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;