85 ta"un

- $re;$resb%o$ic ( M K85 ta"un < sebelum masa $resb%o$ic

umber term *ord+et ,'- http..###'allaboutvision'com.askdoc.astigmatism'htm

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Contact ens 2atasetage spectacle

prescription

stigmatism tear production

rate

reccomended

lenses

young myope no reduced none

young myope no normal so!t

young myope yes reduced noneyoung myope yes normal hard

young hypermetrope no reduced none

young hypermetrope no normal so!t

young hypermetrope yes reduced none

young hypermetrope yes normal hard

pre"pres#yopic myope no reduced none

pre"pres#yopic myope no normal so!t

pre"pres#yopic myope yes reduced none

pre"pres#yopic myope yes normal hard

pre"pres#yopic hypermetrope no reduced none

pre"pres#yopic hypermetrope no normal so!t

pre"pres#yopic hypermetrope yes reduced none

pre"pres#yopic hypermetrope yes normal none

pres#yopic myope no reduced none

pres#yopic myope no normal nonepres#yopic myope yes reduced none

pres#yopic myope yes normal hard

pres#yopic hypermetrope no reduced none

pres#yopic hypermetrope no normal so!t

pres#yopic hypermetrope yes reduced none

pres#yopic hypermetrope yes normal none

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I3 untuk Contact ens (#1)

ibentuk rule %an& men&cover setia$ class) ,a')t)#an ((&*

'isaln%a #imulai #ari ,a'.

IF > THEN &&('&' = ,a'

9ntuk kon#isi $a#a ES %an& masi" koson& ter#a$at $ili"an)

a& = #( 2;8

a& = /&-/&"/! 1;8

a& = /&"/! 1;8

/&ta%& /&!/t!( = /& 3;12

/&ta%& /&!/t!( = ,/&&t/& 1;12

at!at! = ( 0;12

astigmatism = es !"#$

t&a /'#t!( at& = &'#&' 0;12

t&a /'#t!( at& = (a% 4;12

#i$ili" ba&ian %an& terbesar < 8+12, secara acak #i$ili" sala" satu antaranomor A #an nomor $a#a #aftar #i atas, misaln%a nomor A)

IF at!at! = & THEN &&('&' = ,a'

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I3 untuk Contact ens (#%)

Dule I at!at! = & t,&( &&('&' = ,a'

!elas ti#ak akurat* i"at tabel berikut)

Tam4ak !ahwa rule terse!ut han"a mengco$er ; instance "ang

!enar 8ari total 1% instances< ehingga refinement 4erlu 8ilakukan

untuk rule: IF at!at! = & AN > THEN

&&('&' = ,a'

age spectacle

prescription


rate

reccomended

lenses

young myope yes reduced none

young myope yes normal hard

young hypermetrope yes reduced none


pre"pres#yopic myope yes reduced nonepre"pres#yopic myope yes normal hard

pre"pres#yopic hypermetrope yes reduced none

pre"pres#yopic hypermetrope yes normal none

pres#yopic myope yes reduced none


pres#yopic hypermetrope yes reduced none


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I3 untuk Contact ens (#&)

IF at!at! = & AN > THEN &&('&' = ,a'

9ntuk kon#isi $a#a ES %an& masi" koson& ter#a$at A $ili"an)a& = #( 2;4

a& = /&-/&"/! 1;4

a& = /&"/! 1;4

/&ta%& /&!/t!( = /& 3;6

/&ta%& /&!/t!( = ,/&&t/& 1;6

t&a /'#t!( at& = &'#&' 0;6

t&a /'#t!( at& = (a% 4;6

#i$ili" ba&ian %an& terbesar < 8+@, %aitu nomor @)

IF at!at! = & AN t&a /'#t!( at& =(a% THEN &&('&' = ,a'

Sebenarn%a untuk sebua" class (misaln%a ,a', al&oritma #a$at#i$aksa ber"enti (misaln%a #isini $a#a kasus contact lens* Namun,

ba&aimana !ika exact rule "arus #i$erole", tan$a mem$e#ulikansekom$leks a$a$un rulen%a

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I3 untuk Contact ens (#;)

Dule IF at!at! = & AN t&a /'#t!(

at& = (a% THEN &&('&' = ,a' !elas masi"belum akurat* i"at tabel berikut)

Tam4ak !ahwa rule terse!ut han"a mengco$er ; instance "ang

!enar 8ari total = instances< ehingga refinement 4erlu 8ilakukankem!ali untuk rule:

IF at!at! = & AN t&a /'#t!(

at& = (a% AN > THEN &&('&' = ,a'

age spectacle

prescription


rate

reccomended

lenses



pre"pres#yopic myope yes normal hardpre"pres#yopic hypermetrope yes normal none



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I3 untuk Contact ens (#5)

IF at!at! = & AN t&a /'#t!( at& =

(a% AN > THEN &&('&' = ,a'

9ntuk kon#isi $a#a ES %an& masi" koson& ter#a$at 5 $ili"an)

a& = #( 2;2

a& = /&-/&"/! 1;2

a& = /&"/! 1;2

spectacle prescription = mope %"%

/&ta%& /&!/t!( = ,/&&t/& 1;3

Se"in&&a "arus #i$ili" ba&ian %an& terbesar, teta$i %an& mana

2+2 atau 3+3 alam "al ini .sebaiknya dipilih yang mengcover lebihbanyak instance, atau nomor 8, se"in&&a rule se!au" ini a#ala")

IF at!at! = & AN t&a /'#t!( at& =(a% AN /&ta%& /&!/t!( = /& THEN&&('&' = ,a'

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I3 untuk Contact ens (#=)

:arena rule IF at!at! = & AN t&a

/'#t!( at& = (a% AN /&ta%&/&!/t!( = /& THEN &&('&' =

,a' su#a" akurat* i"at tabel berikut)

Tam4ak !ahwa rule terse!ut han"a mengco$er & instance "ang

!enar 8ari total %; instances> 8an !aru & 8ari ; instance untuk

&&('&' = ,a'< elan9utn"a & instance 4a8a ta!el 8iatas 8iha4us 8ari total %; instances> 8an mencari rule lainn"a

8engan !entuk:

IF > THEN &&('&' = ,a'

age spectacle

prescription

astigmatism tear production

rate

reccomended

lenses


pre"pres#yopic myope yes normal hard


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November 23, 2015 Gunawan, Teknik Informatika STTS 2@

I3 untuk Contact ens (#*)

en&an cara %an& sama $a#a sta&e sebelumn%a, berturut;turut

#i$erole") a& = #(, a#ala" %an& terbaik untuk con#ition $ertama

(men&cover 1 #ari A*Mengapa angka 7 ?

at!at! = &, a#ala" %an& terbaik untuk con#ition

ke#ua (#i$ili" 1+3*Apakah pilihan ini karena tips! ?

t&a /'#t!( at& = (a%, a#ala" %an& terbaik

untuk con#ition keti&a (#i$ili" 1+1*

Dule %an& #i$erole" a#ala") IF a& = #( AN

a(t!at! = & AN t&a /'#t!( at& =

(a% THEN &&('&' = ,a' su#a" akurat*

Mengco"er #$#? Atau %$%?

a#i seluru" kasus "ar# tela" tercover, se"in&&a lan&ka";lan&ka"

berikutn%a a#ala" untuk kasus soft #an none*

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November 23, 2015 Gunawan, Teknik Informatika STTS 2A

I3 untuk Contact ens (#)Koleksi ule engka4 4a8a 'khir roses (? ule) I /&ta%& /&!/t!( = /& a(' at!at! = &

a(' t&a /'#t!( at& = (a% t,&( &&('at!( =,a'

I a& = #( a(' at!at! = & a(' t&a /'#t!(at& = (a% t,&( &&('at!( = ,a'

I a& = #( a(' at!at! = ( a(' t&a /'#t!(at& = (a% t,&( &&('at!( = t

I a& = /&-/&"/! a(' at!at! = ( a(' t&a/'#t!( at& = (a% t,&( &&('at!( = t

I /&ta%& /&!/t!( = ,/&&t/& a(' at!at!= ( a(' t&a /'#t!( at& = (a% t,&(&&('at!( = t

I t&a /'#t!( at& = &'#&' t,&( &&('at!( =((&

I a& = /&"/! a(' /&ta%& /&!/t!( = /&a(' at!at! = ( t,&( &&('at!( = ((&

I a& = /&-/&"/! a(' /&ta%& /&!/t!( =,/&&t/& a(' at!at! = & t,&( &&('at!( =((&

I a& = /&"/! a(' /&ta%& /&!/t!( =,/&&t/& a(' at!at! = & t,&( &&('at!( =((&

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November 23, 2015 Gunawan, Teknik Informatika STTS 2B

I3 untuk Contact ens (#?)

akhirnya'''''

'0'@@@'0'@@@

J'AG'A 2IC/B' untukmengatasi Gangguan 3ata

'n8a "ang se!enarn"a*

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