Aminudin, Arif (2019) KLASIFIKASI TINGKAT KEMATANGAN BUAH PEPAYA MENGGUNAKAN METODE K-NEAREST NEIGHBOR BERDASARKAN WARNA KULIT BUAH. Tugas Akhir thesis, University of Technology Yogyakarta.
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Abstract
Papaya fruit is a fruit that is easily grown in tropical areas and also has many health benefits. Papaya fruit is very easy to find anywhere from traditional markets to supermarkets. Papaya fruit that is often found is Californian papaya and Bangkok Thailand papaya. Although this fruit is often found, there are still many buyers who have not been able to classify their own papaya fruit, especially in a minimarket or supermarket that only serves the fruit, where it can reduce the interest of buyers to buy papaya in a minimarket or supermarket. The criteria for determining quality based on the image of papaya is skin color. With the K-Nearest Neighbor (KNN) method, it will classify papaya fruit into several classification classes. The purpose of this research is to help Bangkok papaya fruit buyers who will buy in supermarkets or minimarkets where buyers cannot classify their own fruit to be purchased. The application used is android based so it is easy to carry and operate. There are 3 categories of Bangkok papaya fruit maturity classification, namely Mature, Half Mature, and Raw. The data uses 12 training images and 12 test images. The results of the accuracy of data suitability without using an average of 75% for K = 1, K = 5 and K = 7. Whereas if using an average of 66.67% at K = 5 and 75% at K = 7 with different test data. Keywords: Papaya Fruit, Fruit Classification, KNN, Image Processing, Android.
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
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| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 27 Oct 2019 23:35 |
| Last Modified: | 27 Oct 2019 23:35 |
| URI: | http://eprints.uty.ac.id/id/eprint/3570 |
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