Data Mining Analysis Using Naive Bayes Algorithm and Knn to Predict Graduation of D3 Students Department of Information Management
DOI:
https://doi.org/10.46965/ijeth.v2i1.4Trefwoorden:
graduation, K-NN, KDD, Naïve Bayes, Data MiningSamenvatting
In this study, the specific objective of this research is to obtain the results of decisions in predicting students of the informatics management study program whether they can graduate on time for 3 years or more in the specified time and with a minimum GPA of 3.00. Student graduation is one of the Internal Quality Assurance Standards on campus or college. To achieve a decent quality of graduation, a study is needed to be able to predict the graduation rate with predetermined standards, so as to reduce and anticipate problems in the academic field that occur. In this study, data mining methods are used to predict the passing rate and standard GPA with a classification function. The algorithm used in this study is Naïve Bayes and K-NN, the stages used in the application of this research are KDD starting with selecting, preprocessing, transformation, data mining and evaluation/interpretation stages. The final result of this study using the K-NN and Naïve Bayes algorithms is that K-NN produces an accuracy rate of 94.81% and Nave Bayes produces an accuracy rate of 90.49%, so it can be concluded that the K-NN algorithm is better used to predict graduation of D3 students majoring in informatics management.
Referenties
Azahari, A., Yulindawati, Y., Rosita, D., & Mallala, S. (2020). Komparasi Data Mining Naive Bayes dan Neural network memprediksi Masa Studi Mahasiswa S1. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(3), 443. https://doi.org/10.25126/jtiik.2020732093
Gorunescu, Florin. (2011). Data Mining Concept ,Model Technique.Springer-Verlag Berlin Heidelberg
Lizsara, P. A., Oyama, S., & Wardani, S. (2020). Implementasi Data Mining Menggunakan Metode Naive Bayes Untuk Memprediksi Ketepatan Waktu Tingkat Kelulusan Mahasiswa (Study Kasus: Program Studi Informatika Universitas PGRI Yogyakarta). Seri Prosiding Seminar Nasional Dinamika Informatika, 4(1), 34–37.
Lizsara, P. A., Oyama, S., & Wardani, S. (2020). Implementasi Data Mining Menggunakan Metode Naive Bayes Untuk Memprediksi Ketepatan Waktu Tingkat Kelulusan Mahasiswa (Study Kasus: Program Studi Informatika Universitas PGRI Yogyakarta). Seri Prosiding Seminar Nasional Dinamika Informatika, 4(1), 34–37
Murtopo, A. A. (2015). Prediksi Kelulusan Tepat Waktu Mahasiswa STMIK YMI Tegal Menggunakan Algoritma Naive Bayes Time Graduation Prediction by Using Naive Bayes Algorithm at STMIK YMI Tegal. Vol.7 No.3, 145–154
Pratama, A., Wihandika, R. C., & Ratnawati, D. E. (2018). Implementasi Algoritme Support Vector Machine ( SVM ) untuk Prediksi Ketepatan Waktu Kelulusan Mahasiswa. 2(4), 1704–1708.
Resti Hutami1, E. Z. A. (2016). Implementasi Metode K-Nearest Neighbor
Sillueta, C.Y. (2016). Implementasi Data Mining Untuk Memprediksi Kelulusan Mahasiswa Dengan Metode Klasifikasi Dan Algoritma Knearest Neighbor Berbasis Desktop (Studi Kasus : Fakultas Teknologi Informasi, Program Studi Teknik Informatika, Tugas Akhir
Suyatno. (2017) Data Mining Untuk Klasifikasi dan Klasterisasi Data. Bandung: Informatika.
