uts spam email
TRANSCRIPT
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UTS
DATA MINING
“Klasifkasi spam email menggunakan Naïve Bayes”
Disusun Oleh
1. Stefhanie Andreaane Adelia Tendean
(130411100002)2. Anissa Rachmawati
(130411100004)3. Fitria Najatul aili
(130411100021)4. !asdar "i#$n$
(1304111000%&)'. Anun Sur#aninsih
(130411100101). Nadia !.*
(130411100102)&. R. +wi Ade ,swah#udi
(1204111000&&)
!"OG"AM STUDI T#KNIK IN$O"MATIKA
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$AKU%TAS T#KNIK
UNI"SITAS T"UNO'O(O MADU"A
TA)UN *+,-.*+,/
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Sample Da0a
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1. +ata #an a-an dice- Subject /Viagra Kategori/Iklan Content /LGBT Attachment /N$
Size Data/2'4'& Jumlah Penerima Pean/30.
2. itun $nditi$nal r$ailitas! 1Spam 2 (#S 2 334 (Suje- / 5iara 6 78S ) / 10944 (Suje- / Tut$rial 6 78S ) / &944 (Suje- / Tuas 6 78S ) / 3944 (Suje- / Tes 6 78S ) / '944 (Suje- / entin 6 78S ) / '944 (Suje- / *uliah 6 78S ) / 4944 (Suje- / *$s$n 6 78S ) / 10944
(*ate$ri / Adult 6 78S ) / 10944
(*ate$ri / 8du-asi 6 78S ) / 11944 (*ate$ri / Finansial 6 78S ) / 3944 (*ate$ri / :ratisan 6 78S ) / 944 (*ate$ri / ,-lan 6 78S ) / &944 (*ate$ri / *esehatan 6 78S ) / &944
($ntent / Ada seminar es$- 6 78S ) / 1944 ($ntent / ;a- mie mewah 6 78S ) / 1944 ($ntent / ;ir intan 6 78S ) / '944 ($ntent / ;itc$in 6 78S ) / 1944 ($ntent / +$-umen
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(Attachment / N= 6 78S )/ 2944
(Address / adultBadult.c$m 6 78S ) / 1944 (Address / ranne#Bren$Cati$ns.c$m 6 78S ) / 11944
(Address / datminB#ah$$.c$m 6 78S ) / 1944 (Address / DtrinajaBmail.c$m 6 78S ) / 1944 (Address / hac-erind$nesiaBhac-er.c$m 6 78S ) / 2944 (Address / ind$nesiaelajarBmail.c$m 6 78S ) / 2944 (Address / ind$nesiaja#aBmail.c$m 6 78S ) / 1944 (Address / -
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! 1Spam 2 NO 2 */4 (Suje- / Tut$rial 6 N= ) / &92 (Suje- / Tuas 6 N= ) / 392 (Suje- / Tes 6 N= ) / '92
(Suje- / entin 6 N= ) / 292 (Suje- / *uliah 6 N= ) / 392 (Suje- / *$s$n 6 N= ) / 92
(*ate$ri / Adult 6 N= ) / 292 (*ate$ri / 8du-asi 6 N= ) / 1192 (*ate$ri / Finansial 6 N= ) / '92 (*ate$ri / ,-lan 6 N= ) / 92 (*ate$ri / *esehatan 6 N= ) / 292
($ntent / Ada seminar es$- 6 N= ) / 192
($ntent / *$s$n 6 N= ) / 292 ($ntent / *uliah liur 6 N= ) / 192 ($ntent / :;T 6 N= ) / 192 ($ntent / !lm 6 N= ) / %92 ($ntent / =at +iet eral 6 N= ) / 192 ($ntent / il *; 6 N= ) / 192 ($ntent / Tut$rial N$de.js 6 N= ) / 192 ($ntent / 5irus -$m
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(Address / utmBtrun$j$#$.ac.id 6 N= ) / 292
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Select E fr$m s
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56mpu0e 56n7i0i6nal !869a9ili0y :68 56n0inue A008i9u0e
- Si;e Da0aSi?e data 6 s
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( )( )
( )
08-1.50541E
exp5157936456.2
125457 5157936456.*2
112567.2772254572
=
==
−
−
π
YesSize P
!1Si;e Da0a2*-3-+=N64
( )( )
( )
0.00491258
exp320457.782
130 78.320457*2
849885.8302
=
==
−
−
π
NoSize P
#s0ima0e p rior Probability P(X|”No”)
P(X|”No”) / (Subject /Viagra6!o) I (Kategori/Iklan6!o) I
(Content /:;T6!o) I ( Attachment /N$6!o) I
(Size Data/2'4'&6!o) I ( Jumlah Penerima
Pean/306!o)
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/ 0 I 92 I 192 I 192 I08-1.54247E
I
0.004%12'
/ 0
#s0ima0e p rior Probability P(X|”Yes”)
P(X|”Yes”)/ (Subject /Viagra6"e) I (Kategori/Iklan6"e) I
(Content /:;T6"e) I ( Attachment /N$6"e) I
(Size Data/2'4'&6"e) I ( Jumlah Penerima
Pean/306"e)
/ 10944 I &944 I 1944 I 2944 I08-1.50541E
I
0.004&1
/ 3.34'8J14
Posterior !869a9ili0y :68 “No” Class P(No/X)
( ) 0070/26* == x X No P α
( ) X P 1=α
Posterior !869a9ili0y :68 “Yes” Class P(“Yes”/X)
( ) α α 14-2.41031E14-3.83458E*)70/44(* == X Yes P
( ) X P 1=α
5lassif?a0i6n
;erdasar-an
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:;T Attachment / N$ Si?e +ata / 2'4'& - dan Humlah
enerima esan/30 meru
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