Hardiyanti, Siti (2014) Implementasi Metode Modified K-Nearest Neighbor (Mknn) Pada Penentuan Keminatan Sekolah Mengah Atas (Sma) (Studi Kasus : Sma Negeri 1 Seririt). Sarjana thesis, Universitas Brawijaya.
Abstract
Berdasarkan kurikulum 2013, keminatan SMA tidak melewati tes dan hanya dilihat dari perolehan nilai mata pelajaran kelas IX SMP. Berdasarkan nilai matapelajaran tersebut, kemudian bisa disesuaikan kemana saja siswa yang bersangkutan bisa masuk sesuai dengan kemampuannya. Namun pada kenyataannya saat ini, banyak siswa memilih keminatan berdasarkan karena dorongan orang tua maupun mengikuti pilihan teman yang terkadang tidak sesuai dengan kemampuannya. Untuk mempemudah tahapan pengklasifikasian keminatan berdasarkan nilai rapor maka digunakan metode metode Modified K-Nearest Neighbor (MKNN). MKNN dilakukan dengan cara menghitung validitas data training sesuai dengan nilai k, menghitung jarak antara data uji dengan data training dan menghitung weight voting. Hasil pengujian menunjukkan bahwa parameter nilai k sangat berpengaruh terhadap hasil klasifikasi dan akurasi yang dihasilkan. Rata-rata akurasi cenderung menurun seiring dengan penambahan nilai k sedangkan peningkatan jumlah data training turut disertai dengan peningkatan hasil akurasi. Untuk data training dengan sebaran kelas tidak seimbang mengalami penurunan nilai akurasi seiring dengan bertambahnya jumlah data. Hasil akurasi tertinggi didapat saat nilai k=3 yaitu 86.1% pada saat sebaran data kelas seimbang.
English Abstract
Based on the curriculum of 2013, interest of the high school not pass the test and only seen from grades IX junior class lesson. Based on the value of the lesson, then can be adjusted anywhere can enter the student according to his ability. But in fact today, many students choose the interest thing based as well as urge parents to follow the choice that sometimes friends are not in accordance with his ability. To simplify the classification stages of specialization based on grades then used the method method Modified K-Nearest Neighbor (MKNN). MKNN done by calculating the validity of the training data according to the value of k, calculate the distance between the test data to the training data and calculate the weight of voting. The test results showed that the value of the parameter k affects the classification results and the resulting accuracy. Average accuracy tends to decline with the addition of the value of k, while the increase in the amount of training data also accompanied by an increase in accuracy of the results, for the training data with imbalanced class accuracy values decreased with increasing amount of data. The results of the highest accuracy is obtained when the value of k = 3 is 86.1% upon the distribution of data by class.
| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | SKR/FTIK/2014/238/051407531 |
| Subjects: | 000 Computer science, information and general works > 005 Computer programming, programs, data |
| Divisions: | Fakultas Ilmu Komputer > Teknik Informatika |
| Depositing User: | Budi Wahyono Wahyono |
| Date Deposited: | 06 Nov 2014 14:11 |
| Last Modified: | 20 Oct 2021 05:03 |
| URI: | http://repository.ub.ac.id/id/eprint/146028 |
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