Classification of the Final Project Utilized a Modified Naïve Bayes Algorithm

Abstract

The student is a part of the national aspiration which will be lead to future advancement. The student should has high integrity as a reflection of the academic ability which thereafter pursued their study at university. The science absorption during study measurable base on the academic grade. To proved how science has been accepted by the student, it could be measured from the student thesis. However, the relationship between the academic grade and the student research topic is mostly irrelevant. Mastery of science and research themes has no correlation, therefore the student's ability to accomplished the research was not properly. In order to map the research field, the coursework and student research field interest can be done by grade classification. The data classifier technique can use the simple Naive Bayes method with fairly accurate output. It could be an intake for the student to choose the research topic which suits their academic grade.

Analisis Perbandingan Kinerja Model Machine Learning untuk Memprediksi Risiko Stunting pada Pertumbuhan Anak

“…Implementasi ML telah banyak di terapkan untuk melakukan prediksi dalam berbagai kasus [13], [14]. Pada kasus stunting, beberapa metode ML yang telah digunaakan adalah metode DT [15], Naïve Bayes [16], [17], SVM [18], k-Nearest Neighbors [19], Neural Networks [20], [21], Random Forest [22], [23] dan Logistic Regression [24] [25]. Selain pada kasus stunting, ML juga banyak di terapkan pada bidang Kesehatan untuk memprediksi penyakit jantung, seperti Random forest [26], Logistic Regression [27], Naïve Bayes [28], SVM [29], dan Neural Networks [29], Penyakit diabetes : Random Forest [30], Neural Network [31], Penyakit MALCOM -Vol.…”