1. AUTHOR : LIDYA ( 26406077 ) UNDER THE GUIDANCE OF: YULIA, M. KOM. TANTI OCTAVIA, M. ENG. Designing and Making forecast Application to determine rate of Purchase In "X" construction store with ARIMA Method

2. Background Out of stock for particular item.

3. Main Goal Make application sales forecasting with ARIMA method. Help owner to determine the purchase item quantity.

4. Problem Statement How to make a system with ARIMA method. How to determine purchase items from forecast result

5. ARIMA ARIMA models are, in theory, the most general class of models for forecasting a time series which can be stationarized by differencing. Step forecasting with ARIMA  Identification temporary model using past data to obtain the ARIMA model.  Estimation parameters of ARIMA models using past data.  Diagnostic check to examine feasibility of the model.  Implementation.

6. DFD (Contex Diagram)

7. DFD (Level 0)

8. Flowchart Forecasting

9. Flowchart Forecasting(cont.)

10. ACF

11. PACF

12. AR

13. MA

14. ARMA

15. ARIMA

16. ERD ( Conceptual Data Model )

17. ERD ( Physical Data Model )

18. EXPERIMENTAL RESULT Program Validation Data used to test program validation is: PeriodeJumlah Penjualan 12 22 33 42 53 65

19. Determine Model ACF Interval = ±( 1,96 * (1/ (6) 1/2 )) = ±0,799 lagACFSACFTACF 10.0930.4080.228 2-0.1780.411-0.433 30.1340.4240.316 4-0.2840.431-0.659 5-0.0260.461-0.057 60.0000.4860.000

20. Determine Model PACF ordoPACFSPACFTPACF 10.0930.4080.228 2-0.1890.408-0.463 3-0.1790.408-0.439 4-0.3630.408-0.890 5-0.1500.408-0.368 6-0.1740.408-0.426 P =1, d =0, q =1

21. Forecasting AR X t = 1 + ε t +0,833* X t-1 dataForcasterror 211 23 330 24-2 330 541 MSE = 1,167

22. Forecasting MA X t = 2,423+ ε t - -0,961* ε t-1 dataForcasterror 23 220 330 23 321 541 MSE = 0,667

23. Forecasting ARMA X t = 1 + ε t +0,833* X t-1 -0,961* ε t-1 dataForcasterror 211 24-2 312 26-4 34 58-3 MSE = 8,33

24. Diagnostic Check dataerrorACF(error) 2 0.024 20 0.069 30 0.146 2 -0.082 31 0.033 51 -0.318 Q = 0,658 χ 2 (0.5,4) = 9.48773 Q< χ 2 (0.5,4)

25. EXPERIMENTAL RESULT Result with manual calculation MA dataForcasterror 23 220 330 23 321 541 Result with Program

26. EXPERIMENTAL RESULT Experimental for kind of Data This Experimental use 2 type data. First is roller brush and secondary is paint brush. Data of paint brush.Data of roller brush. Periode Jumlah Penjualan 12 22 33 42 53 65 Periode Jumlah Penjualan 15 27 34 49 57 612

27. EXPERIMENTAL RESULT Result from Data of roller brush.

28. EXPERIMENTAL RESULT Result from Data of paint brush.

29. Conclusion Applications tested on users in design, completeness of features, ease of use of the application, the accuracy of forecasting, and the overall program.  Aspects of the application design, 50% user answered fairly, 33.3% said both, and 16.7% said less.  Aspects of the completeness of features, 50% user said good, and 50% said enough.  Aspect easy to use program, users answered 50% moderate, and 50% say unfavorable.  Aspect of program correctness, the user answered 73.3% moderate and 16.7% unfavorable.  Overall assessment of the program, users answered 73.3% moderate, and 16.7% is good.

30. Sugestion Need a development with Hybrid method

31. THANKS FOR YOUR ATTENTION

32. EXPERIMENTAL RESULT Experimental for Data PeriodeData PenjualanPeriodeData Penjualan 112020140 213721220 318822240 411623253 518024130 612025260 71002697 82902778 94502875 1045029126 1148530100 1235031100 1363032120 1417633220 1515034100 1613035340 1710036200 1832037100 1984 This experimental using data of Metrolite 3lt

33. EXPERIMENTAL RESULT Result forecasting in random period PeriodeHasil PeramalanData Penjualan Sebenarnya 8116290 9224450 10857450 11677485 12884350 13618630 32210120 33266220 34288100 35325340 36395200 37332100