Purpose and Objectives KESIMPULAN DAN SARAN
Jurnal Ilmiah Komputer dan Informatika KOMPUTA
Edisi. .. Volume. .., Bulan 20.. ISSN : 2089-9033
Testing with method 1 Test image included in the database
Testing method 1 was conducted by examining the image included in the database, this test aims to
determine the level of recognition of the image that has been trained, the image data that is used there
are 120 pieces of imagery which consists of three classes, with each class there are 40 images.
Training data is used, and the test data in Appendix C1
Results Testing method 1. Table 1 Level of Accuracy of each class on the
testing method 1
Kelas Prediction
Count Citra
Akurati Kuarsa
Feldspar Targ
et Kuarsa
14 2
16 87.5
Feldspar 10
4 16
25 average
56.25
Testing with Test Method 2 images that are not included in the database
Testing method 2 was conducted by examining the image that are not included in the database, this test
aims to determine the level of the test image recognition outside the database on the image of the
train in the database. image data used to train there were 100 pieces of imagery which consists of five
classes, with each class there are 20 images. And also used the test image data there are 100 pieces of
imagery which consists of five classes, with each class there are 20 images.
Training data and test data exist on the attachment. Assay results using the test method 2.
Table 2 Level of Accuracy of each class on the testing method 1
Kelas Prediksi
Jumla h Citra
Akuras i
Kua rsa
Feldspa r
Ta rg
et Kuarsa
12 4
16 75
Feldspar 13
3 16
18 average
46.5
2.8 conclusion Testing Based on the results of one test scenario that is
testing the same test data with training data, it can be concluded that the K-Nearest Neighbor method can
classify with an accuracy of 70. Based on the test scenario 2 is test of test data that are not in training
data, KNN can classify with an accuracy of 46. From the test results, the accuracy has a very good
level, because the data generated by the feature extraction feature extraction method of order one
and two have a large degree of inequality, so the recognition process can be run well.