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D3TI-10_Prediksi_Saham
Commit 741bb4e5 by Rizky Sianturi
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ForecastingData.m 0 → 100644
function varargout = ForecastingData(varargin)
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', 'PrediksiSaham', ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @ForecastingData_OpeningFcn, ...
'gui_OutputFcn', @ForecastingData_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 ForecastingData is made visible.
function ForecastingData_OpeningFcn(hObject, eventdata, handles, varargin)
% Choose default command line output for ForecastingData
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% --- Outputs from this function are returned to the command line.
function varargout = ForecastingData_OutputFcn(hObject, eventdata, handles)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushButtonPrediksi.
function pushButtonPrediksi_allback(hObject, eventdata, handles)
% --- Executes on button press in pushButtonHasil.
function pushButtonHasil_Callback(hObject, eventdata, handles)
% --- Executes on button press in pushButtonLoadData.
function pushButtonLoadData_Callback(hObject, eventdata, handles)
try
global lenTrain;
global lenTest;
global tableStatus;
global dataExcel;
global hargaSahamTxt;
global dataTabel;
global target;
global crossOverName;
global er;
crossOverName = 'double';
er = 0.01;
% persiapan data train dan data test
% baca file excel dan txt
[hargaSahamXls,path] = uigetfile('.xlsx');
% buka data excel
[dataExcel] = xlsread(fullfile(path,hargaSahamXls));
data_size = length(dataExcel);
lenTrain = 5;
lenTest = data_size-lenTrain;
cla
axes(handles.plotDataSaham)
plot(dataExcel);
grid on
grid minor
legend('Harga Saham');
xlabel('hari ke-');
ylabel('Rp');
catch
msgbox({'Tipe data tidak valid';'Mohon pilih filfe yang sesuai (xls/xlsx)'});
end
%kode program untuk membuat tabel data
try
k = 1;
for i=1:152
for j=1:5
dataTabel(j,i) = dataExcel(k);
k = k+1;
end
end
catch
msgbox('Gagal memuat data');
end
try
%% tulis data saham dari excel ke txt
j = data_size;
[hargaSahamTxt] = ('Harga Saham\data_saham.txt');
fid = fopen(hargaSahamTxt, 'w');
a = 1;
b = 762;
for n=lenTest:-1:1
col = 1;
Y(a) = dataExcel(b);
fprintf(fid, '%d\t', Y(a));
for m=j-1:-1:n
X(col) = dataExcel(m);
fprintf(fid, '%d\t', X(col));
col = col+1;
end
for m=j-1:-1:n
X(col) = dataExcel(m)^2;
fprintf(fid, '%d\t', X(col));
col = col+1;
end
j = j-1;
a = a+1;
b = b-1;
fprintf(fid,'\n');
end
fclose(fid);
target = dlmread(hargaSahamTxt,'',0,0);
catch
msgbox({'Gagal mengonversi data'; 'dari (.xls/.xlsx) ke (.txt)'});
end
% --- Executes on button press in buttonProcessForecasting.
function buttonProcessForecasting_Callback(hObject, eventdata, handles)
try
global target;
global popSize;
global crossOverRate;
global mutationRate;
global iteration;
global BestChrom;
global er;
global uji_prediksi;
global RMSE;
global data_saham_aktual;
global dataUjiPrediksi;
global size;
global prediksiHasil;
global bestValueTxt;
global delimiter;
global formatSpec;
global dataArrayGA;
global bestKromosom;
popSize = 100;
crossOverRate = 0.9;
mutationRate = 0.1;
iteration = 1000;
delimiter = '\t';
formatSpec = '%s%f%f%[^\n\r]';
bestValueTxt = ('Harga Saham\data_tabel_prediksi.txt');
data_saham_aktual = target(1:5);
setappdata(0,'data_saham_aktual',data_saham_aktual);
er = 0.01;
tic
[BestChrom] = GA(target, popSize, crossOverRate, mutationRate, iteration, er);
disp('The best fitness kromosom: ')
BestChrom.kromosom.fitness
disp('The best error value: ')
RMSE = BestChrom.kromosom.fitness
toc
t = toc
catch
msgbox({'Data yang dimasukkan tidak lengkap';'Mohon lengkapi data untuk prediksi'});
end
try
global root_mean_square_error;
uji_prediksi = pengujian_data(BestChrom.kromosom.gen, RMSE, target);
setappdata(0,'uji_prediksi',uji_prediksi);
root_mean_square_error = BestChrom.kromosom.fitness;
setappdata(0,'root_mean_square_error',root_mean_square_error);
size = 5;
dataUjiPrediksi = ('Harga Saham\data_uji_prediksi.txt');
prediksiHasil = prediksi_data(size, target, dataUjiPrediksi, BestChrom);
if (prediksiHasil <= -prediksiHasil)
prediksiHasil = (prediksiHasil*(-1));
end
setappdata(0,'prediksiHasil',prediksiHasil);
fileID = fopen(bestValueTxt, 'r');
dataArrayGA = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'ReturnOnError', false);
fclose(fileID);
setappdata(0,'dataArrayGA',dataArrayGA);
bestKromosom = BestChrom.kromosom.gen
setappdata(0,'bestKromosom',bestKromosom);
msgbox({'Proses Algoritma Genetika telah selesai';'Silakan tekan tombol Lihat Hasil';'untuk melihat hasilnya'});
catch %%exception
%msgText = getReport (exception)
msgbox('Proses persiapan data lanjutan tidak berhasil');
end
% proses GA
function [BestKromosom] = GA( target, popSize, cr_rate, mt_rate, max_generation, er)
try
global rowTarget;
global colTarget;
% menginisialisasi populasi
[rowTarget, colTarget] = size(target);
g = 1;
disp(['Generation #' , num2str(g)]);
yAksen = zeros;
m = 1;
while m <= popSize
% menginisialisasi populasi
[population.kromosom(m).gen, population.kromosom(m).koefisien] = initialize_population( popSize, colTarget );
% menghitung nilai fitness
[population.kromosom(m).fitness, yAksen(m)] = calculate_fitness( target, population.kromosom(m).gen, population.kromosom(m).koefisien );
% pengecekan apakah anda nilai regresi yang negatif
neg = any(yAksen(m)<0);
if (neg) == 1
m = m-1;
end
m = m+1;
end
% urutkan fitness -> elitism
[max_val , indx] = sort([ population.kromosom(:).fitness ] , 'descend');
%% main loop
for g = 2 : max_generation
disp(['Generation @#' , num2str(g)]);
for k = 1: 2: popSize
% tournament selection
% size(population.kromosom(:)) -> panjang kromosom
% (chromosome length)
% 'selection'
[parent1,parent2] = selection(population);
% reproduksi
% cross over
% 'cross over'
[child1 , child2] = crossover(parent1 , parent2, cr_rate);
% mutation
% 'mutasi'
[child1] = mutation(child1, mt_rate);
[child2] = mutation(child2, mt_rate);
newPopulation.kromosom(k) = child1;
newPopulation.kromosom(k+1) = child2;
end
for i = 1 : popSize
newPopulation.kromosom(i).koefisien;
newPopulation.kromosom(i).fitness = calculate_fitness(target, newPopulation.kromosom(i).gen, newPopulation.kromosom(i).koefisien);
end
% regeneration
[ newPopulation ] = regeneration(population, newPopulation, er);
population = newPopulation;
end
BestKromosom.kromosom.gen = population(1).kromosom.gen;
BestKromosom.kromosom.koefisien = population(1).kromosom.koefisien;
BestKromosom.kromosom.fitness = population(1).kromosom.fitness;
catch
msgbox('Proses GA tidak berhasil');
end
function [ gen, koefisien ] = initialize_population( ukuran_populasi, panjang_kromosom )
try
for j = 1 : panjang_kromosom
gen(j) = randi([0 1]);
koefisien(j) = -1 + (2) .* rand(1,1);
end
catch
msgbox('Proses inisialisasi populasi gagal');
end
%PROSES SELEKSI PAKAI TOURNAMENT
% this is tournament selection
function [parent1, parent2] = selection(population)
try
global popSize;
% ukuran turnament pool
tournament_size = 4;
% [sorted_fitness_values , sorted_kromosom] = sort(normalized_fitness , 'descend');
for i=1:2
for j=1:tournament_size
counter = randi ([1 popSize]);
temp_population.kromosom(j) = population.kromosom(counter);
end
[max_val , indx] = sort([ temp_population.kromosom(:).fitness ] , 'descend');
parent(i) = temp_population.kromosom(1);
end
parent1 = parent(1);
parent2 = parent(2);
catch
msgbox('Proses seleksi gagal');
end
%PROSES SELEKSI Nilai Fitness
% this is fitness selection
function [pop] = selectionFitness(population)
try
% Inisialisasi temporeri populasi
temppop = population;
% Mengurutkan temppop berdasarkan nilai fitness tertinggi
[max_val , indx] = sort([ temppop.kromosom(:).fitness ] , 'descend');
% Menyimpan 50 nilai fitness tertinggi
for i=1:50
temp_population.kromosom(i) = temppop.kromosom(i);
end
% Mengembalikan nilai populasi
pop = temp_population;
catch
msgbox('Proses seleksi fitness gagal');
end
% --- Executes on button press in buttonProcessForecastingAGA.
function buttonProcessForecastingAGA_Callback(hObject, eventdata, handles)
try
global target;
global popSize;
global crossOverRate;
global mutationRate;
global iteration;
global BestChrom;
global er;
global uji_prediksi;
global RMSE;
global data_saham_aktual;
global dataUjiPrediksi;
global size;
global prediksiHasil;
global bestValueTxt;
global delimiter;
global formatSpec;
global dataArrayAGA;
global bestKromosom;
popSize = 100;
crossOverRate = 0.9;
mutationRate = 0.013;
iteration = 1000;
delimiter = '\t';
formatSpec = '%s%f%f%[^\n\r]';
bestValueTxt = ('Harga Saham\data_tabel_prediksi.txt');
data_saham_aktual = target(1:5);
setappdata(0,'data_saham_aktual',data_saham_aktual);
er = 0.01;
tic
[BestChrom] = AGA(target, popSize, crossOverRate, mutationRate, iteration, er);
disp('The best fitness kromosom: ')
BestChrom.kromosom.fitness
disp('The best error value: ')
RMSE = BestChrom.kromosom.fitness
toc
t = toc
catch
msgbox({'Data yang dimasukkan tidak lengkap';'Mohon lengkapi data untuk prediksi'});
end
try
global root_mean_square_error_AGA;
uji_prediksi = pengujian_data(BestChrom.kromosom.gen, RMSE, target);
setappdata(0,'uji_prediksi',uji_prediksi);
root_mean_square_error_AGA = BestChrom.kromosom.fitness;
setappdata(0,'root_mean_square_error_AGA',root_mean_square_error_AGA);
size = 5;
dataUjiPrediksi = ('Harga Saham\data_uji_prediksi.txt');
prediksiHasil = prediksi_data(size, target, dataUjiPrediksi, BestChrom);
if (prediksiHasil <= -prediksiHasil)
prediksiHasil = (prediksiHasil*(-1));
end
setappdata(0,'prediksiHasil',prediksiHasil);
fileID = fopen(bestValueTxt, 'r');
dataArrayAGA = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'ReturnOnError', false);
fclose(fileID);
setappdata(0,'dataArrayAGA',dataArrayAGA);
bestKromosom = BestChrom.kromosom.gen
setappdata(0,'bestKromosom',bestKromosom);
msgbox({'Proses Algoritma Genetika telah selesai';'Silakan tekan tombol Lihat Hasil';'untuk melihat hasilnya'});
catch %%exception
%msgText = getReport (exception)
msgbox('Proses persiapan data lanjutan tidak berhasil');
end
% proses AGA
function [BestKromosom] = AGA( target, popSize, cr_rate, mt_rate, max_generation, er)
try
global rowTarget;
global colTarget;
% menginisialisasi populasi
[rowTarget, colTarget] = size(target);
g = 1;
disp(['Generation #' , num2str(g)]);
yAksen = zeros;
m = 1;
while m <= popSize
% menginisialisasi populasi
[population.kromosom(m).gen, population.kromosom(m).koefisien] = initialize_population( popSize, colTarget );
% menghitung nilai fitness
[population.kromosom(m).fitness, yAksen(m)] = calculate_fitness( target, population.kromosom(m).gen, population.kromosom(m).koefisien );
% pengecekan apakah anda nilai regresi yang negatif
neg = any(yAksen(m)<0);
if (neg) == 1
m = m-1;
end
m = m+1;
end
% urutkan fitness -> elitism
[max_val , indx] = sort([ population.kromosom(:).fitness ] , 'descend');
%% main loop
for g = 2 : max_generation
disp(['Generation #' , num2str(g)]);
for k = 1: 2: popSize
% tournament selection
% size(population.kromosom(:)) -> panjang kromosom
% (chromosome length)
% 'selection'
% Seleksi populasi dengan 50 nilai fitness tertingii
[popFitness] = selectionFitness(population);
% Seleksi parent dengan menggunakan PSO
[parent1,parent2] = selectionPSO(popFitness);
% reproduksi
% cross over
% 'cross over'
[child1 , child2] = crossover(parent1 , parent2, cr_rate);
% mutation
% 'mutasi'
[child1] = mutation(child1, mt_rate);
[child2] = mutation(child2, mt_rate);
newPopulation.kromosom(k) = child1;
newPopulation.kromosom(k+1) = child2;
end
for i = 1 : popSize
newPopulation.kromosom(i).koefisien;
newPopulation.kromosom(i).fitness = calculate_fitness(target, newPopulation.kromosom(i).gen, newPopulation.kromosom(i).koefisien);
end
% regeneration
[ newPopulation ] = regeneration(population, newPopulation, er);
population = newPopulation;
end
BestKromosom.kromosom.gen = population(1).kromosom.gen;
BestKromosom.kromosom.koefisien = population(1).kromosom.koefisien;
BestKromosom.kromosom.fitness = population(1).kromosom.fitness;
catch
msgbox('Proses GA tidak berhasil');
end
% PROSES PERHITUNGAN NILAI FITNESS
function [fitness,regresi] = calculate_fitness( dataset, populasi, koefisien )
try
global rowDataset;
global colDataset;
[rowDataset, colDataset] = size(dataset);
% nilai awal sumx di-state sebagai array pertama chromosome
sumx = zeros;
for i=1:rowDataset
sumx(i) = populasi(1) * koefisien(1);
for j=2:colDataset
sumx(i) = sumx(i) + (populasi(j) * koefisien(j) * dataset(i,j));
end
end
regresi = sum(sumx);
square = 0;
jumlah = 0;
for i=1:rowDataset
square = power((dataset(i,1)-sumx(i)),2);
jumlah = jumlah + square;
end
error = sqrt(jumlah)/rowDataset;
fitness = 1/error;
catch
msgbox('Proses penghitungan nilai fitness gagal');
end
%PROSES SELEKSI PAKAI PSO
% this is tournament selection
function [parent1, parent2] = selectionPSO(population)
try
% Jumlah populasi
global size;
size = 50;
% Nilai batasan posisi
Boundary=[1,50];
% Initialize Constants
weight=0.5;
constant1=2;
constant2=2;
% Jumlah seleksi yang diinginkan
nOfSelection=2;
% Jumlah iterasi
itrationMax=50;
% Fungsi nilai rata-rata
fun=@meandata;
% Initialize Pariticles
Positions=zeros(size,nOfSelection);
for i=1:size
tempVar = randperm(Boundary(2));
Positions(i,:) = tempVar(1:nOfSelection);
end
% velocity initialization
Velocity = ones(size,nOfSelection);
% Initialize Global best
itration = 1;
globalBest.value(itration) = inf;
%% Optimizing
Particles = struct;
while(1)
% Calculating Fitness Values
for i=1:size
% Inisialisasi posisi partikel
Particles(itration).position(i,:) = Positions(i,:);
% Membuat array berdasarkan nilai fitness pada posisi tertentu
% Cth: nilai posisi 3 sehingga nilai fitness yang dipakai
% adalah kromosom nomer 3
fit = [population.kromosom(Positions(i,1)).fitness population.kromosom(Positions(i,2)).fitness];
% Merata-rata nilai fitness untuk dijadikan nilai partikel
Particles(itration).value(i) = fun(fit);
end
% Update Position Best
[Particlesbest.value(itration),ind] = min(Particles(itration).value);
Particlesbest.position(itration,:) = Particles(itration).position(ind,:);
% Update Global Best
if globalBest.value(itration) > Particlesbest.value(itration)
[globalBest.value(itration),globalBest.value(itration+1)] = deal(Particlesbest.value(itration));
globalBest.position(itration,:) = Positions(ind(1),:);
else
globalBest.value(itration+1) = globalBest.value(itration);
globalBest.position(itration,:) = globalBest.position(itration-1,:);
end
disp('Iteration---->')
disp(itration)
disp('gbest value of fitness=---->')
disp(globalBest.value(itration))
% Velocity Update\
Velocity = (weight*Velocity) + (constant1*rand(1)*(repmat(Particlesbest.position(itration,:),size,1)-Positions)) + ...
(constant2*rand(1)*(repmat(globalBest.position(itration,:),size,1)-Positions));
% Postion Update
Positions = Positions+round(Velocity);
% Boundary Checking for Position
Positions(Positions>Boundary(2)) = round(rand(1)*(Boundary(2)-1))+1;
Positions(Positions<Boundary(1)) = round(rand(1)*(Boundary(2)-1))+1;
for i=1:size
if length(unique( Positions(i,:) )) ~= nOfSelection
tempVar = randperm(Boundary(2));
Positions(i,:) = tempVar(1:nOfSelection);
end
end
% Loop Breaking is Positions all are same or itertion acheived maximum
% itertion
count=0;
for i=1:size-1;
if Positions(i,:) == Positions(i+1,:)
count = count+1;
end
end
if (count == size-1) || (itration >= itrationMax)
fprintf('\n******Iteration completed******\n')
break
end
itration = itration+1;
end
Selection=globalBest.position(itration,:);
%SelectionValue=globalBest.value(itration);
disp('Selection---->')
disp(Selection)
parent1 = population.kromosom(Selection(1));
parent2 = population.kromosom(Selection(2));
catch
msgbox('Proses seleksi PSO gagal');
end
% proses crossover
function [child1,child2] = crossover(parent1,parent2, cr_rate)
try
global colTarget;
child1 = parent1;
child2 = parent2;
ub = colTarget - 1;
lb = 1;
Cross_P1 = abs(round ( (ub - lb) *rand() + lb ));
Cross_P2 = Cross_P1;
while Cross_P2 == Cross_P1
Cross_P2 = abs(round ( (ub - lb) *rand() + lb ));
end
if Cross_P1 > Cross_P2
temp = Cross_P1;
Cross_P1 = Cross_P2;
Cross_P2 = temp;
end
Part1 = parent1.gen(1:Cross_P1);
Part2 = parent2.gen(Cross_P1 + 1 :Cross_P2);
Part3 = parent1.gen(Cross_P2+1:end);
child1.gen = [Part1 , Part2 , Part3];
Part1 = parent2.gen(1:Cross_P1);
Part2 = parent1.gen(Cross_P1 + 1 :Cross_P2);
Part3 = parent2.gen(Cross_P2+1:end);
child2.gen = [Part1 , Part2 , Part3];
R1 = rand();
if R1 <= cr_rate
child1 = child1;
else
child1 = parent1;
end
R2 = rand();
if R2 <= cr_rate
child2 = child2;
else
child2 = parent2;
end
catch %exception
%msgReport (exception)
msgbox('Proses cross over gagal');
end
% proses mutasi
function [child] = mutation(child,mutation_rate)
try
global colTarget;
for k = 1: colTarget
R = rand();
if R < mutation_rate
child.gen(k) = ~ child.gen(k);
end
end
catch
msgbox('Proses mutasi gagal');
end
% proses regenerasi
function [ newPopulation2 ] = regeneration(population , newPopulation, Er)
try
global popSize;
Elite_no = round(popSize * Er);
[max_val , indx] = sort([ population.kromosom(:).fitness ] , 'descend');
% The elites from the previous population
for k = 1 : Elite_no
newPopulation2.kromosom(k).gen = population.kromosom(indx(k)).gen;
newPopulation2.kromosom(k).koefisien = population.kromosom(indx(k)).koefisien;
newPopulation2.kromosom(k).fitness = population.kromosom(indx(k)).fitness;
end
% The rest from the new population
for k = Elite_no + 1 : popSize
newPopulation2.kromosom(k).gen = newPopulation.kromosom(k).gen;
newPopulation2.kromosom(k).koefisien = newPopulation.kromosom(k).koefisien;
newPopulation2.kromosom(k).fitness = newPopulation.kromosom(k).fitness;
end
catch
msgbox('Proses regenerasi gagal');
end
% proses evaluasi
function islooping = evaluation(population)
try
global popSize;
for i=1:popSize
if population(i).fitness > 100
islooping = false;
else
islooping = true;
end
end
catch
msgbox('Proses evaluasi gagal');
end
%PERBANDINGAN DATA
function [ data_uji ] = pengujian_data( kromosom, error, target )
% Y^2 - 2Y'Y + (Y'^2-RMSE^2) = 0
% a=1 ; b=-2Y' ; c=(Y'^2-RMSE^2)
% Y'~>regresi <=> kromosom*target
% RMSE=error
% rumus mencari akar-akar dari persamaan kuadrat dengan rumus a,b,c
% (x1,x2) = [(-b)<+/->(square(b^2-4*a*c))]/(2*a)
global rowDataset;
global colDataset;
for i=1:rowDataset
regresi(i) = kromosom(1);
for j=2:colDataset
regresi(i) = regresi(i) + (kromosom(j)*target(i,j));
end
data_uji(i) = regresi(i)+error;
end
data_uji;
% --- Executes on button press in buttonLihatHasil.
function buttonLihatHasil_Callback(hObject, eventdata, handles)
global data_saham_aktual;
global uji_prediksi;
global data_prediksi_saham;
data_prediksi_saham = getappdata(0, 'prediksiHasil')
data_saham_aktual = getappdata(0,'data_saham_aktual');
uji_prediksi = getappdata(0,'uji_prediksi');
axes(handles.plotPerbandingan);
plot(1:size(data_prediksi_saham,2), data_prediksi_saham,'b');
%%plot(1:size(data_saham_aktual,2), data_saham_aktual,'g');
%%plot(1:size(data_saham_aktual), flip(data_saham_aktual),'g',1:size(data_prediksi_saham,2),flip(data_prediksi_saham),'r');
grid on
grid minor
legend('Data Prediksi','asdsad')
xlabel('hari ke-');
ylabel('Rp');
handles.textGantiPerbandinganPrediksi.String = {'Perbandingan Data Aktual dan Data Prediksi Harga Saham';'pada tahun 2016-2019'};
global root_mean_square_error;
global root_mean_square_error_AGA;
root_mean_square_error = getappdata(0,'root_mean_square_error');
root_mean_square_error_AGA = getappdata(0,'root_mean_square_error_AGA');
set(handles.text7,'String',root_mean_square_error);
set(handles.text8,'String',root_mean_square_error_AGA);
set(handles.uitable1,'data',round(data_prediksi_saham),'ColumnName',{'Hari 1','Hari 2','Hari 3','Hari 4','Hari 5'});
%DATA PREDIKSI
function [dataPrediksi] = prediksi_data(size, target, dataUjiPrediksi, BestChrom)
try
global counter;
global errorPredict;
errorPredict=100;
counter = length(target);
fid2 = fopen(dataUjiPrediksi, 'w');
for prepare=1:5
dataPreparation(prepare) = target(counter);
counter = counter - 1;
end
for predict=1:size
for kolom1=1:size
preX(predict,kolom1) = dataPreparation(kolom1);
fprintf(fid2, '%d\t', preX(predict,kolom1));
end
for kolom2=(size+1):(2*size)
preX(predict,kolom2) = dataPreparation(kolom2-5);
fprintf(fid2, '%d\t', preX(predict,kolom2).^2);
end
fprintf(fid2, '\n');
temp_dataPreparation = dataPreparation;
% regresiData = randi([0,100],1,1);
[~,regresiData] = calculate_fitness(temp_dataPreparation, BestChrom.kromosom.gen, BestChrom.kromosom.koefisien);
dataPreparation(1) = regresiData+errorPredict;
for swap=2:5
dataPreparation(swap) = temp_dataPreparation(swap-1);
end
dataPrediksi = abs(dataPreparation(end:-1:1));
end
fclose(fid2);
catch
msgbox('Proses prediksi data gagal');
end
function simpanButton_Callback(hObject, eventdata, handles)
try
% hObject handle to savebutton (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global data_prediksi_saham;
data_prediksi_saham = getappdata(0, 'prediksiHasil')
[excelName,excelPath] = uiputfile('*.xlsx')
headers = {'Hari 1','Hari 2','Hari 3','Hari 4','Hari 5'};
data = get(handles.uitable1, 'data');
datacell = num2cell(data);
a = [headers;datacell];
x1Range = 'Prediksi Saham'
xlswrite([excelPath excelName],a,x1Range)
catch exception
msgText(exception)
%msgbox('Proses prediksi data gagal');
end
% --- Executes during object creation, after setting all properties.
function panelGA_CreateFcn(hObject, eventdata, handles)
set(handles.panelGA, 'visible', 'off');
% --- Executes during object creation, after setting all properties.
function panelAGA_CreateFcn(hObject, eventdata, handles)
set(handles.panelAGA, 'visible', 'off');
Harga Saham/data_saham.txt 0 → 100644
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67500 67500 67500 67500 66000 66000 4556250000 4556250000 4556250000 4356000000 4356000000
67500 67500 67500 66000 66000 67150 4556250000 4556250000 4356000000 4356000000 4509122500
67500 67500 66000 66000 67150 67000 4556250000 4356000000 4356000000 4509122500 4489000000
67500 66000 66000 67150 67000 66575 4356000000 4356000000 4509122500 4489000000 4432230625
66000 66000 67150 67000 66575 65900 4356000000 4509122500 4489000000 4432230625 4342810000
66000 67150 67000 66575 65900 65200 4509122500 4489000000 4432230625 4342810000 4251040000
67150 67000 66575 65900 65200 65300 4489000000 4432230625 4342810000 4251040000 4264090000
67000 66575 65900 65200 65300 62800 4432230625 4342810000 4251040000 4264090000 3943840000
66575 65900 65200 65300 62800 61000 4342810000 4251040000 4264090000 3943840000 3721000000
65900 65200 65300 62800 61000 59300 4251040000 4264090000 3943840000 3721000000 3516490000
65200 65300 62800 61000 59300 60625 4264090000 3943840000 3721000000 3516490000 3675390625
65300 62800 61000 59300 60625 61150 3943840000 3721000000 3516490000 3675390625 3739322500
62800 61000 59300 60625 61150 59300 3721000000 3516490000 3675390625 3739322500 3516490000
61000 59300 60625 61150 59300 60800 3516490000 3675390625 3739322500 3516490000 3696640000
59300 60625 61150 59300 60800 61000 3675390625 3739322500 3516490000 3696640000 3721000000
60625 61150 59300 60800 61000 62500 3739322500 3516490000 3696640000 3721000000 3906250000
61150 59300 60800 61000 62500 63000 3516490000 3696640000 3721000000 3906250000 3969000000
59300 60800 61000 62500 63000 65600 3696640000 3721000000 3906250000 3969000000 4303360000
60800 61000 62500 63000 65600 65575 3721000000 3906250000 3969000000 4303360000 4300080625
61000 62500 63000 65600 65575 65575 3906250000 3969000000 4303360000 4300080625 4300080625
62500 63000 65600 65575 65575 65575 3969000000 4303360000 4300080625 4300080625 4300080625
63000 65600 65575 65575 65575 63150 4303360000 4300080625 4300080625 4300080625 3987922500
65600 65575 65575 65575 63150 65025 4300080625 4300080625 4300080625 3987922500 4228250625
65575 65575 65575 63150 65025 66200 4300080625 4300080625 3987922500 4228250625 4382440000
65575 65575 63150 65025 66200 66000 4300080625 3987922500 4228250625 4382440000 4356000000
65575 63150 65025 66200 66000 64550 3987922500 4228250625 4382440000 4356000000 4166702500
63150 65025 66200 66000 64550 63175 4228250625 4382440000 4356000000 4166702500 3991080625
65025 66200 66000 64550 63175 63700 4382440000 4356000000 4166702500 3991080625 4057690000
66200 66000 64550 63175 63700 63000 4356000000 4166702500 3991080625 4057690000 3969000000
66000 64550 63175 63700 63000 61800 4166702500 3991080625 4057690000 3969000000 3819240000
64550 63175 63700 63000 61800 65500 3991080625 4057690000 3969000000 3819240000 4290250000
63175 63700 63000 61800 65500 66100 4057690000 3969000000 3819240000 4290250000 4369210000
63700 63000 61800 65500 66100 65400 3969000000 3819240000 4290250000 4369210000 4277160000
63000 61800 65500 66100 65400 64125 3819240000 4290250000 4369210000 4277160000 4112015625
61800 65500 66100 65400 64125 63500 4290250000 4369210000 4277160000 4112015625 4032250000
65500 66100 65400 64125 63500 63475 4369210000 4277160000 4112015625 4032250000 4029075625
66100 65400 64125 63500 63475 61500 4277160000 4112015625 4032250000 4029075625 3782250000
65400 64125 63500 63475 61500 61500 4112015625 4032250000 4029075625 3782250000 3782250000
64125 63500 63475 61500 61500 60700 4032250000 4029075625 3782250000 3782250000 3684490000
63500 63475 61500 61500 60700 61000 4029075625 3782250000 3782250000 3684490000 3721000000
63475 61500 61500 60700 61000 61500 3782250000 3782250000 3684490000 3721000000 3782250000
61500 61500 60700 61000 61500 60600 3782250000 3684490000 3721000000 3782250000 3672360000
61500 60700 61000 61500 60600 60200 3684490000 3721000000 3782250000 3672360000 3624040000
60700 61000 61500 60600 60200 59900 3721000000 3782250000 3672360000 3624040000 3588010000
61000 61500 60600 60200 59900 58250 3782250000 3672360000 3624040000 3588010000 3393062500
61500 60600 60200 59900 58250 57900 3672360000 3624040000 3588010000 3393062500 3352410000
60600 60200 59900 58250 57900 58025 3624040000 3588010000 3393062500 3352410000 3366900625
60200 59900 58250 57900 58025 58350 3588010000 3393062500 3352410000 3366900625 3404722500
59900 58250 57900 58025 58350 58000 3393062500 3352410000 3366900625 3404722500 3364000000
58250 57900 58025 58350 58000 58800 3352410000 3366900625 3404722500 3364000000 3457440000
57900 58025 58350 58000 58800 55875 3366900625 3404722500 3364000000 3457440000 3122015625
58025 58350 58000 58800 55875 54975 3404722500 3364000000 3457440000 3122015625 3022250625
58350 58000 58800 55875 54975 55250 3364000000 3457440000 3122015625 3022250625 3052562500
58000 58800 55875 54975 55250 55400 3457440000 3122015625 3022250625 3052562500 3069160000
58800 55875 54975 55250 55400 55500 3122015625 3022250625 3052562500 3069160000 3080250000
55875 54975 55250 55400 55500 55000 3022250625 3052562500 3069160000 3080250000 3025000000
54975 55250 55400 55500 55000 55500 3052562500 3069160000 3080250000 3025000000 3080250000
55250 55400 55500 55000 55500 56300 3069160000 3080250000 3025000000 3080250000 3169690000
55400 55500 55000 55500 56300 55800 3080250000 3025000000 3080250000 3169690000 3113640000
55500 55000 55500 56300 55800 55900 3025000000 3080250000 3169690000 3113640000 3124810000
55000 55500 56300 55800 55900 56000 3080250000 3169690000 3113640000 3124810000 3136000000
55500 56300 55800 55900 56000 54500 3169690000 3113640000 3124810000 3136000000 2970250000
56300 55800 55900 56000 54500 55100 3113640000 3124810000 3136000000 2970250000 3036010000
55800 55900 56000 54500 55100 54025 3124810000 3136000000 2970250000 3036010000 2918700625
55900 56000 54500 55100 54025 54750 3136000000 2970250000 3036010000 2918700625 2997562500
56000 54500 55100 54025 54750 53400 2970250000 3036010000 2918700625 2997562500 2851560000
54500 55100 54025 54750 53400 53725 3036010000 2918700625 2997562500 2851560000 2886375625
Harga Saham/data_tabel_prediksi.txt 0 → 100644
[0;0;0;0;4.963032e-01;0;0;0;9.922694e-01;0;0;0;7.373894e-01;0;0;0;0;0;1.594092e-01;-7.100904e-01;2.441103e-01;0;0;0;0] 9.180107e-02 1.089312e+01
[0;0;0;0;4.963032e-01;0;0;0;9.922694e-01;0;0;0;7.373894e-01;0;0;0;0;0;1.594092e-01;-7.100904e-01;2.441103e-01;0;0;0;0] 9.180107e-02 1.089312e+01
[0;7.825293e-02;3.330558e-01;0;-6.577579e-01;0;3.383506e-01;9.372987e-01;0;0;-2.474556e-01;0;1;-5.476246e-01;0;0;-4.694382e-01;9.653268e-01;-3.122460e-01;-7.844620e-01;7.593074e-01;-4.785440e-01;-9.549748e-01;0;1] 9.056965e-02 1.104123e+01
[0;7.825293e-02;3.330558e-01;0;-6.577579e-01;0;3.383506e-01;0;0;0;-2.474556e-01;0;1;-5.476246e-01;0;1;-4.694382e-01;9.653268e-01;-3.122460e-01;-7.844620e-01;7.593074e-01;-4.785440e-01;-9.549748e-01;0;1] 9.056965e-02 1.104123e+01
[0;7.825293e-02;3.330558e-01;0;-6.577579e-01;0;3.383506e-01;0;0;0;-2.474556e-01;0;1;-5.476246e-01;0;1;-4.694382e-01;9.653268e-01;0;-7.844620e-01;7.593074e-01;-4.785440e-01;-9.549748e-01;0;1] 9.056965e-02 1.104123e+01
[1;7.825293e-02;3.330558e-01;0;-6.577579e-01;0;3.383506e-01;0;0;0;-2.474556e-01;0;1;0;0;1;-4.694382e-01;9.653268e-01;0;-7.844620e-01;7.593074e-01;-4.785440e-01;-9.549748e-01;0;1] 9.235356e-02 1.082795e+01
[1;7.825293e-02;3.330558e-01;0;-6.577579e-01;0;3.383506e-01;0;0;0;-2.474556e-01;0;1;0;0;1;-4.694382e-01;9.653268e-01;1;-7.844620e-01;7.593074e-01;-4.785440e-01;-9.549748e-01;0;1] 9.293112e-02 1.076066e+01
[1;7.825293e-02;3.330558e-01;0;0;0;3.383506e-01;0;0;0;-2.474556e-01;0;1;0;0;1;-4.694382e-01;9.653268e-01;1;-7.844620e-01;7.593074e-01;-4.785440e-01;-9.549748e-01;0;1] 9.331288e-02 1.071663e+01
[1;7.825293e-02;3.330558e-01;0;0;0;3.383506e-01;0;0;0;-2.474556e-01;0;1;0;0;0;-4.694382e-01;9.653268e-01;1;-7.844620e-01;7.593074e-01;-4.785440e-01;-9.549748e-01;0;1] 9.273307e-02 1.078364e+01
[1;7.825293e-02;3.330558e-01;0;0;0;3.383506e-01;1;0;0;-2.474556e-01;0;1;0;0;0;-4.694382e-01;9.653268e-01;1;-7.844620e-01;7.593074e-01;0;-9.549748e-01;0;1] 9.359153e-02 1.068473e+01
Harga Saham/data_uji_prediksi.txt 0 → 100644
54500 56000 55900 55800 56300 2970250000 3136000000 3124810000 3113640000 3169690000
-2.823451e+04 54500 56000 55900 55800 7.971877e+08 2970250000 3136000000 3124810000 3113640000
-2.778369e+04 -2.823451e+04 54500 56000 55900 7.719332e+08 7.971877e+08 2970250000 3136000000 3124810000
-2.810105e+04 -2.778369e+04 -2.823451e+04 54500 56000 7.896691e+08 7.719332e+08 7.971877e+08 2970250000 3136000000
-4.225740e+04 -2.810105e+04 -2.778369e+04 -2.823451e+04 54500 1.785688e+09 7.896691e+08 7.719332e+08 7.971877e+08 2970250000
Harga Saham/hasil_prediksi.txt 0 → 100644
9 176 205 28 299 227 10 20 0 24 105 45 91 30976 42025 784 89401 51529 100 400 0 576 11025 2025 8281
\ No newline at end of file
Harga Saham/prediksi.txt 0 → 100644
[1;7.825293e-02;3.330558e-01;0;0;0;3.383506e-01;1;0;0;-2.474556e-01;0;1;0;0;0;-4.694382e-01;9.653268e-01;1;-7.844620e-01;7.593074e-01;0;-9.549748e-01;0;1]
[1,1, datake-11]
\ No newline at end of file
Petunjuk Instalasi/Instalasi.txt 0 → 100644
Petunjuk instalasi:
1. Install matlab
2. Copy projek ke direktori anda
3. Jalankan ForecastingData.m
\ No newline at end of file
meandata.m 0 → 100644
function m=meandata(data)
m=mean(data(:));
\ No newline at end of file
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