Klasifikasi Kualitas Buah Apel Dengan Algoritma K-Nearest Neighbor (K-NN) Menggunakan Bahasa Pemrograman Python
Fruit is an important intake for the human body, apples are included in the fruit favored by the people of Indonesia. For this reason, it is necessary to provide apples of good quality, so that they can benefit the body. By using the k-NN method that is considered able to train data quickly and effe...
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| Main Author: | |
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| Format: | Article |
| Language: | Indonesian |
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LPPM Universitas Bina Sarana Informatika
2024-07-01
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| Series: | Computer Science |
| Subjects: | |
| Online Access: | https://jurnal.bsi.ac.id/index.php/co-science/article/view/3328 |
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| Summary: | Fruit is an important intake for the human body, apples are included in the fruit favored by the people of Indonesia. For this reason, it is necessary to provide apples of good quality, so that they can benefit the body. By using the k-NN method that is considered able to train data quickly and effectively for training data and testing data in large quantities. This study began from the collection of datasets obtained from https://www.kaggle.com/, then perform a preprocessing process followed by separating the training data and testing data with a composition of 25% testing data and 75% training data. Then the k-NN method is applied to this study to be classified based on several existing criteria, so as to obtain the results of performance evaluation K-NN with the value of accuracy that has been calculated with python programming. In implementing datamining using Python programming language by utilizing the library that has been provided as a process to facilitate the implementation of machine learning. From The Matrix confution test, there are 441 data predicted with true data, and 440 data predicted incorrectly. As for the 54 and 65 data predicted to be less precise than 1000 testing data. So that the accuracy value obtained by the k-NN method is equal to 0.88 or 88%. It is seen that the k-NN method can work well, quickly and efficiently in training large amounts of data. |
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| ISSN: | 2808-9065 2774-9711 |