scholarly journals Penggunaan Metode Naive Bayes Classifier untuk Mengevaluasi Kinerja Akademik Mahasiswa di Perguruan Tinggi

2021 ◽  
Vol 4 (2) ◽  
pp. 65
Author(s):  
Novitalia Novitalia ◽  
Putri Dinanti Mawasgenti ◽  
Tina Apriani ◽  
Ahmad Prayogi S. ◽  
Aries Saifudin
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Student Graduation ◽  
The Individual ◽  
The University

Inaccuracy in selecting faculties at universities is one of the constraints experienced by students which affect academic values or student performance which affects the accuracy of student graduation, in developing a performance it is necessary to know the individual talents of the students, this is the background of the application The Naive Bayes Classifier (NBC) Algorithm method in admitting new students to find out the talents and interests of students, with the NBC method it is expected that there will be an increase in the activity of students in higher education. The research that we do focuses on evaluating the success of administering a department at a university. Our research focuses on evaluating the success of administering a department at a university using the Naive Bayes Classifier (NBC) algorithm. Because the success of student academic performance is very dependent on the level of student ability to develop the knowledge they have. So that to evaluate the performance of students, a method is needed, namely the Naive Bayes Classifier (NBC) algorithm to analyze the level of student performance. The results of this study will show which are very influential on the provisions of a classification of a student's academic performance. The results can be based on the Achievement Index (IP) so that the results obtained by the method used can be used as evaluation material for the university or related students.

scholarly journals Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm

2020 ◽  
Vol 4 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Edi Sutoyo ◽  
Ahmad Almaarif
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Educational Data Mining ◽  
Naïve Bayes ◽  
Drop Out ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Student Graduation

The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735.

scholarly journals Evaluation of Student Academic Performance Using Naïve Bayes Classifier

2021 ◽  
Vol 1 (1) ◽  
pp. 46-52
Author(s):  
Khin Shin Thant ◽  
Ei Theint Theint Thu ◽  
Myat Mon Khaing ◽  
Khin Lay Myint ◽  
Hlaing Htake Khaung Tin
Keyword(s):  
Academic Performance ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Student Academic Performance

scholarly journals Pembangunan Aplikasi Klasifikasi Mahasiswa Baru untuk Prediksi Hasil Studi Menggunakan Naïve Bayes Classifier

2016 ◽  
Vol 7 (2) ◽  
Author(s):  
Mega Kartika Sari ◽  
Ernawati Ernawati ◽  
Irya Wisnubhadra
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Ve Bayes ◽  
C Programming Language ◽  
C Programming ◽  
The University ◽  
Learning Data

Abstract. Every university hopes to create the best potential graduates. Some of the efforts can be achieved by utilizing university data to be processed into information. The resulting information can help the university to determine the decisions to take in improving the students’ academic performance. One of the methods to process the data is by using Naive Bayes Classifier. This method requires some information such as GPA, average credits and attendance percentage. The prediction results are taken based on the data of the students at the university. Because there are large number of data to process it will require an information system that can classify data using Naïve Bayes Classifier. This application is built by using C# programming language with an average of 81,67% accuracy results (the accuracy depends on the data used). This application is expected to help the university to increase the achievements of its graduates.Keywords: Classification, Naive Bayes, Machine learning, Data mining Abstrak. Setiap universitas tentu berharap dapat meluluskan mahasiswa dengan prestasi terbaik. Usaha meningkatkan prestasi dapat diwujudkan dengan memanfaatkan data universitas untuk diolah menjadi informasi. Informasi yang dihasilkan dapat berguna untuk menentukan keputusan yang harus diambil pihak universitas dalam meningkatkan prestasi. Salah satu metode yang digunakan untuk memproses data ialah metode klasifikasi Naive Bayes. Metode ini menggunakan beberapa informasi seperti IPK, rata-rata sks dan persentase kehadiran mahasiswa sebagai data pelatihan. Data pelatihan tersebut digunakan untuk memprediksi IPK, rata-rata sks dan presentase kehadiran mahasiswa baru sebagai data uji. Adanya data yang cukup besar untuk diolah, maka dibutuhkan aplikasi klasifikasi mahasiswa baru menggunakan metode Naive Bayes Classifier. Aplikasi ini dibangun menggunakan bahasa pemrograman C# dengan rata-rata hasil akurasi 81,67%. Aplikasi ini diharapkan dapat membantu universitas dalam meningkatkan hasil prestasi akademik mahasiswa.Kata Kunci: Naive Bayes, Pembelajaran mesin, Penambangan data

A FRAMEWORK FOR STUDENTS’ ACADEMIC PERFORMANCE ANALYSIS USING NAÏVE BAYES CLASSIFIER

2015 ◽  
Vol 75 (3) ◽  
Author(s):  
Azwa Abdul Aziz ◽  
Nur Hafieza Ismail ◽  
Fadhilah Ahmad ◽  
Hasni Hassan
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Grade Point Average ◽  
Family Income ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Grade Point

Educational database of Higher Learning Institutions holds an enormous amount of data that increases every semester. Data mining technique is usually applied to this database to discover underlying information about the students. This paper proposed a framework to predict the performance of first year bachelor students in Computer Science course. Naïve Bayes Classifier was used to extract patterns using WEKA as a Data mining tool in order to build a prediction model. The data were collected from 6 year period intakes from July 2006/2007 until July 2011/2012. From the students’ data, six parameters were selected that are race, gender, family income, university entry mode, and Grade Point Average. By using Naïve Bayes Classifier, it would predict the class label “Grade Point Average” as a categorical value; Poor, Average, and Good. Result from the study shows that the students’ family income, gender, and hometown parameter contribute towards students’ academic performance. The prediction model is useful to the lecturers and management of the faculty in identifying students with weak performance so that they will be able to take necessary actions to improve the students’ academic performance.

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