Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh)

Abstract

The e-questionnaire application that researchers built using CodeIgniter and React-Js This study aims to data mining by using rapidminer tools to collect student data from the Feeder application page from the class of 2010-2014 which is assumed that the student class has been declared graduated in 2018. The data was collected from 5 (five) Private Universities in the City Banda Aceh. then by observing the graduation level using data mining can bring a considerable contribution to educational institutions, in an effort to improve curriculum competency in Higher Education, it is expected that the results of data mining can make reference to curriculum standards as a form of graduate competency improvement. The research method uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) which is used as a standard data mining process as well as a research method with stages starting from Business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results showed that the data mining algorithm for graduation prediction based on the selected pass accuracy attribute revealed that the prediction level was uniform with the algorithm used, Naïve Bayes, prediction accuracy was 84%. The data attributes that were found to have significantly influenced the classification process were the GPA and Study Length. The results obtained that students who graduated by 60% are students who are educated in ASM Nusantara and AMIK Indonesia, while in Banda Aceh STIES and Serambi University Mecca the prediction of graduation is 52%. Another thing is different from STIA Iskandar Thani where the prediction of graduating is only 48% and not passing on time is 52%. The results of this prediction can reveal and become a recommendation for prospective students or academics to increase the quantity of graduates and increase student confidence in tertiary institutions.Keywords:Prediction, Student Graduation, Naive Bayes Algorithm.

Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Algoritma C4.5 Pada Uin Syarif Hidayatullah Jakarta

“…Atribut prediksi terdiri dari Angkatan, IPK, lama studi, PTS dan 1 atribut kelas yaitu tepat waktu yang berisi klasifikasi ketepatan lulus mahasiswa yaitu "ya" tepat waktu dan "tidak" tidak tepat waktu.Metode yang digunakan adalah Naïve Bayes. Dari penelitian ini diperoleh tingkat akurasi algoritma Naïve Bayes dalam memprediksi kelulusan mahasiswa sebesar 84% [14].…”

Penerapan Data Mining Dan Algoritma Naïve Bayes Untuk Pemilihan Konsentrasi Mahasiswa Menggunakan Metode Klasifikasi

“…Dari hasil prediksi kedua algoritma tersebut, maka Algoritma C4.5 memiliki nilai akurasi yang lebih baik dibandingkan nilai akurasi metode Naive Bayes Classifier, oleh karena itu Algoritma C4.5 dapat direkomendasikan sebagai algoritma yang layak digunakan dalam menyelesaikan masalah prediksi kelulusan mahasiswa pada STMIK Bina Nusantara Jaya Lubuklinggau. Hasil penelitian [6] tentang bagaimana memprediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh)". Adapun atribut data yang digunakan untuk proses klasifikasi adalah Indeks Prestasi Akademik (IPK) dan masa studi.…”

Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa

“…Cross Industry Standard Process for Data Mining (CRISP-DM) merupakan metode penelitian yang dipakai pada penelitian ini. Karena sudah banyak para peneliti yang menggunakannya dan terbukti cocok untuk menyelesaikan masalah proyek atau penelitian bidang data mining [25], [34]. Dimana alur dari metode ini dimulai dari tahap pemahaman bisnis (business understanding), pemahaman data (data understanding), persiapan data (data preparation), pemodelan data (modeling), evaluasi model (evaluation) dan pengembangan (deployment) seperti yang terlihat pada Gambar 1.…”