Contoh Presentasi Pembelajaran Berbasis Riset (PBR) Mata Kuliah Pemodelan Sistem
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SYSTEM MODELLING PROJECT- Aisha Adilla- Givanny Permata Sari- Hanny Riana- Latifa Ayu Lestari- Salma Nabila Hadi- Sarah Marsha Davinna
SquadBPJ
• BPJSquad consists of 6 female students from the Department of Industrial Engineering, Universitas Indonesia. This team was formed for the project of Systems Modeling class taught by Mr. Arry Rahmawan. In this project, we were asked to do research on the system of the BPJS Program in a public hospital, in our case RSUD Pasar Minggu.
Salma Nabila
Givanny Permata
Latifa Ayu
Aisha Adilla
Sarah Marsha
Hanny Riana
TEAM PROFILESquadBPJ
DETERMINETHEPROBLEM
MODELCONCEPTUALIZATION
DATAGATHERINGANDANALYSISIS
MODELCONSTRUCTION
VALIDATIONANDVERIFICATION
QUESTIONS
ANSWERS
CONCLUSIONAND
SUGGESTIONOUTPUTANALYSIS
OUTLINE
01
• Definingtheproblem,objectives,actualcondition,andscopeofourresearchproject
DEFINE THE PROBLEM
What?Serviceandqueuesystem
PROBLEM DEFINITION
Who?BPJSPatients(Bothnewandoldmembers)
When?Duringpeaktime
(Monday,Wednesday,andThursday)
Where?RSUDPasarMinggu
Why?Massivenumberof BPJSpatientsin
Jakarta
5
• Definingtheproblemwith5Wtools
6
PROBLEM DEFINITION• Definingthehypothesisoftheproblem
And how it will affects?If the management of RSUD Pasar
Minggu ignores this problem, the BPJS patient will feel uncomfortable while treating their disease in this hospital due to dissatisfaction of the hospital’s
BPJS system.
What are the symptoms?
Since RSUD Pasar Minggu has accepted BPJS patients, there have been problems in
the form of a massive amount of BPJS patients resulting in a long queueing time
in the registration, polyclinic, and pharmacy lines that makes patients
dissatisfied.
7
RESEARCH OBJECTIVES• Definingtheobjectivesofourresearchproject
• ReducingthequeuingtimeofBPJSatRSUDPasarMinggu
• ReducingtheservicetimeofBPJSatRSUDPasar Minggu
• Increasingcustomersatisfaction
• Answeringthequestionsthathavebeengiveninclass
02
• RepresentingtherealBPJSsystematRSUDPasar Minggu asaflowchart
MODELCONCEPTUALIZATION
MODEL CONCEPTUALIZATION• Usingaflowchartasarepresentationoftherealsystem
9
03
• Explainingthemethodusedfordatacollection,thetypesofdata,andanalysisofthedatausingStatFit
DATA GATHERINGAND ANALYSIS
DATA COLLECTION
DATA COLLECTION• Explainingthedatacollectionmethodandthedatatypes
12
Directobservation
Interview DirectMeasurement
Books /Journals
Inter-arrivalTime
13
DATA COLLECTION• Explainingaboutthedatathatwecollected
ArrivalRate
Service TimeandServiceRate
QueueTime
RegistrationTime
DATA COLLECTION• Explaininghowweknowthepeakdaysofthesystemandthesamplingmethod
14
We used direct observation and interview to know what the peak days are of the BPJS system at RSUD PasarMinggu
From direct observation, we got Monday, Wednesday, and Thursday as a peak day. This was also proven by the result of interviews with hospital management. The average population observed in Monday, Wednesday, and Thursday is 222 patients (the population from polyclinics are most influential). We get 144 samples from Slovin’s Formula.
We used stratified random sampling because the difference of cumulative from each registration and polyclinic.
Stratified Random SamplingFrom 144 samples, Registration Samples • BPJS Lama= 84% from population (121 patients)• BPJS Baru= 16% from population (23 patients)Polyclinics Samples• Penyakit Dalam= 44% from population (63 patients)• Jantung= 36% from population (51 patients)• Syaraf= 20% from population (29 patients)
• Based on direct observation (service time and arrival rate), 80% of the service time and arrival rate comes from 20% of the polyclinics: Penyakit Dalam, Jantung, and Syaraf
DATA COLLECTION• Usingpareto diagramtoknowwhichpolyclinicshouldbeobserved
15
0%20%40%60%80%100%120%
0%5%
10%15%20%25%30%35%40%
Jumlah Kedatangan Per Periode Waktu
% Kumulatif %
0%
20%
40%
60%
80%
100%
120%
0%
5%
10%
15%
20%
25%
Waktu Pelayanan
% Kumulatif %
DATA COLLECTION• Summingupalldatatofindmeanandstandarddeviation
16
REGBPJSLAMA Mean StandarDevDetik Menit Detik Menit
WaktuantarKedatangan 27.3 0.46 20 0.33Waktu Pelayanan (4server) 65.1 1.09 20.2 0.34
WaktuTunggu 6694 111.57 278.8 4.65
REGBPJSLAMA JumlahOrang/JamMean
WaktuantarKedatangan 131.87WaktuPelayanan 55.30
REGBPJSBARU Mean StandarDevDetik Menit Detik Menit
Waktu antar Kedatangan 224.30 3.74 213.30 3.56WaktuPelayanan(3server) 199.3 3.32 58.7 0.98WaktuTunggu 1413.9 23.565 766.6 12.78
REGBPJSBARU JumlahOrang/JamMean
WaktuantarKedatangan 16.05WaktuPelayanan 18.06
FARMASI Mean StandarDevDetik Menit Detik Menit
WaktuantarKedatangan 163.5 2.73 154.4 2.57WaktuPelayananScanBarcode 9.201 0.15 4.091 0.07PelayananMemberiObat 139.6 2.33 30.16 0.50WaktuTunggu 5890 98.16667 2482 41.37
FARMASI JumlahOrang/JamMean
TingkatKedatangan 22.02TingkatPelayananServer1 391.26Server2 25.79
DATA COLLECTION• Summingupalldatatofindmeanandstandarddeviation
17
POLIJANTUNG Mean StandarDevDetik Menit Detik Menit
WaktuantarKedatangan 178 2.97 116 1.93WaktuPelayanan(2server) 330.4 5.51 56.8 0.95Waktu Tunggu 9204 153.4 1796 29.93
POLIJANTUNG JumlahOrang/JamMean
TingkatKedatangan 20.22TingkatPelayanan 10.90
POLIPENYAKITDALAM Mean StandarDevDetik Menit Detik Menit
WaktuantarKedatangan 160.1 2.67 163.6 2.73Waktu Pelayanan (2server) 578.7 9.65 192.1 3.20WaktuTunggu 4349 72.48 1893 31.55
POLIPENYAKITDALAM JumlahOrang/JamMean
WaktuantarKedatangan 22.49WaktuPelayanan 6.22
POLISYARAF Mean StandarDevDetik Menit Detik Menit
WaktuantarKedatangan 233.28 3.89 197.66 3.29WaktuPelayanan(2server) 333.3 5.56 55.57 0.93WaktuTunggu 4526.0 75.43 695.80 11.60
POLISYARAF JumlahOrang/JamMean
TingkatKedatangan 15.43TingkatPelayanan 10.80
DATA ANALYSIS
DATA ANALYSIS• Identifyingthetypeofdistributionofeachdata
19
Normal
Normal
BPJS Lama
Inte
r-ar
rival
Ti
me
Serv
ice
Tim
e
DATA ANALYSIS• Identifyingthetypeofdistributionofeachdata
20
Normal
Wai
ting
Tim
e
DATA ANALYSIS
21
Exponential
Exponential
BPJS Baru
• Identifyingthetypeofdistributionofeachdata
Inte
r-ar
rival
Ti
me
Serv
ice
Tim
e
DATA ANALYSIS• Identifyingthetypeofdistributionofeachdata
22
Normal
Wai
ting
Tim
e
DATA ANALYSIS
23
Normal
Normal
Poli Penyakit Dalam
• Identifyingthetypeofdistributionofeachdata
Wai
ting
Tim
eSe
rvic
e Ti
me
DATA ANALYSIS• Identifyingthetypeofdistributionofeachdata
24
Normal
Normal
Poli Jantung
Wai
ting
Tim
eSe
rvic
e Ti
me
DATA ANALYSIS
25
Normal
Normal
• Identifyingthetypeofdistributionofeachdata
Wai
ting
Tim
eSe
rvic
e Ti
me
Poli Syaraf
DATA ANALYSIS
26
Normal
Normal
Farmasi
• Identifyingthetypeofdistributionofeachdata
Wai
ting
Tim
eSe
rvic
e Ti
me
of S
cann
ing
Bar
code
DATA ANALYSIS
27
Normal
• Identifyingthetypeofdistributionofeachdata
Serv
ice
Tim
e of
Giv
ing
Med
icin
e
04
• IllustratingabouthowaProModel modelisbuilttorepresenttherealsystem
MODEL CONSTRUCTION
MODEL CONSTRUCTIONIllustratinghowaProModel modelisbuiltrepresenttherealsystem
29
ENTITIES
MODEL CONSTRUCTION
30
IllustratinghowaProModel modelisbuiltrepresenttherealsystem
LOCATIONS
MODEL CONSTRUCTION
31
IllustratinghowaProModel modelisbuiltrepresenttherealsystem
ARRIVALS
MODEL CONSTRUCTION
32
IllustratinghowaProModel modelisbuiltrepresenttherealsystem
PROCESSING
MODEL CONSTRUCTION
33
IllustratinghowaProModel modelisbuiltrepresenttherealsystem
ARRIVALCYCLES
MODEL CONSTRUCTION
34
IllustratinghowaProModel modelisbuiltrepresenttherealsystem
ATTRIBUTES
MODEL CONSTRUCTION
35
IllustratinghowaProModel modelisbuiltrepresenttherealsystem
USERDISTRIBUTION
MODEL CONSTRUCTION
36
IllustratinghowaProModel modelisbuiltrepresenttherealsystem
• Final ProModel for the BPJS system of RSUD Pasar Minggu
When model pause When model run
05
• Thevalidationandverificationofmodelconceptualizationandcomputermodel
VALIDATION AND VERIFICATION
VALIDATION
VALIDATION• Modelconceptualizationvalidation
39
Trace Validity Face Validity
Trace in PromodelUsing a feature in ProModel(trace) to trace the truth of the model logic and computer model (debugging)
Validity in Real LifeChecking the validity of model conceptualization by asking people who know the system well and trusted
Determining the truth of model flow diagram or model logic mechanism
VALIDATION• Modelconceptualizationvalidation
40
Trace Validity
VALIDATION• Modelconceptualizationvalidation
41
Weinterviewedpeoplefrominformationcentreandalsosecurity
whoisondutyandalwaysobservethequeuingsystemofBPJSpatientsin
RSUDPasar MingguFace Validity
Interviewrecordingattached
VALIDATION• ProModel validation
42
Comparing with Queuing Theory
Watching theAnimation
ExtremeCondition Test
RunningTraces
Comparing output from the simulation with queuing theory
Watching the computer model that has conducted
Testing the model using 2 extreme conditions
Stage of processes are traced using the processing logic model to be compared with the actual model
VALIDATION• ProModel validation
44
Comparing with Queuing Theory
VALIDATIONOF BPJSBARUPROMODELWITHQUEUINGTHEORYCALCULATION
Arival Rate 4.545455
Service Rate 14.66667
AverageUtilization Rate 0.3099174
TheProbability SysteminEmptySituation 73.342039%
AverageNumberofPatientinQueuing 44.43555
AverageNumberofPatientinSystem 44.745468
Average Patient’sWaitingTimeinQueuing 9.7758211
Average Patient’sWaitingTimeinSystem 9.8440029
VALIDATION• ProModel validation
45
Watching the Animation
VALIDATION• ProModel validation
46
• Total entities: 22200
Extreme Condition Test
• Total entities: 0
VALIDATION• ProModel validation
47
Running Traces
VERIFICATION
VERIFICATION• Modelverification
49
Watching theAnimation
Using Trace andDebugging Facilities
Reviewing theModel Code
Visual verification whether the model
running has been right
Checking for code errors or inconsistency in the statistics results
• Trace : chronologically describe what’s happening during the simulation
• Debugger : showing the stages of the processes in the simulation
• Trace & Debugger enable us to look deeper what’s happening in the simulation
VERIFICATION• Modelverification
50
Watching the Animation
VERIFICATION• Modelverification
51
Reviewing the Model Code
VERIFICATION• Modelverification
52
Reviewing the Model Code (cont’d)
VERIFICATION• Modelverification
53
Reviewing the Model Code (cont’d)
VERIFICATION• Modelverification
54
Reviewing the Model Code (cont’d)
(1) (2)
VERIFICATION• Modelverification
55
• There are no bugs, so the model can run perfectly
Running Trace and Debugging Facilities
06
• ShowingthestatisticsresultsofProModel
OUTPUT ANALYSIS
OUTPUT ANALYSISShowingthestatisticsresultsofProModel
57
OUTPUT ANALYSISShowingthestatisticsresultsofProModel
58
OUTPUT ANALYSISShowingthestatisticsresultsofProModel
59
07
• AnsweringthequestionsthathavegivenbyMr.Arry Rahmawan
QUESTIONS & ANSWERS
ANSWER TO THE 1ST QUESTION
61
• Whatisthebestlineformation?• SingleLine->MultiServer
SquadBPJ
62
• Wherearetheworstbottlenecksofsystem?
SquadBPJANSWER TO THE 2ND QUESTION
BPJSBaru Registration• Alotofthenewpatientsdonotknowaboutthedocumentstheymustbringinordertoregistersothatsometimestheyhavetogobackhomeorgotophotocopystationifthere’smissingdocuments.Thisaffecttheregistrationtimesincesometimestheyhavebeencalledbutthey’restillsomewhereelse.
BPJSLamaRegistration• Lackofserver&ineffectivequeuingposition
Poli Penyakit• Everyserverhasdifferentopeningtimewherealotofpatientshavebeenwaiting
Pharmacy• Thereisnospecificjobforeachemployees&sometimesthereisdowntimesintheDoctorssendthemedicinereceiptbyserveronline
63
• Howmanystaffshouldbeassignedtoreachtheobjectivewiththelowestpossiblecost?
SquadBPJANSWER TO THE 3RD QUESTION
BPJSBaru• 4staff
BPJSLama• 7staff
Poli Penyakit• Penyakit Dalam 8staff,Syaraf 4staff&jantung 4staff
Pharmacy• 2staff
64
• Whatarethenewandmosteffectivebusinessprocessideasforthehospitaltoreachtheobjective?
SquadBPJANSWER TO THE 4TH QUESTION
BPJSLama• Addservers&services,&giveclearway-findinginstructionforpatients
Poli Penyakit Dalam• Openroomthatusedtobeunused,openeverycheckingroomatthesametime(7.30a.m.)&makethefirstlineaspriorityseat
Poli Jantung• Openeverycheckingroomatthesametime(7.30a.m.)&make8priorityseat
Poli Syaraf• Openeverycheckingroomatthesametime(7.30a.m.)
32
• Othersolutionsthatshouldbeconsidered
SquadBPJANSWER TO THE 5TH QUESTION
Makeaclearway-finding&signage
system
MakeinformationboarddetailingtheproceduresofBPJS
registration
Utilizingthewebsiteforreal-timewaitinglineinformationat
thehospital
Makingaclearjobdescriptionfor
resourcestoavoididlehumanresources
08MODEL IMPROVEMENT
ANALYSIS MODEL IMPROVEMENT
32
ANALYSIS MODEL IMPROVEMENT
68
TheDataAfterImprovement(WaitingTime)BPJSLAMA =79.23minBPJSBARU =40.34minPOLIPD =68.71minPOLISYARAF =60.86minPOLIJANTUNG =77.21minFARMASI =50.31min
TheDataBeforeImprovement(WaitingTime)BPJSLAMA =112.4minBPJSBARU =47.71minPOLIPD =98.67minPOLISYARAF =71.48minPOLIJANTUNG =155.59minFARMASI =196.42min
69
ANALYSIS MODEL IMPROVEMENT
09
• Concludingourprojectresearch
CONCLUSION
CONCLUSIONConcludingourprojectresearch
71
• Theprocessoffindingsolutionsthroughmakingmodelsinvolvesmakingaconceptualmodelbasedontherealworld andthenacomputermodelbasedontheconceptualmodel.
• TheBPJSsysteminRSUDPasar Minggu consistsofBPJSLamaregistration,BPJSBaru registration,polyclinic,andpharmacy.
• Thebottleneckofthesystemrelativetothenumberofentriesis PoliJantung.
• Weimprovedthesystembyaddingserversandintroducingapunctualitypolicyfordoctorsandemployees.
THANK YOU
- Aisha Adilla (1406606152)- Givanny Permata Sari (1406606070)- Hanny Riana (1406606341)- Latifa Ayu Lestari (1406606354)- Salma Nabila Hadi (1406553133)- Sarah Marsha Davinna (1406553285)
SquadBPJ
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