Analisis Metode K-Nearest Neighbors (K-NN) Dan Naive Bayes Dalam Memprediksi Kelulusan Mahasiswa

Abstract

In this research the author aims to apply the K-NN and Naive Bayes algorithms for predicting student graduation rates at Sekolah Tinggi Pariwisata (STP) Mataram, The comparison of these two methods was carried out because based on several previous studies it was found that K-NN and Naive Bayes are well-known classification methods with a good level of accuracy. But which one has a better accuracy rate than the two algorithms, that's what researchers are trying to do. The output of this application is in the form of information on the prediction of student graduation, whether to graduate on time or not on time. The selection of STP as the research location was carried out because of the imbalance between the entry and exit of students who had completed their studies. Students who enter have a large number, but students who graduate on time according to the provisions are far very small, resulting in accumulation of the high number of students in each period of graduation, so it takes the initial predictions to quickly overcome these problems. Based on the results of designing, implementing, testing, and testing the Student Graduation Prediction Application program using the K-NN and Naive Bayes Methods with the Cross Validation method, the result is an accuracy for the K-NN method of 96.18% and for the Naive Bayes method an accuracy of 91.94% with using the RapideMiner accuracy test. So based on the results of the two tests between the K-NN and Naive Bayes methods which produce the highest accuracy, namely the K-NN method with an accuracy of 96.18%. So it can be concluded that the K-NN method is more feasible to use to predict student graduation

Analisis Komparasi Algoritma Data Mining Naive Bayes, K-Nearest Neighbors dan Regresi Linier Dalam Prediksi Harga Emas

“…Merupakan suatu metode yang menggunakan algoritma supervised dimana hasil dari Query instance dapat diklasifikasikan berdasarkan mayoritas dari label class pada K-NN [1]. Algoritma K-NN bekerja berdasarkan jarak terpendek dari Query instance ke training data untuk menentukan K-NNnya dan merupakan salah satu cara untuk menghitung jarak dekat [12]…”

Deteksi Kegawatan Pasien Covid-19 Berbasis Android Menggunakan Algoritma C45

“…Informasi diperoleh melalui observasi data realtime dari webKaggle yang dapat di akses dari link UNCOVER COVID-19 Challenge | Kaggledan survei langsung pada Rumash Sakit Povinsi NTB. Informasi dianalisis untuk mendapatkan data yang dibutuhkan oleh pengguna [12].…”

Implementation of Exploratory Data Analysis and Artificial Neural Networks to Predict Student Graduation on-Time

“…For higher education institutions, these efforts are useful for identifying students who may not graduate and providing appropriate support to ensure their success (Pelima, Sukmana, & Rosmansyah, 2024). The benefit of graduation prediction is that the results can be used by universities to find useful patterns from large graduation data (Kartarina, Sriwinarti, & Juniarti, 2021;Qisthiano et al, 2021), which are used to predict future graduation outcomes (Basheer, Mutalib, Hamid, Abdul-Rahman, & Malik, 2019). By making predictions, the imbalance between student acceptance and graduation can be overcome, so that students who may not graduate on time can be identified.…”

“…It does not explain how the relationship between features affects students' on-time graduation. Meanwhile, the benefit of graduation prediction is to find patterns in large amounts of graduation data (Kartarina et al, 2021;Qisthiano et al, 2021). For this reason, before carrying out the prediction process, further analysis is needed on how the correlation between the features used affects students' on-time graduation.…”