PENERAPAN EQUAL-WIDTH INTERVAL DISCRETIZATION DALAM METODE NAIVE BAYES UNTUK MENINGKATKAN AKURASI PREDIKSI PEMILIHAN JURUSAN SISWA

Alfa Saleh, Fina Nasari

Abstract

Abstract

The student majors is very important thing in developing students' academic skills and talents, it is required by the students are expected to hone the academic ability according to the field that is mastered and this is done so that each student can learn more in the Subjects that match the concentration that has been determined for each - students based on some predefined criteria. In this research has been tested by the method of Naive Bayes which aims to classify the students department based on the criteria that support. Where it is currently conducted with a case study on Madrasah Aliyah PAB 6 Helvetia students and obtained results from 100 student data with 90% accuracy rate. However, in order to improve the accuracy of the results of calcification, the researcher, the method used by using Unsupervised Discretization techniques that will transform numerical / continuous criteria into a categorical criterion. The result of the discretization on 100 data have been tested, it is proved that the results of the techniques used Discontented Disputes on the method of Naive Bayes rose from 90% to 93%.

 

Abstrak

Jurusan siswa merupakan hal yang sangat penting dalam mengembangkan keterampilan dan bakat akademik siswa, hal ini dianggap perlu sebab siswa diharapkan mampu mengasah kemampuan akademis sesuai bidang yang dikuasai dan hal ini dilakukan agar setiap siswa dapat belajar lebih dalam pada mata pelajaran yang sesuai dengan konsentrasi yang telah ditentukan untuk masing-masing siswa berdasar beberapa kriteria yang telah ditetapkan. Pada penelitian ini telah dilakukan pengujian dengan metode Naive Bayes yang bertujuan untuk mengkasifikasikan jurusan siswa bedasarkan kriteria yang menunjang. pada penelitian ini dilakukan dengan studi kasus pada siswa Madrasah Aliyah Swasta PAB 6 Helvetia dan didapatkan hasil pengujian dari 100 data siswa dengan tingkat keakuratan 90%. Namun, untuk meningkatkan akurasi hasil kalsifikasi penentuan jurusan siswa ini, peneliti mengembangkan metode yang digunakan sebelumnya dengan menerapkan teknik Unsupervised Discretization yang akan mentransformasikan kriteria numerik/kontinyu menjadi kriteria kategorikal. Hasil dari diskritasi pada 100 data siswa yang diuji, terbukti bahwa hasil klasifikasi penerapan teknik Unsupervised Discretization pada metode Naive Bayes naik dari 90% menjadi 93%.

Keywords

Data Mining; Naive Bayes; Equal-Width Interval Discretization; student major prediction; Data Mining; Naive Bayes; Equal-Width Interval Discretization; Memprediksi Jurusan Siswa

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