So you just want to translate the pseudo code above into working code and choose how to best plot it. The pseudo code probably does not do what you want, seeing the result, and needs some troubleshooting (see comments in code). In any case, it wouldn't be a confusion matrix if elements are allowed in more than one cell and the sum is more than the total. But here it is:
confdat = xlsread(websave('rd', "https://nl.mathworks.com/matlabcentral/answers/uploaded_files/1372189/Confusion.xlsx"));
T_out = confdat(:,1); % I had assumed Predicted Value to be the first column since that's how you ordered them
P_out = confdat(:,2); % then I actually checked the xls file
maxTou = 1.1.*T_out;
minTou = 0.9.*T_out;
Tr_Pos = (P_out >= T_out) & (P_out <= maxTou);
Tr_Neg = (P_out <= T_out) & (P_out >= minTou); % all 0s (try switching maxTou/minTou here and line above)
Fa_Pos = (P_out > maxTou); % all 1s here, that's what I meant with "probably does not do what you want"
Fa_Neg = (P_out < maxTou); % see above, I'll assume you meant False Negative here (maybe minTou)
conmat = [sum(Tr_Pos) sum(Fa_Pos); sum(Fa_Neg) sum(Tr_Neg)];
cm = confusionchart(conmat); %this is one way to plot, convenient
cm.Title = 'Confusion ± 10% tolerance';
cm.RowSummary = 'row-normalized'; %adds percentage rows (or % cm.ColumnSummary = 'column-normalized'; columns),% you can omit this
