PENDAHULUAN
TINJAUAN PUSTAKA
PROPOSAL PENELITIAN
APLIKASI DA DAT TA MINING MIN ING MARKET BASKET ANALYSIS ANAL YSIS UNTUK MENEMUKAN POLA PEMBELIAN DI LULU MART
METODE PENELITIAN
ILUSTRASI
KESIMPULAN
OLEH: NADYA RAHMAWATI NIM. 1207015012
Latar Belakan g Manfaat Peneliti an
Batasan Masalah
PENDAHULUAN
Tujuan Peneliti an
Rumusa n Masalah
Batasan Masaa! Pa"a #$n$%t%an %n%& #$'(a!asan "%(atas% #a"a: 1. Data )an* "%*+na,an a"aa! "ata t-ansa,s% #$n+aan "% L++ Ma-t #a"a (+an D$s$'($- 2015. 2. Ana%sa "ata )an* "%a,+,an "$n*an '$n**+na,an '$t/"$ 'a-,$t (as,$t ana)s%s.
R+'+san Masaa! R+'+san 'asaa! #a"a #$n$%t%an %n% a"aa!: 1. Ba*a%'ana,a! ana%s%s "ata t-ansa,s% #$n+aan "% L++ Ma-t #a"a D$s$'($2015 "$n*an '$n**+na,an algoritma apriori 2. Ba*a%'ana !+,+' as/s%as% )an* "%!as%,an "a-% "ata #$n+aan "% L++ Ma-t #a"a D$s$'($- 2015 "$n*an '$n**+na,an algoritma apriori
T++an P$n$%t%an T++an #a"a #$n$%t%an %n% a"aa!: 1. M$n*$ta!+% !as% ana%s%s "ata t-ansa,s% #$n+aan "% L++ Ma-t #a"a D$s$'($- 2015. 2. M$n*$ta!+% !+,+' as/s%as% )an* "%!as%,an "a-% "ata #$n+aan "% L++ Ma-t #a"a D$s$'($- 2015 "$n*an '$n**+na,an algoritma apriori.
Manaat P$n$%t%an Manaat #$n$%t%an %n% a"aa!: 1. M$'($-%,an %n/-'as% ,$#a"a saa)an '$n*$na% !as% ana%s%s "ata t-ansa,s% #$n+aan "% L++ Ma-t #a"a D$s$'($- 2015. 2. M$'($-%,an %n/-'as% ,$#a"a saa)an '$n*$na% !+,+' as/s%as% )an* "%!as%,an "a-% "ata #$n+aan "% L++ Ma-t #a"a D$s$'($- 2015. 3. M$'($-%,an %n/-'as% ,$#a"a #a-a 'a!as%sa ,!+s+sn)a stat%st%,a t$ntan* #$n$-a#an data mining "$n*an '$t/"$ market basket analysis. 4. S$(a*a% (a!an 'as+,an +nt+, '$n"+,+n* #$n*a'(%an ,$#+t+san st-at$*% #$'asa-an
TINJAUAN PUSTAKA Data Mining
Market Basket Analysis
Associatio n Rule
Algoritma Apriori
Lulu Mart
Data M%n%n*
P$,$-aan Daa' Data P/s%s% Daa' B$-(a*a% P$n*$-t%an Ta!a#an Mining D%s%#%n I'+
Ta!a#an Data Mining
P/s%s% Daa' B$-(a*a% D%s%#%n I'+
Stat%st%, A-t%6%a %nt$%*$n6$& Patt$-n -$6/*n%t%/n& Ma6!%n$ $a-n%n*
P$,$-aan Daa' Data Mining
Market Basket Analysis
Assoiation R!le •
•
•
K+s-%n% 20089 '$n)ata,an assoiation r!le mining ata+ ana%s%s as/s%as% a"aa! t$,n%, data mining +nt+, '$n$'+,an at+-an as/s%as% anta-a s+at+ ,/'(%nas% item. /nt/! at+-an as/s%as% "a-% ana%s%s #$'($%an "% s+at+ #asa- saa)an a"aa! "a#at "%,$ta!+%n)a ($-a#a ($sa- ,$'+n*,%nan s$/-an* #$an**an '$'($% -/t% ($-sa'aan "$n*an s+s+. Ana%s%s as/s%as% '$na"% t$-,$na ,a-$na a#%,as%n)a +nt+, '$n*ana%sa %s% ,$-anan* ($ana "% #asa- saa)an. Ana%s%s as/s%as% +*a s$-%n* "%s$(+t "$n*an %st%a! market basket analysis.
Algoritma Apriori Algoritma apriori a"aa! a*/-%t'a )an* "%,$na,an /$! R. A*-aa "an R. S-%,ant #a"a ta!+n 1884. Algoritma apriori ($-t++an +nt+, '$n$'+,an "re#!ent itemsets )an* "%aan,an #a"a s$,+'#+an "ata. Pa"a %t$-as% ,$;k a,an "%t$'+,an s$'+a itemset )an* '$'%%,% k item& )an* "%s$(+t k ;itemset . S%at +ta'a "a-% Algoritma apriori a"aa! s$'+a s+(s$t "a-% s+at+ "re#!ent itemsets a"aa! +*a '$-+#a,an an**/ta "re#!ent itemsets <%-*%aan "an M+,!as!& 20139. M$t/"$ "asa- ana%s%s as/s%as% '$n+-+t K+s-%n% 20089 t$-(a*% '$na"% "+a ta!a#: 1. Ana%s%s #/a -$,+$ns% t%n**% S$'$nta-a %t+& n%a% s+##/-t "a-% 2 %t$' "%#$-/$! "a-% -+'+s 2 ($-%,+t:
2. P$'($nt+,an at+-an as/s%as%
L++ Ma-t L++ Ma-t '$-+#a,an saa! sat+ "a-% s$,%an (an)a, saa)an )an* a"a "% K/ta Sa'a-%n"a. D% saa)an %n% ,%ta "a#at '$'($% ,$(+t+!an #an*an& #a,a%an& #$-a(/tan "a#+-& a,s$s/-%s "an a%n s$(a*a%n)a. Saa)an %n% t$-$ta, "% J. M. Ya'%n SH. N/. 3> Sa'a-%n"a& Ka%'antan T%'+-. L$ta, )an* san*at st-at$*%s ($-a"a "% "$,at a-$a ,a'#+s %n%& '$na"%,an L++ Ma-t s$(a*a% saa! sat+ saa)an #%%!an 'a!as%sa "aa' '$'$n+!% ,$(+t+!an s$!a-%;!a-%.
METODE PENELITIAN Wa,t+ "an T$'#at
T$,n%, P$n*+'#+ an Data
T$,n%, Ana%s%s Data
K$-an*,a P$n$%t%an Ran6an*an
P/#+as% "an Sa'#$
T$,n%, Sampli ng
K$-an*,a P$n$%t%an M+a%
P$-+'+san Masaa!
Ana%s%s Stat%st%,a D$s,-%#t%
M$n*!%t+n* n%a% s!pport "$n*an '$t/"$ algoritma apriori
M$n*inp!t synta$ ar!les "% R
M$a,+,an oding #a"a "ata t-ansa,s%
M$n*!%t+n* n%a% on%dene "$n*an '$t/"$ algoritma apriori
K$s%'#+an
S$$sa%
ILUSTRASI Data Transaksi Penjualan Sayur S$(a*a% %+st-as% "aa' #$n$%t%an %n%& "ata )an* "%*+na,an a"aa! "ata t-ansa,s% #$n+aan sa)+- s$(a*a% ($-%,+t: Tabel 4.1 Data Transaksi Penjualan Sayur Transaksi
Item yang dibeli
1
B-/66/%& ?-$$n P$##$-s& /-n
2
As#a-a*+s& S@+as!& /-n
3
/-n& T/'at/$s& B$ans& S@+as!
?-$$n P$##$-s& /-n& T/'at/$s& B$ans
!
B$ans& As#a-a*+s& B-/66/%
"
S@+as!& As#a-a*+s& B$ans& T/'at/$s
#
T/'at/$s& /-n
$
B-/66/%& T/'at/$s& ?-$$n P$##$-s
%
S@+as!& As#a-a*+s& B$ans
1&
B$ans& /-n
11
?-$$n P$##$-s& B-/66/%& B$ans& S@+as!
12
As#a-a*+s& B$ans& S@+as!
13
S@+as!& /-n& As#a-a*+s& B$ans
1
/-n& ?-$$n P$##$-s& T/'at/$s& B$ans& B-/66/%
ILUSTRASI Statistika Deskri'tif Stat%st%,a "$s,-%#t% "ata #$n+aan sa)+- "%*+na,an +nt+, '$%!at *a'(a-an +'+' "ata #$n+aan sa)+-. Has% #$-!%t+n*an stat%st%,a "$s,-%#t% "ata #$n+aan sa)+- "a#at "%%!at #a"a Ta($ 4.2 "an ?a'(a- 4.1. Ta($ 4.2 Stat%st%,a D$s,-%#t% ?a'(a- 4.1 Item
n
Ma(
Min
Rata )rata
12
Standar De*iasi
10 >
+s'aragus Beans
10
Br,--,li
5
.,rn
>
/reen Pe''ers
5
S0uash
7
T,mat,es
re0uensi Penjualan
10
5
&71 42>5 77
4 2 0
1&588
item
ILUSTRASI +'likasi Algoritma Apriori 'ada Data Penjualan Sayur 1. M$'%sa!,an 'as%n*;'as%n* item )an* "%($% "an '$a,+,an oding #a"a t%a# item ,de Item yang Ta($ 4.3 Item )an* "%($% dibeli +s'aragus Beans Br,--,li .,rn /reen Pe''ers S0uash T,mat,es
S1 S2 S3 S4 S5 S S7
2. M$a,+,an /a" #a6,a*$ ar!les. 3. In#+t "ata t-ansa,s% item )an* t$a! "%oding "an synta$ "aa' #-/*-a' R La'#%-an 19 t$-$(%! "a!++& s$t$a! %t+ a,an '+n6+ !as% s$(a*a% ($-%,+t: t-ansa6t%/ns as %t$'Mat-% %n s#a-s$ /-'at %t! 14 -/s $$'$ntsC%t$'s$tsCt-ansa6t%/ns9 an" 7 6/+'ns %t$'s9 an" a "$ns%t) / 0.478581> '/st -$@+$nt %t$'s: S2 S4 S S1
S7 Ot!$-9
ILUSTRASI 4. M$n*!%t+n* -$,+$ns% a(s/+t s$t%a# item. K$'+"%an a,an '+n6+ /+t#+t ($-%,+t: S1 S2 S3 S4 S5 S S7 10 5 > 5 7 5. S$t$a! %t+& '$'(+at #-/s$s algoritma apriori& 'a,a a,an '+n6+ /+t#+t s$#$-t%:
KESIMPULAN Has% ana%s%s "ata t-ansa,s% #$n+aan sa)+-
H+,+' as/s%as%
