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Transkripsi

1 LAMPIRAN 1 109

2 110

3 111

4 LAMPIRAN 2 112

5 113

6 114 LAMPIRAN 3 Coding Hidden Layer 10: clc; clear all; close all; a=[1.07,1.01,0.93,0.90,1.25,1.18,1.03,0.89,0.81,1.25,1.15,1.13,1.05,0.98,0.83,0.87,1. 23,1.01,0.97,0.85,0.87,1.29,1.19,1.15,1.12]; size(a); p1 = a(:,13:24); p2 = a(:,1:12); p = [p1;p2] t = a(:,13:24) tv = a(:,25:30); % Set FFNN dengan jumlah unit hidden 10, fungsi input-hidden = tansig, % fungsi aktivasi hidden-output = linear, metode = traingd net = newff(minmax(p), [10,1], {'tansig','purelin'}, 'traingd'); net.trainparam.epochs = 10000; % maksimum epoch = net.trainparam.goal = 0.001; % target yang ingin dicapai MSE < net.trainparam.lr = 0.1; % nilai pembelajaran (alpha) = 0.1 net.trainparam.show = 100; % tunjukkan perubahan setiap epoch 100

7 115 %lakukan train terhadap input, target [net,tr]=train(net,p,t); %Simulasikan hasil training (pelatihan) y=sim(net,p) e = y-t; % error = output - target v=a; v(:,36)=0.1; % mencari prediksi untuk 12 bulan ke depan for i = 1:12 k=i+23; l=i+12; j=i+24; pv1=v(:,k); pv2=v(:,l); pv=[pv1;pv2]; v(:,j)=sim(net,pv); end % menampilkan hasil akhir dari bulan ke 1 s/d 36 Hasil_akhir = v(:,1:36) z = v(:,25:36); yv = v(:,25:30); e1=yv-tv;

8 116 MSE = mse(e); fprintf('mse_train = %12.8f\n',MSE); Kemudian dilakukan proses running program dan akan muncul window neural network training yang berisi proses dari pelatihan yang di-running.

9 117 LAMPIRAN 4 Coding Hidden Layer 12: clc; clear all; close all; a=[1.07,1.01,0.93,0.90,1.25,1.18,1.03,0.89,0.81,1.25,1.15,1.13,1.05,0.98,0.83,0.87,1. 23,1.01,0.97,0.85,0.87,1.29,1.19,1.15,1.12]; size(a); p1 = a(:,13:24); p2 = a(:,1:12); p = [p1;p2] t = a(:,13:24) tv = a(:,25:30); % Set FFNN dengan jumlah unit hidden 12, fungsi input-hidden = tansig, % fungsi aktivasi hidden-output = linear, metode = traingd net = newff(minmax(p), [12,1], {'tansig','purelin'}, 'traingd'); net.trainparam.epochs = 10000; % maksimum epoch = net.trainparam.goal = 0.001; % target yang ingin dicapai MSE < net.trainparam.lr = 0.1; % nilai pembelajaran (alpha) = 0.1 net.trainparam.show = 100; % tunjukkan perubahan setiap epoch 100 %lakukan train terhadap input, target

10 118 [net,tr]=train(net,p,t); %Simulasikan hasil training (pelatihan) y=sim(net,p) e = y-t; % error = output - target v=a; v(:,36)=0.1; % mencari prediksi untuk 12 bulan ke depan for i = 1:12 k=i+23; l=i+12; j=i+24; pv1=v(:,k); pv2=v(:,l); pv=[pv1;pv2]; v(:,j)=sim(net,pv); end % menampilkan hasil akhir dari bulan ke 1 s/d 36 Hasil_akhir = v(:,1:36) z = v(:,25:36); yv = v(:,25:30); e1=yv-tv; MSE = mse(e);

11 119 fprintf('mse_train = %12.8f\n',MSE); Kemudian dilakukan proses running program dan akan muncul window neural network training yang berisi proses dari pelatihan yang di-running.

12 120 LAMPIRAN 5 Coding Hidden Layer 14: clc; clear all; close all; a=[1.07,1.01,0.93,0.90,1.25,1.18,1.03,0.89,0.81,1.25,1.15,1.13,1.05,0.98,0.83,0.87,1. 23,1.01,0.97,0.85,0.87,1.29,1.19,1.15,1.12]; size(a); p1 = a(:,13:24); p2 = a(:,1:12); p = [p1;p2] t = a(:,13:24) tv = a(:,25:30); % Set FFNN dengan jumlah unit hidden 14, fungsi input-hidden = tansig, % fungsi aktivasi hidden-output = linear, metode = traingd net = newff(minmax(p), [14,1], {'tansig','purelin'}, 'traingd'); net.trainparam.epochs = 10000; % maksimum epoch = net.trainparam.goal = 0.001; % target yang ingin dicapai MSE < net.trainparam.lr = 0.1; % nilai pembelajaran (alpha) = 0.1 net.trainparam.show = 100; % tunjukkan perubahan setiap epoch 100 %lakukan train terhadap input, target

13 121 [net,tr]=train(net,p,t); %Simulasikan hasil training (pelatihan) y=sim(net,p) e = y-t; % error = output - target v=a; v(:,36)=0.1; % mencari prediksi untuk 12 bulan ke depan for i = 1:12 k=i+23; l=i+12; j=i+24; pv1=v(:,k); pv2=v(:,l); pv=[pv1;pv2]; v(:,j)=sim(net,pv); end % menampilkan hasil akhir dari bulan ke 1 s/d 36 Hasil_akhir = v(:,1:36) z = v(:,25:36); yv = v(:,25:30); e1=yv-tv; MSE = mse(e);

14 122 fprintf('mse_train = %12.8f\n',MSE); Kemudian dilakuan proses running program dan akan muncul window neural network training yang berisi proses dari pelatihan yang di-running.

15 123 LAMPIRAN 6 Coding Hidden Layer 16: clc; clear all; close all; a=[1.07,1.01,0.93,0.90,1.25,1.18,1.03,0.89,0.81,1.25,1.15,1.13,1.05,0.98,0.83,0.87,1. 23,1.01,0.97,0.85,0.87,1.29,1.19,1.15,1.12]; size(a); p1 = a(:,13:24); p2 = a(:,1:12); p = [p1;p2] t = a(:,13:24) tv = a(:,25:30); % Set FFNN dengan jumlah unit hidden 16, fungsi input-hidden = tansig, % fungsi aktivasi hidden-output = linear, metode = traingd net = newff(minmax(p), [16,1], {'tansig','purelin'}, 'traingd'); net.trainparam.epochs = 10000; % maksimum epoch = net.trainparam.goal = 0.001; % target yang ingin dicapai MSE < net.trainparam.lr = 0.1; % nilai pembelajaran (alpha) = 0.1 net.trainparam.show = 100; % tunjukkan perubahan setiap epoch 100 %lakukan train terhadap input, target

16 124 [net,tr]=train(net,p,t); %Simulasikan hasil training (pelatihan) y=sim(net,p) e = y-t; % error = output - target v=a; v(:,36)=0.1; % mencari prediksi untuk 12 bulan ke depan for i = 1:12 k=i+23; l=i+12; j=i+24; pv1=v(:,k); pv2=v(:,l); pv=[pv1;pv2]; v(:,j)=sim(net,pv); end % menampilkan hasil akhir dari bulan ke 1 s/d 36 Hasil_akhir = v(:,1:36) z = v(:,25:36); yv = v(:,25:30); e1=yv-tv; MSE = mse(e);

17 125 fprintf('mse_train = %12.8f\n',MSE); Kemudian dilakuan proses running program dan akan muncul window neural network training yang berisi proses dari pelatihan yang di-running.

18 126 LAMPIRAN 7 Coding Hidden Layer 15: clc; clear all; close all; a=[1.07,1.01,0.93,0.90,1.25,1.18,1.03,0.89,0.81,1.25,1.15,1.13,1.05,0.98,0.83,0.87,1. 23,1.01,0.97,0.85,0.87,1.29,1.19,1.15,1.12]; size(a); p1 = a(:,13:24); p2 = a(:,1:12); p = [p1;p2] t = a(:,13:24) tv = a(:,25:30); % Set FFNN dengan jumlah unit hidden 15, fungsi input-hidden = tansig, % fungsi aktivasi hidden-output = linear, metode = traingd net = newff(minmax(p), [15,1], {'tansig','purelin'}, 'traingd'); net.trainparam.epochs = 10000; % maksimum epoch = net.trainparam.goal = 0.001; % target yang ingin dicapai MSE < net.trainparam.lr = 0.1; % nilai pembelajaran (alpha) = 0.1 net.trainparam.show = 100; % tunjukkan perubahan setiap epoch 100 %lakukan train terhadap input, target

19 127 [net,tr]=train(net,p,t); %Simulasikan hasil training (pelatihan) y=sim(net,p) e = y-t; % error = output - target v=a; v(:,36)=0.1; % mencari prediksi untuk 12 bulan ke depan for i = 1:12 k=i+23; l=i+12; j=i+24; pv1=v(:,k); pv2=v(:,l); pv=[pv1;pv2]; v(:,j)=sim(net,pv); end % menampilkan hasil akhir dari bulan ke 1 s/d 36 hasil_akhir = v(:,1:36 z = v(:,25:36); yv = v(:,25:30); e1=yv-tv; MSE = mse(e);

20 128 fprintf('mse_train = %12.8f\n',MSE); Kemudian dilakukan proses running program dan akan muncul window neural network training yang berisi proses dari pelatihan yang di-running.

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