Komparasi Algoritma Decision Tree, Naive Bayes Dan K-Nearest Neighbor Untuk Memprediksi Mahasiswa Lulus Tepat Waktu

Abstract

Private Universities (PTS) compete so tight in providing performance in producing quality graduates. In addition, the number of universities in Indonesia which counts a lot both PTN and PTS makes the higher competition between universities as well. So the university strives to improve quality and provide the best education for service recipients, namely students, where one of the problems if there are some students who are late graduating or not on time so that it becomes an obstacle to the progress of the college. Prediction of students graduating on time is needed by university management in determining preventive policies related to early prevention of Drop Out (DO) cases. This prediction aims to determine the academic factors that influence the period of study and build the best prediction model with Data Mining techniques. There are 11 attributes used for Data Mining Classification, namely NPM, Gender, Age, Department, Class, Occupation, Semester 1 Achievement Index, Semester 2 Achievement Index, Semester 3 Achievement Index, Semester 4 Achievement Index and Information as result attributes. From the results of evaluations and validations that have been carried out using the RapidMiner tools the accuracy of the Decision Tree (C4.5) method is 98.04% in the 3rd test. The accuracy of the Naïve Bayes Method is 96.00% in the 4th test. And the accuracy of the K-Nearest Neighbor Method (K-NN) of 90.00% in the second test.

Menentukan Cluster Yang Tepat Dengan K-Means Dalam Rangka Mengukur Efektivitas Pelaksanaan Anggaran Pada Kementerian Agraria Dan Tata Ruang/Badan Pertanahan

“…Bussines Understanding atau pemahaman terhadap bisnis adalah suatu tahapan yang diawali dari memahami kebutuhan dan tujuan dalam sudut pandang penelitian secara menyeluruh. Mengenali masalah yang ada dan bagaimana cara menyelesaikan permasalahan tersebut agar dapat mencapai tujuan (Budiyantara et al, 2020).…”

Integration of Multiplatform Desktop, Web and Mobile Applications at Pt. Prima Artha Kreasindo Using Web Service

“…3. Studi Literatur, untuk memperoleh informasi serta referensi pada penelitian ini (Budiyantara et al, 2020)…”

Perbandingan Tingkat Akurasi Metode KNN Dan Decision Tree Dalam Memprediksi Lama Studi Mahasiswa

“…Temuan dari penelitian tersebut menunjukan tingkat akurasi metode Algoritma Decision Tree lebih baik dibandingkan dengan K-Nearest Neighbor. Hasil yang sama diperoleh pada penelitian [9] membandingkan metode data mining untuk memprediksi mahasiswa lulus tepat waktu dengan menggunakan tiga metode yaitu Decision Tree, Naive Bayes Classifier dan K-Nearert Neighbor. Secara berturut-turut diperoleh tingkat akurasi sebesar 98.04%, 96% dan 90%.…”