Attribution- ShareAlike 4.0 International (CC BY SA
4.0)
Vol. 03, No. 11, November 2023
e -
ISSN
:
2807-8691 |
p-
ISSN
:
2807-839X
!
IJSSR!Page%2842
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!Keywords!
!
ABSTRACT!
Social'Assistance,'Crisp'Dm,'SVM,'
Classification.'
%
In# Jakarta ,# social# assistance# is# available# for# specific# categories# of#
older# adults# but# is# given# by# Conditional# Cash# Transfer# (CCT).#
Technical#assistance#is#based# on#a#database# of#beneficiaries# and#its#
mining# due# to# the# regulation# registry.# The# sub-national# social#
government# maintains# a# Mining# database.# The# author# uses# a#
database#in#one#of#the#districts#as#a#case#study.#The#author#uses#Crisp#
DM#to#understand#this#social# welfare#problem#because#it#has#its#logic#
of# understanding,# the# logic# and# the# details# of# pre-processing# the#
data.# Then,# to# decide,# used# a# Support# Vector# Machine# (SVM)# as# a#
model#and#te sted#it#with#cross-validation.#To# obtain#the#classification#
accuracy# formed# from# the# data# processing# results.# This# research#
aims#to#see#the#predictive#results#of#the#distribution#of#KLJ#program #
assistance.#Then,#answer#the#form#of#modeling#to#create#a#decision#
system#for# the# distribution# of# KLJ.#For#this# reason,# SVM#is# used# to#
make#decisions#related#to#social#assistance#issues.##In#the#SVM,# which#
consists#of#two#classes,#it#can#be#interpreted#that#one#class#contains#
“stop”#while# the#other#“recei ve”.#Ho wever,#there#are#offers#bias#in#the#
data#processing,#w ith#the# "if"#condition.#The#data#processing# reaches#
100%#accuracy#and#gets#SVM#kernel#weight.#
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REFERENCES)
J1(&:(82! +=! ODEPMQ=! U()(! '*#*#19! U()(! '*#*#1! @%#@.B)-! (#/! ).@;#*I<.-=! "#! Proceedings+ -+ 2013+
International+ Conference+ on+ Machine+ Intelligence+ Research+ and+ Advancement,+ ICMIRA+ 2013=!
;))B-9ff/%*=%&1fPE=PPE^f"G0"dJ=DEPY=MN!
J8'(-B%%&2! 0=! S=2! +($(.*2! J=2! +(8(A.1;.;2! J=2! 0*#(.*R3*/1%8*2! 3=2! l! +($(.*2! J=! J=! ODEDPQ=! Support+ Vector+
Machines+in+Big+Data+Classiication:+A+Systematic+Literature+Review+Support+Vector+Machines+in+Big+
Data+Classification:+1+A+Sy stematic+Literature+Review+2+3=!
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International+ Journal+ of+ Business+ Intelligence+ Research2! 5ODQ2! Y^n[P=!
;))B-9ff/%*=%&1fPE=MEP]f*A?*&=DEPMEMEPEY!
3(8(-<?&('(#*(#2! F=! ODEPDQ=! +%@*(8! -.@<&*),! (#/! -%@*(8! :.8$(&.! /()(! '*#*#19! J#! %6.&6*.:=! IEEE+
Transactions+on+Systems,+Man+and+Cybernetics+Part+C:+Applications+and+Reviews2!42O[Q2!]Y_n]NY=!
;))B-9ff/%*=%&1fPE=PPE^fF+0GG=DEPP=DP__DN]!
3(&@(2!T=!ODEP_Q=!Integrating+data+and+information+management+for+social+protection:!O"--<.!L@)%?.&Q=!
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Man+ and+ Cybernetics+ Part+ C:+ Applications+ and+ Reviews2! 42O[Q2! ]Y_n]NY=!
;))B-9ff/%*=%&1fPE=PPE^fF+0GG=DEPP=DP__DN]!
U(/(BRG(#)(82! 7=! a=2! c*-@;.&2! J=! 0=2! l! d('%-2! G=! e=! ODEDPQ=! F(&1.)*#1! 6.&-<-!-%@*(8! B&%).@)*%#! *#! @(-;!
)&(#-$.&-!*#!);.!K;*8*BB*#.-9!d.(--.--*#1!(!@.8.?&()./!@(-.!%$!-%@*(8!B&%).@)*%#=!Critical+ Social+
Policy2!41OYQ2!Y[MnY]M=!;))B-9ff/%*=%&1fPE=PP__fED[PEP]YDPPEE^]^P!
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Z(6*/2!F=2!e<B)(2!0=!\=2!l!e<B)(2!J=!ODEDDQ=!J!;,?&*/R-.@<&*),! ' %/.8!$%&!B&*6(@,R.#;(#@./!/*-)&*?<)./!/()(!
'*#*#1=!Journal+of+King+Saud+University+-+Computer+and+Information+Sciences2!34O[Q2!Y[EDnY[PM=!
;))B-9ff/%*=%&1fPE=PEP[fZ=Z\+5G"=DEDE=E[=EPE!
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