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Lampiran 1 Coding program MATLAB lengkap desain model JST clear all; close all; clc; %DATA data=xlsread('D:\ummi\my desertasi\Penelitian\prediksiMKnorm0,1\transformSort.xlsx',1,'J:O' ); input=data(1:288,1:5); output=data(1:288,6); input=input'; output=output'; %INISIALISASI JARINGAN nodehidden1=10; nodehidden2=10; nodehidden3=10; nodehidden4=10; nodehidden5=10; nodeoutput=1; fgsaktivhidden1='tansig'; fgsaktivhidden2='tansig'; fgsaktivhidden3='tansig'; fgsaktivhidden4='tansig'; fgsaktivhidden5='tansig'; fgsaktivoutput='purelin'; fgstrain='trainlm'; epo=1000; goa=1e-4; lrate=0.05; for i=1:3 net_bagi=newff(minmax(input),[nodehidden1,nodehidden2,nodehidden3, nodehidden4,nodehidden5,nodeoutput],{fgsaktivhidden1,fgsaktivhidde n2,fgsaktivhidden3,fgsaktivhidden4,fgsaktivhidden5,fgsaktivoutput} , fgstrain); net_bagi=init(net_bagi);
%PREDIKSI TANPA LATIHAN
disp(i)
disp('prediksi tanpa latihan')
y=sim(net_bagi,input);
[y,Pf,Af,e,perf]=sim(net_bagi,input,[],[],output); mean_square_errornotrain=mse(e)
%PREDIKSI DENGAN LATIHAN
net_bagi.trainParam.epochs=epo; net_bagi.trainParam.goal=goa; net_bagi.trainParam.show=100; net_bagi.trainParam.lr=lrate;
net_bagi.trainParam.mu_max=1e+100; net_bagi=train(net_bagi,input,output);
disp('prediksi dengan latihan')
[ytrain,Pftrain,Aftrain,etrain,perftrain]=sim(net_bagi,input,[],[] ,output); mean_square_errortrain=mse(etrain) [m,b,r]=postreg(ytrain,output); R=r R2=r^2 save net_bagi.mat end %DATA PENGUJIAN inputuji=data(289:360,1:5); outputuji=data(289:360,6); inputuji=inputuji'; outputuji=outputuji';
%HASIL PREDIKSI PENGUJIAN
disp('prediksi dari pengujian')
yuji=sim(net_bagi,inputuji); [yuji,Pfuji,Afuji,euji,perfuji]=sim(net_bagi,inputuji,[],[],output uji) mean_square_erroruji=mse(euji) [muji,buji,ruji]=postreg(yuji,outputuji) %VERIFIKASI %dataver=xlsread('D:\ummi\my desertasi\Penelitian\prediksiMKnorm0,1\transformSort.xlsx',1,'J:O' ); inputver=data(362:373,1:5); outputver=data(362:373,6); inputver=inputver'; outputver=outputver'; %HASIL VERIFIKASI
disp('prediksi dari verifikasi')
yver=sim(net_bagi,inputver);
[yver,Pfver,Afver,ever,perfver]=sim(net_bagi,inputver,[],[],output ver)
Lampiran 2 Coding program MATLAB lengkap integrasi JST dengan sensor dielektrik dan desain GUI
%clear all; %close all; %clc;
function varargout = PrediksiGUI(varargin)
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @PrediksiGUI_OpeningFcn, ...
'gui_OutputFcn', @PrediksiGUI_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1}); end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else
gui_mainfcn(gui_State, varargin{:}); end
% End initialization code - DO NOT EDIT
% --- Executes just before PrediksiGUI is made visible.
function PrediksiGUI_OpeningFcn(hObject, eventdata, handles,
varargin)
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% --- Outputs from this function are returned to the command line.
function varargout = PrediksiGUI_OutputFcn(hObject, eventdata,
handles)
% varargout cell array for returning output args (see VARARGOUT); % hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton5_Callback(hObject, eventdata, handles)
%–membuat sebuah object serial pada matlab dan mengatur setting
sesuai dengan setting
f=serial('COM10','BaudRate',9600,'DataBits',8,'StopBits',1,'InputB ufferSize',600);
%–membuka koneksi object dengan port serial
fopen(f)
%–meminta user untuk memasukkan sejumlah nilai pengambilan data
%SET = input('Tekan "ENTER"');
%–melakukan lopping sejumlah data yang dimasukkan
%while (j<=SET)
data=fread(f,400,'uint8');
%–menutup koneksi dengan serial
fclose(f)
%–menghapus object serial
delete(f);
%–menyimpan data hasil pengukuran
lihat = char(data)'; n=length(data);
if data(1)~= double('A')
k=1;
while(data(k)~=double('A'))
k=k+1; end data=data(k-1:end); end if data(end)~= double('D') k2=length(data); while(data(k2)~=double('D')) k2=k2-1; end data=data(1:k2); end lihat=char(data)'; n = length(data); z=[]; i2=0; j=0; z1=[]; V11=[];V22=[];V33=[]; V1=[];V2=[];V3=[]; for i=1:n
if data(i)== double('A')
V11=[];
while(i2<100)
i2=1+i2;
if i2+i > n || data(i2+i)==double('B'),break,end
V=data(i2+i); V11=[V11 V]; end V=str2double(char(V11)); V1 = [V1 V]; %i=i2+i; i2=0; end
if data(i)== double('B') V11=[];
while(i2<100)
i2=1+i2;
if i2+i > n || data(i2+i)==double('C'),break,end
V=data(i2+i); V11=[V11 V]; end V=str2double(char(V11)); V2 = [V2 V]; %i=i2+i; i2=0; end if data(i)== double('C') V11=[]; while(i2<100) i2=1+i2;
if i2+i > n || data(i2+i)==double('D'),break,end
V=data(i2+i); V11=[V11 V]; end V=str2double(char(V11)); V3 = [V3 V]; %i=i2+i; i2=0; end end % V3 = Vin % V2 = Vfrek % V1 = Vsen V = [V1' V2' V3'].*4.93./1023; V = mean(V); %V(1,3)=V(1,3)-1.2 Vin = (202e3+198e3)/202e3*V(3); f = sqrt( (Vin/V(2)).^2 - 1 ) ./ (2*pi*1.977e3*10.5e-9); VCnoninv = V(1)./(1+100.4e3./46.5e3); VClow = VCnoninv.*sqrt(1+(2*pi()*f*100e-9*559)^2); VChight = VClow.*sqrt(1+(2*pi()*f*100e- 9*253)^2)./(2*pi()*f*100e-9*253); C = sqrt( (Vin/VChight).^2 - 1 ) ./ (2*pi*0.998e6*f); Xc = 1./(2*pi*f*C); G =1./Xc; A = 4e-4;d = 0.25e-2;Eo=8.85e-12; k = C*d/Eo/A; set(handles.text11,'string',f); set(handles.text8,'string',C); set(handles.text21,'string',k);
set(handles.uipanel2,'Visible','on')
% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close;
% --- Executes on selection change in popupmenu1.
function popupmenu1_Callback(hObject, eventdata, handles)
indeks=get(handles.popupmenu1,'value');
handles.indeks=indeks; guidata(hObject,handles)
% --- Executes during object creation, after setting all properties.
function popupmenu1_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'),
get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on selection change in popupmenu2.
function popupmenu2_Callback(hObject, eventdata, handles)
indeks2=get(handles.popupmenu1,'value');
handles.indeks2=indeks2; guidata(hObject,handles)
% --- Executes during object creation, after setting all properties.
function popupmenu2_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'),
get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton6.
function pushbutton6_Callback(hObject, eventdata, handles)
load net_bagi.mat; indeks=get(handles.popupmenu1,'value'); indeks2=get(handles.popupmenu2,'value'); fr=str2num(get(handles.text11,'string')); cp=str2num(get(handles.text8,'string')); kd=str2num(get(handles.text21,'string'));
jkemasan=indeks; jbiskuit=indeks2; frek=(0.8*(fr-5840.3))/(6246.3-5840.3)+0.1; Cp=(0.8*(cp - (1.2075e-11)))/((2.9129e-11) - (1.2075e-11))+0.1; kdielektrik=(0.8*(kd-8.5279))/(20.5713-8.5279)+0.1; jkmsan=(0.8*(jkemasan-1))/(3-1)+0.1; jbisk=(0.8*(jbiskuit-1))/(4-1)+0.1;
matrikspred=[jkmsan jbisk frek Cp kdielektrik]; matrikspred=matrikspred';
ypred=sim(net,matrikspred);
hasilprediksi=(0.8*(-177)+((ypred-0.1)*(517-(-177))))/0.8;
Lampiran 3 Petunjuk penggunaan sistem real time cerdas prediksi masa kadaluarsa biskuit (user manual)
Untuk menggunakan sistem real time cerdas prediksi masa kadaluarsa biskuit, ikuti tahapan berikut :
1. Sensor dihubungkan dengan sumber listrik. 2. Hubungkan mikrokontroler dengan komputer.
3. Jalankan program prediksi dengan menekan dua kali file PrediksiGUI.fig yang terdapat pada folder C:/PrediksiMK. Sehingga muncul tampilan utama.
4. Selipkan sampel pada probe. Untuk biskuit yang diameternya lebih besar dari diameter probe, perlu ditipiskan sehingga dapat disisipkan pada probe.
5. Klik „UKUR‟ pada frame „Pengukuran‟ untuk mengukur beberapa nilai inputan frekuensi, kapasitansi dan konstanta dielektrik. Akan terlihat hasil pengukuran frekuensi, kapasitansi dan konstanta dielektrik pada text box.
6. Isi „Jenis kemasan‟ dan „Jenis biskuit‟ pada frame „Pengisisan data‟ dengan memilih item pada masing-masing input box. Pilihan Jenis kemasan yang tersedia adalah Kaleng, Alumunium foil, dan Plastik. Pilihan Jenis biskuit yang tersedia adalah Wafer, Cookies, Crackers, dan Biskuit keras.
7. Klik „PREDIKSI‟ pada frame „Keluaran‟ untuk menunjukkan hasil prediksi masa kadaluarsa biskuit tersebut dalam hari. 8. Jika akan mengukur sampel yang lain, dapat diulangi langkah 4
sampai 7. Jika telah selesai, dapat meng-klik „KELUAR‟
Mulai