Jurnal Ilmiah Komputer dan Informatika KOMPUTA
7
Edisi. .. Volume. .., Bulan 20.. ISSN : 2089-9033
is the value of SRE short run emphasis, LRE long run emphasis, GLU gray level uninformity, RLU
run length uninformity and RPC run percentage . The first step in calculating run length method is to
make a run length matrix, the matrix value obtained from the run length grayscale matrix to calculate the
degree of gray the same on each line. Orientation is formed by a four-way shift, ie 0
, 45 , 90
and 135 .
For example grayscale matrix in Figure 5 is used to obtain a run length matrix. Here is a run
length matrix with a shift towards 00, 450, 900 and 1350 are produced.
Where i = gray degrees value
j = consecutive pixel run rj = The number of pixels in sequence by many
order gi = The number of pixels in sequence based on
the gray degrees s = The total number of runs generated value
Table 3 Run Length Matrix 0
Table 4 Run Length Matrix 45 Table 5 Run Length Matrix 90
Table 6 Run Length Matrix 135
After cakcukating the SRE features Short Run Emphasis, LRE Long Run Emphasis, GLU Grey
Level Uninformity,
RLURun Length
Uninformity, and RPC Run Percentage on run length matrix 0
, 45 , 90
, dan 135 , then the result
of features value are : Table 7 Run Length Matrix Features Value
Feature Run Length Matrix
45 90
135 SRE
0.69666 0.79458
0.59571 0.73156
LRE 3.67089
2.66213 7.9125
3.35329 GLU
40.61392 46.80109
29.525 42.47305
RLU 286.58544
430.62398 162.83333
333.35329 RPC
0.61719 0.7168
0.46875 0.65234
2.4.2 Naive Bayesian Training Analysis
Naive Bayesian training is done to obtain training data in the form of the mean and variance.
The mean and variance of this will be referred for testing. In the training phase the mean and variance
sought from every feature on every class training data. The following dataset used for training can be
seen in Table 8.
1 2
3 4
5 6
7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 1
2 3
4 1
1 5
1 3
2 2
8 6
6 6
2 2
16 7
4 7
4 7
5 1
28 8
8 10 5
6 29
9 19 15
4 1
39 10
27 12 4
1 44
11 44
7 4
3 58
12 31 13
7 1
2 54
13 37 16
2 1
1 57
14 39
6 3
1 1
1 51
15 33
9 3
1 1
1 48
16 29
7 3
39 17
33 8
2 1
44 18
20 9
3 1
33 19
17 6
2 25
20 16
3 1
20 21
10 1
11 22
7 1
8 23
6 6
24 3
3 25
2 2
26 2
2 4
27 2
1 3
28 1
1 29
30 31
r j|ɵ 397 141 52 29 9 2 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 632 i
r j G i|ɵ
1 2
3 4
5 6
7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 1
2 3
4 2
2 5
6 3
3 12
6 19
1 2
1 23
7 27
7 3
2 2
3 44
8 20
8 4
3 35
9 33 13
2 48
10 43
9 2
54 11
49 12 3
64 12
47 12 4
1 1
65 13
47 6
5 1
1 60
14 47 11
3 61
15 44
8 4
1 57
16 32
7 2
41 17
34 11 1
46 18
25 6
2 2
35 19
17 7
1 25
20 19
2 1
22 21
8 2
10 22
8 1
9 23
6 6
24 3
3 25
2 2
26 4
1 5
27 3
1 4
28 1
1 29
30 31
r j|ɵ 546 128 37 12 7 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 734 i
r j G i|ɵ
1 2
3 4
5 6
7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 1
2 3
4 2
2 5
6 1
1 2
10 6
10 7
1 1
19 7
16 3
4 2
1 3
1 30
8 8
4 4
1 2
1 20
9 5
3 4
1 3
2 1
19 10
8 5
6 4
1 1
25 11
19 4
5 4
2 1
1 36
12 20
7 9
3 2
1 42
13 19
7 5
4 1
1 37
14 22 10
5 4
1 42
15 22 12
6 1
1 42
16 20
6 4
2 32
17 21
9 3
1 1
35 18
11 5
4 2
2 24
19 7
5 2
2 16
20 7
2 2
1 1
13 21
7 1
1 9
22 4
3 7
23 6
6 24
3 3
25 2
2 26
4 1
5 27
2 1
3 28
1 1
29 30
31 r j|ɵ 252 95 67 28 16 6 2 2 2 7 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 480
i r j
G i|ɵ
1 2
3 4
5 6
7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 1
2 3
4 2
2 5
7 1
4 12
6 19
1 1
2 23
7 20 10
1 5
4 1
41 8
8 8
9 1
1 1
28 9
19 9
2 1
2 33
10 24
8 3
1 1
1 38
11 37 12
1 2
2 54
12 32 19
6 1
58 13
42 13 3
2 60
14 40 16
2 58
15 43 12
1 1
57 16
37 6
1 44
17 32
7 3
1 43
18 23 11
2 36
19 14
6 3
23 20
9 7
1 17
21 12
12 22
8 1
9 23
4 1
5 24
3 3
25 2
2 26
4 1
5 27
3 1
4 28
1 1
29 30
31 r j|ɵ 445 150 43 15 8 3 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 668
i r j
G i|ɵ
Jurnal Ilmiah Komputer dan Informatika KOMPUTA
8
Edisi. .. Volume. .., Bulan 20.. ISSN : 2089-9033
Table 8 Training Data
Setelah dilakukan proses pelatihan maka didapat hasil data pelatihan yang dapat dilihat pada
table 9. Tabel 9 Hasil Pelatihan
2.4.3 Naive Bayesian Testing Analysis
Naive Bayesian testing phase is the testing phase of new data on the training data. Examples of
imagery to be tested can be seen in Figure 5.
Figure 5 Test Image The following feature extraction feature value test
images can be seen in Table 10. Table 10 Test Image Feature
Fitur Nilai
SRE 0.68916
LRE 4.27881
GLU 38.94517
RLU 287.53854
RPC 0.60889
After the testing process Naive Bayesian posterior retina obtained value class A is greater
than B grade retina, then the class which is suitable for the above test images are retina Class A.
3. TESTING AND RESULT
Testing method aims to find out the results of the run length method and Naive Bayesian in
identifying the retina of the eye. In this study, plan testing is done with a few scenarios. Heres a
scenario that will be done : 1.
Test the image of the eyes retina that have been used as training data.
2. Test the image of the eyes retina that has
not been previously trained and tested the effect of the amount of training data on the
level of accuracy.
Figure 6 Method Testing Result Based on the testing that was done method of
run length and Naive Bayesian methods can be used to identify the image of the retina of the human eye
and is based on the above test concluded that the amount of training data affect the level of accuracy
as more training data the greater the resulting degree of accuracy.
4. CLOSURE
4.1 Conclusion
The results of the research that has been done in the arrangement of this paper as well as referring to the
purpose of research, so it can be concluded. 1.
Run length method and Naive Bayesian can be used to identify the retina of the eye based on
the image. 2.
The level of accuracy of the run length method and Naive Bayesian in identifying the retina of
the eye based on the image is 100.
4.2 Suggestion Based on the results of the research that has been
reached at this time, there are some suggestions that might be helpful if someone wants to do a similar
study, i.e.: 1.
The imagery Dataset that is used should have a
more diverse class.
2. To obtain a high level of accuracy in classifying
the various images, it’s better to use a lot of training data.
Retina SRE
LRE GLU
RLU RPC
A.1 0.70463
4.3997 39.85326
303.34901 0.61377
A.2 0.68916
4.27881 38.94517
287.53854 0.60889
B.1 0.66979
5.1908 40.03463
261.16692 0.57544
B.2 0.64519
5.91579 41.56174
232.42015 0.54858
Retina Nilai
Fitur SRE
LRE GLU
RLU RPC
A Mean
0.69690 4.33926
39.39922 295.44378
0.61133 Varian
0.00012 0.00731
0.41231 124.98548
0.00001 B
Mean 0.65749
5.55330 40.79819
246.79354 0.56201
Varian 0.00030
0.26281 1.16603
413.18839 0.00036
