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Contoh Desain Slide Presentasi Ilmiah Kreatif dan Menarik #3

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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?

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Contoh Desain Slide Presentasi Ilmiah Kreatif dan Menarik #3

1. 1. QUEUEING MODELKOTA KASABLANKA MALL PARKING SPACES
2. 2. MEET OUR TEAM Head of Team Aditya Nursyamsi Coordinator of Data Analysis Adinda AmaIia I Coordinator of Promodel Leo Hubertus Dimas A Coordinator of Verification Widi Kusnantoyo Coordinator of Validation Nur Annisamatin Coordinator of Spreadsheet Gaby Reveria Helianto Vice Coordinator of Promodel Bagus Novan S
3. 3. OUTLINE OF PRESENTATION Determine the Problem Model Construction Data Collection and Analysis Model Conceptualization Conclusion and Suggestion Validation & Verification 1 65432
4. 4. Determine The Problem
5. 5. PROBLEM DEFINITION Problem hypothesis of Kota Kasablanka Mall Parking System And how it will affects? If PT Secure Parking Indonesia in Kota Kasablanka Mall ignores this problem, there will be a decreasing number of customers in the motorcycle parking area of Kota Kasablanka Mall due to the difficulty in looking for parking space. What is the symptoms? Since September 26th, 2015, the motorcycle parking area in Kota Kasablanka Mall is having problem due to the over capacity of the parking area, queueing in arrival line, and queueing to find parking spaces that makes customers unsatisfied.
6. 6. OBJECTIVES OF THE RESEARCH Determine when the peak time of arrival number in Kota Kasablanka Mall Parking Spaces, in range 6 hours. To know how much arrival rate and service time in Kota Kasablanka Mall Parking Spaces when peak time 1. 2. 3. 7. 5. To know how long the average waiting time for one visitor in counter of Kasablanka Mall Parking Spaces when peak time 6. To know how long queueing rate in counter of Kasablanka Mall Parking Spaces when peak time4. Making reccomendation related to number of server to reduce queuing time on one customer before she/he is served in counter (into 2 minutes) Making reccomendation related to parking capacity to reduce queuing time of one customer before get the parking (into 2 minutes) Making discrete event modeling and comparing with spreadsheet model The objectives are used to answer the questions
7. 7. Model Conceptualization
8. 8. MODEL CONCEPTUALIZATION • Numb of server : 4 • Server are used : 2 • Type of server : manual • Parking segmentation : 5 Notes This is used as representative of real system to simplify the system
9. 9. Data Collection And Analysis
10. 10. Data Collection
11. 11. DATA COLLECTION & ANALYSIS How we get the data? Time Observation Online Survey 1 Advanced Judgement What are types of data?2 Inter Arrival time Service Time and Service Rate Arrival Rate Find Parking Time Parking TIme TICKET Data Collection Methodology and Types of Data 1 2 3 4 5 Direct Measurement
12. 12. DATA COLLECTION & ANALYSIS When are the peak days and peak hour of Kota Kasablanka Mall Parking Spaces? We used online survey and advanced judgement to know when is the peak days of parking spaces From online survey we got Saturday as a peak day also proved by result of advanced judgement From the result of direct observation we got distribution of arrival rate that showed from graphic below 1 3 1 4 8 67 16 Number of Arrival 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% Arrival rate/hour So the 6 peak hours are Saturday at 11.00 – 12.00, 12.00 – 13.00, 16.00 – 17.00, 17.00 – 18.00, 18.00 – 19.00
13. 13. DATA COLLECTION & ANALYSIS Server 1 Sever 2 For promodel Inter arrival time Mean 14.95 second/person 15.69 second/person Standard Deviasi 18.34 second/person 21.02 second/person Service Time Mean 9.32 second/person 9.87 second/person Standard Deviasi 3.46 second/person 3.15 second/person For spreadsheet Arrival rate Mean 215.67 person/hour 216.33 person/hour Standard Deviasi 47.92 visitors/person 72.03 person/hour Service Rate Mean 386.39 person/hour 364.69 person/hour Sum up all data for inter arrival time, arrival rate, service time and service rate How we get data? Direct Observation
14. 14. DATA COLLECTION & ANALYSIS Sum up all data for find parking time How we get data? Time Obserbation 1 2 3 4 5 Direct Measurement Distance of Actual System (m) From To Capacity Time estimated (detik) 17.05 Entrance Block A 320 47.36111111 17.3 Entrance Block B 320 47.91666667 44.6 Entrance Block C 400 123.9444444 42.2 Entrance Block D 215 117.2777778 64.7 Entrance Block E 500 179.6111111 Total 1755
15. 15. DATA COLLECTION & ANALYSIS Sum up all data for find parking time Online Survey How we get data? Time Interval (hour) Middle Point (minute) (Xi) Frequency (fi) Xi * fi 0.5 - 1 45 1 45 1 - 1.5 75 4 300 1.5 - 2 105 3 315 2 - 2.5 135 18 2430 2.5 - 3 165 23 3795 3 - 3.5 195 14 2730 3.5 - 4 225 15 3375 4 - 4.5 255 9 2295 4.5 - 5 285 6 1710 5 - 5.5 315 2 630 5.5 - 6 345 2 690 > 6 375 3 1125 Total 100 19440 Average Parking Time (minute) 194.4
16. 16. Data Analysis
17. 17. DATA COLLECTION & ANALYSIS Identify distribustion of each type data 1 Service Time Server 1 2 Service Time Server 2 Log Logistic Distribution
18. 18. DATA COLLECTION & ANALYSIS Identify distribustion of each type data 3 Arrival Rate Server 1 4 Arrival Rate Server 2 Poisson
19. 19. DATA COLLECTION & ANALYSIS Identify distribustion of each type data 5 Long Time Parking Normal distribution
20. 20. Model Construction
21. 21. MODEL CONSTRUCTION To make a model, we should at least have 4 basic Elements Locations1
22. 22. MODEL CONSTRUCTION To make a model, we should at least have 4 basic Elements (cont’d) Entities, only consist of 1 entity, the customer itself2
23. 23. MODEL CONSTRUCTION To make a model, we should at least have 4 basic Elements (cont’d) Arrivals, The arrival is divided into two, one for the front gate and the other for the backdoor gate3
24. 24. MODEL CONSTRUCTION To make a model, we should at least have 4 basic Elements (cont’d) Processing, The process is quiet the most difficult of it all because it contains many step, routing, and also logic 4
25. 25. MODEL CONSTRUCTION Final Model Result beginning and the end simulation So, this is our final Model for Kota Kasablanka’s Parking System At first one hour simulation At the last hour of simulation1 2
26. 26. Validation & Verification
27. 27. Validation Model Conceptualization Validation
28. 28. VALIDATION & VERIFICATION Checking by asking someone whose knowledge of the system is trusted Determining the truth of model flow diagram or model logic mechanism Trace Validity1 2 Model Conceptualization Validation Face Validity Tracing the truth of the model logic and computer model (debugging)
29. 29. VALIDATION & VERIFICATION Trace Validity Result1 Model Conceptualization Validation VALID
30. 30. VALIDATION & VERIFICATION Asked one of the Kota Kasablanka’s Parking Supervisor called Mr. Tri Direct observation on the field Model Conceptualization Validation Face Validity2 VALID
31. 31. Validation Model Validation
32. 32. VALIDATION & VERIFICATION Model Validation Watching the Animation Comparing with Other Model Comparing output from the simulation with other valid model (such as spreadsheet) 1 Conducting Degeneracy and Extreme Condition Test Testing the model using 2 extreme conditions Running Traces Stage of processes are traced using the processing logic model to be compared with the actual model 2 3 4
33. 33. VALIDATION & VERIFICATION Comparing with Other Model Model Ws = 0.24 + 0.15 = 0.39 Wq = 0.24 Ls = 0.97 + 0.62 = 1.59 Lq = 0.97 Excel Ws = 0.40485 Wq = 0.249811 Ls = 1.46 Lq = 0.8993 SERVER 11a Model Validation
34. 34. VALIDATION & VERIFICATION Model Validation Model Ws = 0.32 + 0.16 = 0.48 Wq = 0.32 Ls = 1.22 + 0.63= 1.85 Lq = 1.22 Excel Ws = 0.486383 Wq = 0.321999 Ls = 1.76 Lq = 1.1646 Comparing with Other Model SERVER 21b
35. 35. Watching the Animation2 VALIDATION & VERIFICATION Model Validation
36. 36. Conducting Degeneracy and Extreme Condition Test 1st Extreme Condition : Occurrence 0 2nd Extreme Condition : Quantities 100 Long Queue No Visitors VALIDATION & VERIFICATION Model Validation 3
37. 37. Running Traces VALIDATION & VERIFICATION Model Validation 4
38. 38. VALIDATION & VERIFICATION VALID Through model conceptulization validation and model validation, we know that the model is valid
39. 39. Veficiation Model Verfication
40. 40. VALIDATION & VERIFICATION Model Verification Visual verification whether the model running has been right Checking for code errors or inconsistency 1 2 3Watching the Animation Reviewing Model Code Using Trace and Debugging Facilities • 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
41. 41. Watching the Animation1 VALIDATION & VERIFICATION Model Verification
42. 42. VALIDATION & VERIFICATION Model Verification Reviewing Model Code2
43. 43. VALIDATION & VERIFICATION Model Verification Reviewing Model Code (cont’d)2
44. 44. VALIDATION & VERIFICATION Model Verification Reviewing Model Code (cont’d)2
45. 45. VALIDATION & VERIFICATION Model Verification Using Traces and Debugging Facilities2 There are no bugs, so the model can run perfectly
46. 46. VALIDATION & VERIFICATION VERIFIED Through model verification, we know that the model is verified and can run properly
47. 47. Output Analysis, Conclusion &Suggestion,
48. 48. Output Analysis
49. 49. Data at per Locations OUTPUT ANALYSIS
50. 50. The utilization of the D area is the highest. This is because D is the one with the least number of Capacity, so it always appears like it is ‘full’ OUTPUT ANALYSIS
51. 51. Location states multi OUTPUT ANALYSIS
52. 52. Location states single OUTPUT ANALYSIS
53. 53. OUTPUT ANALYSIS
54. 54. Conclusion and Suggestion
55. 55. These are the conclusions which also represent as the answer for the research objectives Mall Kota Kasablanka most peaked time is during Saturday at 12-2 PM and 4-8 PM To answer number 2, 3, and 4 please look at the table below: CONCLUSION 1 3 1 4 8 67 16 Number of Arrival 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% Arrival rate/hour Server 1 Server 2 Arrival Rate 216 Visitors/Hr 217 Visitors/Hr Service Time 9.3 sec with 3.5 sec standar deviation 9.8 sec with 3.2 sec standar deviation Wq 0.25 minutes 0.32 min Ws 0.4 minutes 0.48 min Lq 0.9 Visitors 1.16 Visitors Ls 1.46 Visitors 1.76 Visitors
56. 56. These are the conclusions which also represent as the answer for the research objectives Here is the model and the data comparison between promodel and spreadsheet Promodel Spreadsheet Server 1 Server 2 Server 1 Server 2 Average Number of Customers in the Queue (Lq) 0.97 1.22 0.9 1.16 Average Number of Customers in the System (Ls) 1.59 1.85 1.46 1.76 Average Waiting time in the Queue (Wq) 0.24 0.32 0.25 0.32 Average Time in the System (Ws) 0.39 0.48 1.46 0.48 CONCLUSION
57. 57. Then those statements lead to a final conclusion that our Hypothesis for Mall Kota Kasablanka Parking System is DENIED These are the suggestion for Kota Kasablanka Parking Spaces SUGGESTION Actually there are nothing wrong with Kota Kasablanka’s Parking System. The model do not show a significant number of waiting time both when in the queue for the check in counter (average 0.16 min) or when they search for a parking spot (1 min) The current number of server used is also not really a problem, because there’s not a significant number of people in the queue. So, we think it’s best for now just to use one server per gate. The current total capacity is 1755 spots, which is a huge number of capacity for a parking lot. So, for now Mall Kota Kasablanka do not have to add the capacity of the parking lot, because it is already sufficient
58. 58. THANKYOU