Munanda, Wiliana (2020) KLASIFIKASI PEMINATAN SISWA DENGAN METODE K-NEAREST NEIGHBOR (Studi kasus SMP Negeri 1 Pabuaran). Tugas Akhir thesis, University of Technology Yogyakarta.
|
Text
Naskah_Publikasi_Wiliana Arie Munanda_5150411239.pdf Download (1MB) | Preview |
Abstract
One of the main parameters to achieve the effectiveness of learning is to achieve student learning objectives optimally according to graduation competency standards. The evaluation results are very diverse in various ways to evaluate the results of the evaluation in accordance with the competencies of interests and talents of the students, however the students with different abilities are one of the problems in determining the policy of learning activities that are in accordance with the competencies of interests and talents of students. The interests and talents of different students become one of the problems in determining the learning activities needed by the students. Therefore, a system for classifying student learning outcomes based on examinations was made using the k Nearest Neighbor (kNN) method. The k-Nearest Neighbor algorithm is a method for classifying objects based on learning data that have the most distance from the object. The implementation of k Nearest Neighbor (kNN) has been carried out using 243 data, with a comparison of training data and test data is 70%: 30%, namely 170 training data and 73 test data with k = 5 giving a true prediction result of 61 data and a false prediction of 12 data with an accuracy of 83.56%. Keywords: Interests and Talents, Test Scores, Nearest Neighbor
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 26 Mar 2020 06:47 |
| Last Modified: | 26 Mar 2020 06:47 |
| URI: | http://eprints.uty.ac.id/id/eprint/4877 |
Actions (login required)
 |
View Item |
