(Second File) Line 6 : The function return value "jumlah_data" might be unset and the function "numel" might be unused . Line 23 . Line 35. Line 38 . Line 39 . Line 47

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clc; clear; close all;
% membaca file citra dalam folder
image_folder ='data uji';
filename = dir(fullfile(image_folder, '*.jpg'));
LINE 6 : function jumlah_data = numel(filenames)
% menginiliasisasi variabel data_latih
data_latih = zeros(jumlah_data,5);
% proses ekstraksi ciri orde satu
for k = 1:jumlah_data
full_name = fullfile(image_folder, filenames(k).name);
Img = imread(full_name);
Img = rgb2gray(Img);
H = imhist(Img)';
H = H/sum(H);
I = (0:255);
CiriMEAN = I * H;
CiriENT = -H * log2(H+eps)';
CiriVAR = (I-CiriMean).^2*H';
CiriSKEW = (I-CiriMean).^3*H'/ CiriVAR^1.5;
LINE 23 : CiriKURT = (I-CiriMean).^4*H'/ CiriVAR^2-3;
data_latih(k,:) = [CiriMEAN,CiriENT,CiriVAR,CiriSKEW,CiriSKURT];
end
% penentuan nilai target untuk masing-maisng jenis bunga
target_latih = zero(1,jumlah_data);
target_latih(1:6) = 1; %kamboja_biasa
target_latih(7:12) = 2; %kamboja_merah
target_latih(13:18) = 3; %kamboja_plumeriapudica
target_latih(19:24) = 4; %melati-gambir
target_latih(25:30) = 5; %melati_kuning
% pelatihan menggunakan algoritma multivism
LINE 35 : output = multivsm(data_latih,target_latih,data_latih);
% load data_latih dan target_latih hasil pelatihan
LINE 38 : load data_latih
LINE 39 : load target_latih
% pengujian menggunakan algoritma multivsm
output = multivsm(data_latih,target_latuh,data_uji);
%menghitung nilai akurasi pelatihan
[n,~] = find(targer_latihan==output');
jumlah_benar = sum(n);
LINE 47 : akurasi = jumlah_benar/jumlah_data*100;
end
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Accepted Answer

Walter Roberson
Walter Roberson on 24 Apr 2020
% membaca file citra dalam folder
image_folder ='data uji';
filename = dir(fullfile(image_folder, '*.jpg'));
jumlah_data = numel(filenames);
% menginiliasisasi variabel data_latih
data_latih = zeros(jumlah_data,5);
% proses ekstraksi ciri orde satu
for k = 1:jumlah_data
full_name = fullfile(image_folder, filenames(k).name);
Img = imread(full_name);
Img = rgb2gray(Img);
H = imhist(Img)';
H = H/sum(H);
I = (0:255);
CiriMEAN = I * H;
CiriENT = -H * log2(H+eps)';
CiriVAR = (I-CiriMean).^2*H';
CiriSKEW = (I-CiriMean).^3*H'/ CiriVAR^1.5;
CiriKURT = (I-CiriMean).^4*H'/ CiriVAR^2-3;
data_latih(k,:) = [CiriMEAN,CiriENT,CiriVAR,CiriSKEW,CiriSKURT];
end
% penentuan nilai target untuk masing-maisng jenis bunga
target_latih = zero(1,jumlah_data);
target_latih(1:6) = 1; %kamboja_biasa
target_latih(7:12) = 2; %kamboja_merah
target_latih(13:18) = 3; %kamboja_plumeriapudica
target_latih(19:24) = 4; %melati-gambir
target_latih(25:30) = 5; %melati_kuning
% pelatihan menggunakan algoritma multivism
output = multivsm(data_latih,target_latih,data_latih);
[n,~] = find(targer_latih==output');
jumlah_benar(1) = sum(n);
akurasi(1) = jumlah_benar(1)/jumlah_data*100;
% load data_latih dan target_latih hasil pelatihan
dl = load('data_latih');
tl = load('target_latih');
% pengujian menggunakan algoritma multivsm
output = multivsm(dl.data_latih,tl.target_latuh,data_uji);
%menghitung nilai akurasi pelatihan
[n,~] = find(targer_latihan==output');
jumlah_benar(2) = sum(n);
akurasi(2) = jumlah_benar(2)/jumlah_data*100;

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