Ketika saya mengampu mata kuliah permodelan sistem, di mana mata kuliah ini merupakan mata kuliah untuk mahasiswa tingkat 3, saya menugaskan mahasiswa untuk melakukan sebuah penelitian sederhana dengan menerapkan prinsip - prinsip ilmiah ke lapangan langsung. Saya juga menantang mereka untuk dapat mempresentasikan hasil penelitian mereka dengan tampilan slide yang tidak biasa dan menjemukan. Hingga akhirnya, inilah beberapa di antaranya. Bagaimana menurut Anda?

1. PARKING SYSTEM MODELLING
of Mall Ambassador
Group 7
Anindya Alfi Septyanti (1306448110)
Anggi Hazella (1306370051)
Felisa Fitriani (1306369945)
Nadila Aristiaputri (1306393023)
Natasya Sheba S (1306370146)
Timotius Alfin (1306409633)
Dosen Pembimbing : Arry Rahmawan, ST, MT

2. 2
Mall Ambassador
Capacity
360 parking spots.
Parking Lot
Has 3 basement levels of
parking lot.
Location
Jl. Prof.Dr.Satrio No. 14,
Kuningan, Jakarta Selatan,
Banten 15810, Indonesia
Working Hours
10.00 – 22.00

3. Mall Ambassador is one of the most favorite place for people in
the terms of buying electronics and shopping clothes. Among other
malls and shopping centers that also sell variety of electronics,
Mall Ambassador gives more affordable prices than other places.
Branded cheap clothes can also be found here.
3

4. 4
Problem
Formulation
Model
Conceptualization
Formal System Modelling Methods
Data Collection and
Analysis
Model Construction Validation and
Verification
1 2 3 4 5
Project Report and
Presentation
6

5. Problem Formulation

6. 6
With such high visitors coming to Mall
Ambassador and many of them drive cars,
this Mall only provides small spaces for
parking – only 360 parking spots available
– causing a queue when entering the parking
spot and difficulties in finding the parking
spot, especially on peak time.
Problem Statement

7. 7
Make the model based on the data we
obtained from observation. Based on the
model we can conclude whether the
parking system of Mall Ambassador is
already optimum and met the criteria that
had given previously.
Objectives

8. Model Conceptualization

9. 9

10. Data Collection and Analysis

11. 11
Determining data
requirement
Source of data
Analyze the data
using software
Step of Data Collection

12. 12
Arrival time
Quantity of parking areaService time
Arrival rate Pattern of parking area
Distribution
Number of servers
Duration time at the Mall
Data Requirement
Searching time
Working hour
Peak hour
What do we need to do simulation?

13. 13
System Documentation
We made a documentation of
layout and quantity parking
area
Personal Observation
We did personal observation
such as direct observation and
made a questionnaire
Personal Interviews
We interviewed Mr. Zaeni as
Head of Parking Area at
Ambassador Mall
Resource of Data
Where are the data come from?

14. 14
Mall Ambassador Jakarta
Place
26 September 2015
Date
10.00 am – 10.00 pm
Time
Observation

15. Result of Data Observation
15

16. 16
Data Processing in Observation
We observed from 10.00 am
until 22.00 (12 hours), then we
processed data to determine
peak hours in Ambassador Mall.
The result is from 11.00-17.00
is peak hours in Ambassador
Mall
131
228
255
195
158
187
144
127
83 79
66
35
0
50
100
150
200
250
300
Number
of
Car
Arrival
Rate

17. Data Processing in Observation
228
255
195
158
187
144
0
50
100
150
200
250
300
Arrival
Rate
in
Peak
Hour
avg
arrival
rate
7.20
5.40 5.51
6.24
5.44
5.84
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
Service
Time
in
Peak
Hour
avg
service
time
15.79
14.07
18.44
22.76
19.04
24.95
0.00
5.00
10.00
15.00
20.00
25.00
30.00
Inter
Arrival
Time
in
Peak
Hour
avg
inter
arrival
time
17

18. 18
Spreadsheet

19. 19
Data Analysis in Observation
Arrival Rate Service Time

20. 20
Data Analysis in Observation
Searching Time Inter Arrival Time

21. 21
Questionnaire
The questionnaire consists of:
01 How often do they go to Mall Ambassador
02 Time interval in entering the Mall Ambassador
03Duration of parking time in Mall Ambassador
04 The length of waiting time in queue

22. 22
Result of Questionnaire
20
44
21
8
0
5
10
15
20
25
30
35
40
45
50
10.00-­‐12.00 12.00-­‐15.00 15.00-­‐18.00 18.00-­‐21.00
Arrival
TimeQuestionnaire
Arrival
Time
10.00-­‐12.00 20
12.00-­‐15.00 44
15.00-­‐18.00 21
18.00-­‐21.00 8
Time
Duration
1-­‐2
jam 27
2-­‐3
jam 50
>3
jam 16
27
50
16
0
10
20
30
40
50
60
1-­‐2
jam 2-­‐3
jam >3
jam
Time
Duration

23. 23
Data Analysis in Questionnaire
This distribution shows the
distribution of data from
questionnaire is normal
distribution.

24. 1
2
3
4
5
67
89

25. 25
Attachments
Form Online Questionnaire

26. Model Construction

27. 27
Model Construction
Put a layout as
background for the
model
Build location and
Location Logic for
the model
Build Entities and
Entities Logic for the
model
Build Process and
Process Logic for
the model
Build Arrivals and
Arrival Logic for
the model
1 2 3 4 5
Run the model
6
Entrance
Queue
Locket
Parking Area
Exit

28. Put a layout as background for the model
28

29. Build Location and Location Logic for the model
29

30. 30
Build Location and Location Logic for the model

31. Build Entities and Entities Logic for the model
31

32. Build Process and Process Logic for the model
32

33. Build Arrivals and Arrivals Logic for the model
33

34. Run the model
34
Click to watch the video of model simulation

35. Validation and Verification

36. 36
01 Watching the animation
02 Comparing with other models
03Conducting degeneracy and
extreme condition test
Validation
We use these following techniques
for validating the model:
04 Performing sensitivity
analysis
05Running trace

37. 37
Watching the Animation
After the model was done, we have to
run the model to see whether the model
is correct or not.

38. Comparing with Other Models
We have to make sure the model is correct by
comparing with the excel data. After we run the model,
we can see the statistic, if the number is the same with
the excel calculating, then our model is finally correct
38

39. 39

40. 40
Conducting Degeneracy and
Extreme Condition Test
In conducting degeneracy and extreme condition test
we can change the arrival rate. Assume that we
change the arrival rate to 0 (zero) following with the
accuracy, then the result should be:
No car arrive in the entry queue. If
it is happened, then our model is
finally correct

41. In performing sensitivity analysis, we can try to change the service
time, if we change into the smaller number of service time there
will be no queue, if we change into bigger number there will be
queue. After we try, our model adjust with the changing of
numbers, it means the model is correct
Performing Sensitivity Analysis
41

42. 42
Running Trace
Running trace will show all of the event on the discrete model.
There is no error on our model, based on the trace results

43. 43
01 Reviewing model code
02 Checking for reasonable input
and output
03Watching the animation
Verification
In order to verified the model, we use some techniques:
04 Using trace and debugging
facilities

44. Reviewing Model Code
44

45. Checking for Reasonable Input and Output
45
The number of entry car is same as the number of exit car and
the number of car at the current location, so input and output
are reasonable.

46. Watching the Animation
We can know that the model is correct if the model is running
until it’s done correctly and without bug.
46

47. 47
Using Trace and Debugging Facilities
We use trace and debugging facilities to make sure we build the
model correctly implemented with good input and structure.
There is no error on our model, based on the trace results. There is no
debugging in our model.

48. Results and Recommendations

49. 49
01 The peak time is at 11.00-17.00
02
-Arrival Rate at peak Time:
P(195;41.8) sec
-Service Time at peak Time:
L(5.94;0.69) sec
03Average waiting time in
queue: 0.15 sec
Results
Our Result is:
04 Average cars in queue: 0.01
05
The data from spreadsheet is
same with the data from
promodel

50. 50
01
The capacity should be
increased by 25 parking slot to
meet the requirement.
02
The number of ticket locket is
enough to meet the
requirement.
Recommendation
Our Recommendation is:

51. THANK YOU!