I have a problem. Please help !

how to train my some data series. i.e I have data beban=5,10,15,20,25,30,35,40,45,50 and penduduk=100,200,300,400,500,600 and ekonomi=50000,100000,150000,200000,250000,300000. how to make matrix input in workspace and the script if my script like this?Because my input here just use one data variable. P is input, T is target. My data is like the picture until 52 to the right. Thank you so much for the help

    clear;
    clc;
    load DataPelatihan2.mat;

    %%Preprocessing
    P=Data2(:,1:42)';
    T=Data2(:,43:54)';

    [pn,meanp,stdp,tn,meant,stdt] = prestd(P,T);

    %%Menentukan Parameter
    hiddenlayer1max = 13; 
    hiddenlayer2max = 11;
    error = 1; 
    errormax = 0; 
    modmse = 2e2;
    epochs = 1000;

    % Pelatihan hidden layer 1
    for l1=1:hiddenlayer1max;
        if error > errormax ;
        net=newelm(minmax(pn),[l1 12],{'tansig','purelin'}); 

        %parameter pelatihan
        net.trainfcn = 'trainlm';
        net.trainParam.show = 50;
        net.trainParam.lr = 0.1; 
        net.trainParam.epochs = epochs; 
        net.trainParam.goal = errormax; 
        net.trainParam.max_fail = 500;
        net.trainParam.min_grad = 1e-10;
        net.trainParam.mc = 0.5;
        net.performFcn = 'mse';

        %Menampilkan Jumlah Neuron
        disp([' Hidden Layer I, Jumlah Neuron = ' num2str(l1)]);

        %Train 
        [netRec,tr]= train(net, pn, tn); 

        %Simulasi Hasil
        Rec= sim(netRec,pn); 

        %Hitung mse
        error=mse(Rec-tn);

        %error(l1)=error; %cek
        %Ambil Network yg terbaik 
     if modmse > error;
        layerRec=l1;
        modmse=error;
        hl=1;
        save training.mat;
     end
        else
        layer1=11;
        break;
        end
    end 

    % Pelatihan hidden layer 2
    for l2=1:hiddenlayer2max;
        if error > errormax ;
        net=newelm(minmax(pn),[l1 l2 12],{'tansig','tansig','purelin'}); 

        %parameter pelatihan
        net.trainfcn = 'trainlm';
        net.trainParam.show = 50;
        net.trainParam.lr = 0.1; 
        net.trainParam.epochs = epochs; 
        net.trainParam.goal = errormax; 
        net.trainParam.max_fail = 500; 
        net.trainParam.min_grad = 1e-10;
        net.trainParam.mc = 0.5;
        net.performFcn = 'mse';

        %Menampilkan Jumlah Neuron
        disp([' Hidden Layer II, Jumlah Neuron = ' num2str(l2)]);

        %Train 
        [netRec,tr]= train(net, pn, tn); 

        %Simulasi Hasil
        Rec= sim(netRec,pn);

        %Hitung mse
        error=mse(Rec-tn);

        %error(l2)=error; %cek
        %Ambil Network yg terbaik
     if modmse > error;
        layerRec=l2;
        modmse=error;
        hl=2;
        save training.mat;
     end
        else
        layer2=12;
        break;
        end 
    end 

    %%Mengambil network yg terbaik
    load training.mat;
    an= sim(netRec,pn)
    bs= [layerRec modmse]
    a = poststd(an,meant,stdt);
    disp('Network terbaik dengan error terkecil');
    disp(['Hidden Layer ke ' num2str(hl),', Neuron = ' num2str(layerRec),', Error = ' num2str(modmse)]);

    %Regression Diagram
    plotregression(a,T);