Kesimpulan KESIMPULAN DAN SARAN
76
Lampiran 14 Pola output target pembelajaran FNN
NO T1
T2 T3
NO T1
T2 T3
1 0.9734
0.9457 0.8853
54 0.9608
0.9875 0.9847
2 0.9858
0.9547 0.8765
55 0.9372
0.9750 0.9987
3 0.9987
0.9827 0.9273
56 0.9967
0.9935 0.9541
4 0.9476
0.9241 0.8493
57 0.9802
0.9987 0.9837
5 0.9946
0.9882 0.9461
58 0.9650
0.9927 0.9863
6 0.9984
0.9858 0.9364
59 0.9904
0.9937 0.9661
7 0.9841
0.9835 0.9468
60 0.9598
0.9888 0.9847
8 0.9938
0.9915 0.9431
61 0.9752
0.9721 0.9451
9 0.9991
0.9851 0.9287
62 0.9790
0.9974 0.9813
10 0.9952
0.9705 0.9073
63 0.9608
0.9855 0.9814
11 0.9881
0.9847 0.9326
64 0.9365
0.9764 0.9992
12 0.9770
0.9770 0.9547
65 0.9586
0.9876 0.9926
13 0.9868
0.9992 0.9768
66 0.9426
0.9707 0.9536
14 0.9973
0.9863 0.9333
67 0.9221
0.9534 0.9532
15 0.9974
0.9897 0.9434
68 0.9183
0.9628 0.9935
16 0.9880
0.9913 0.9475
69 0.8954
0.9482 0.9917
17 0.9925
0.9732 0.9198
70 0.9115
0.9582 0.9787
18 0.9923
0.9670 0.9065
71 0.9714
0.9919 0.9817
19 0.9968
0.9889 0.9362
72 0.9342
0.9706 0.9692
20 0.9849
0.9795 0.9367
73 0.9580
0.9873 0.9963
21 0.9922
0.9779 0.9295
74 0.8950
0.9482 0.9899
22 0.9940
0.9767 0.9139
75 0.8870
0.9359 0.9852
23 0.9946
0.9897 0.9381
76 0.9009
0.9481 0.9815
24 0.9821
0.9958 0.9677
77 0.9397
0.9720 0.9909
25 0.9794
0.9651 0.8983
78 0.9510
0.9839 0.9817
26 0.9880
0.9944 0.9715
79 0.9295
0.9665 0.9953
27 0.9978
0.9955 0.9523
80 0.9722
0.9958 0.9874
28 0.9756
0.9922 0.9653
81 0.9667
0.9930 0.9837
29 0.9692
0.9766 0.9533
82 0.9710
0.9824 0.9619
30 0.9895
0.9950 0.9559
83 0.9575
0.9663 0.9574
31 0.9846
0.9966 0.9668
84 0.9381
0.9762 0.9873
32 0.9919
0.9622 0.8932
85 0.8782
0.9175 0.9660
33 0.9915
0.9985 0.9647
86 0.9253
0.9676 0.9859
34 0.9832
0.9835 0.9560
87 0.9285
0.9708 0.9989
35 0.9900
0.9992 0.9699
88 0.8110
0.8736 0.9290
36 0.9846
0.9966 0.9668
89 0.8682
0.9177 0.9766
37 0.9870
0.9958 0.9718
90 0.8296
0.8869 0.9603
38 0.9919
0.9857 0.9428
91 0.8863
0.9342 0.9839
39 0.9387
0.9778 0.9978
92 0.9521
0.9863 0.9974
40 0.9715
0.9946 0.9897
93 0.8718
0.9187 0.9747
77
Lanjutan Lampiran 14 41
0.9721 0.9756
0.9472 94
0.9417 0.9795
0.9949 42
0.9881 0.9751
0.9244 95
0.8662 0.9193
0.9800 43
0.9648 0.9920
0.9930 96
0.8906 0.9245
0.9650 44
0.9830 0.9949
0.9699 97
0.8860 0.9375
0.9893 45
0.9976 0.9922
0.9447 98
0.9062 0.9439
0.9809 46
0.9486 0.9461
0.9009 99
0.9462 0.9789
0.9950 47
0.9663 0.9896
0.9916 100
0.9130 0.9569
0.9953 48
0.9515 0.9849
0.9845 101
0.9150 0.9614
0.9815 49
0.9041 0.9478
0.9679 102
0.9417 0.9675
0.9830 50
0.9899 0.9989
0.9744 103
0.9192 0.9602
0.9955 51
0.9862 0.9993
0.9778 104
0.9473 0.9615
0.9653 52
0.9759 0.9965
0.9890 105
0.9698 0.9862
0.9727 53
0.9353 0.9738
0.9851
Lampiran 15 Source code antar muka model klasifikasi kematangan manggis
function btnbukaimage_CallbackhObject, eventdata, handles
proyek=guidatagcbo; [namafile,direktori]=uigetfile{
.jpg ;
.bmp ;
.png ;
.tif },
Buka Gambar ;
I=imreadnamafile; setproyek.figmanggis,
CurrentAxes ,proyek.axes1;
setimshowI;
Lanjutan Lampiran 15
info=imfinfonamafile; setproyek.enama,
String , info.Filename;
setproyek.figmanggis, Userdata
,I; setproyek.enama,
Userdata ,info.Filename;
function btnolahimage_CallbackhObject, eventdata, handles
proyek=guidatagcbo; I=getproyek.figmanggis,
Userdata ;
nama=getproyek.enama, Userdata
; Hitung nilai green
r = I :,:,1; g = I:,:,2;
b = I:,:,3; varr = meanmeanr255;
varg = meanmeang255; varb = meanmeanb255;
sethandles.egreen, String
,varg;