Komparasi Algoritme K-NN, Naïve Bayes, dan Cart untuk Memprediksi Penerima Beasiswa
DOI:
https://doi.org/10.23887/jstundiksha.v12i2.51745Keywords:
Beasiswa, Algoritme, K-NN, Naïve Bayes, CARTAbstract
Persebaran penerima beasiswa di tanah air Indonesia terdapat masalah salah satunya yaitu tidak tepat sasaran. Pemerintah Indonesia memberikan beasiswa kepada peserta didik di Indonesia sebagai contoh yaitu Program Indonesaia Pintar dan, Program Indonesia Pintar Pendidikan Dasar dan Pendidikan menengah. Pemberian beasiswa diperlukan adanya klasifikasi dalam mengambil keputusan penerima beasiswa tersebut untuk meminimalisir salah sasaran. Prediksi secara dini harus dilakukan untuk mengantisipasi kesalahan dalam penerima bantuan beasiswa, salah satunya menggunakan teknik data mining. Tujuan penelitian ini untuk menganalisis Komparasi Algoritme K-NN, Naïve Bayes, Dan CART untuk Memprediksi Penerima Beasiswa bagi pengelola di SMA. Penelitian yang dilakukan menggunakan data mining terhadap dataset penerima beasiswa dengan memanfaatkan aplikasi Weka dalam mengolah data. Dataset yang digunakan dalam penelitian ini yaitu data penerima beasiswa di salah satu SMA dengan jumlah dataset yaitu 948 data dan memiliki 6 atribut (5 atribut dan 1 target atribut). Metode yang digunakan dalam penelitian ini yaitu Confusion matrix dan K-fold 10 Cross Validation. Komparasi Algoritme K-NN, Naïve Bayes, Dan CART untuk Memprediksi Penerima Beasiswa. Dari ketiga Algoritme yang digunakan dalam penelitian diperoleh kesimpulan Algoritme CART merupakan Algoritme dengan hasil akurasi yang paling tinggi sebesar 91.3502% untuk memprediksi penerima beasiswa dengan kategori Good Classification.
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