scholarly journals Implementation Equal-Width Interval Discretization in Naive Bayes Method for Increasing Accuracy of Students' Majors Prediction

2018 ◽  
pp. 104 ◽  
Author(s):  
Alfa Saleh ◽  
Fina Nasari
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
K Value ◽  
Student Data ◽  
Positive Step ◽  
Selection Of ◽  
Naive Bayes Method

The Selection of majors for students is a positive step that is done to focus students in accordance with their potential, it is considered important because with the majors, students are expected to develop academic ability according to the field of interest. In previous research, Naive Bayes method has been tested to classify the student’s department based on the criteria that support the case study on Private Madrasah Aliyah PAB 6 Helvetia students and the accuracy of the test from 100 student data is 90%. in this study, the researcher developed a previously used method by applying an equal-width interval discretization that would transform numerical or continuous criteria into a categorical criteria with a predetermined k value, different k values ??would be tested to find the best accuracy value. from the 120-student data that have been tested, it is proved that the result of the classification of the application of equal-width interval discretization on the Naive Bayes method with the value of k = 8 is better and increased the accuracy value 91.7% to 93.3%.

scholarly journals Penggunaan Teknik Unsupervised Discretization pada Metode Naive Bayes dalam Menentukan Jurusan Siswa Madrasah Aliyah

2018 ◽  
Vol 5 (3) ◽  
pp. 353
Author(s):  
Alfa Saleh ◽  
Fina Nasari
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Test Results ◽  
Bayes Method ◽  
Test Result ◽  
Positive Step ◽  
Selection Of ◽  
Naive Bayes Method

<p class="Abstrak">Pemilihan jurusan bagi siswa merupakan langkah positif yang dilakukan untuk memfokuskan siswa sesuai dengan potensi yang dimiliki, hal ini dianggap penting karena dengan adanya jurusan, siswa diharapkan mampu mengembangkan kemampuan akademis sesuai bidang yang dikuasai. Pada penelitian sebelumnya, telah dilakukan pengujian dengan metode <em>Naive Bayes</em> yang bertujuan untuk mengkasifikasikan jurusan siswa bedasarkan kriteria yang menunjang dengan studi kasus pada siswa Madrasah Aliyah Swasta PAB 6 Helvetia, dan didapatkan hasil pengujian dari 100 data siswa dengan tingkat keakuratan 90%. pada penelitian ini, dilakukan optimalisasi metode yang digunakan sebelumnya dengan menerapkan teknik <em>Unsupervised Discretization</em> yang akan mentransformasikan kriteria numerik/kontinyu menjadi kriteria kategorikal dan mengeliminasi satu kriteria yang dianggap tidak mempengaruhi keakuratan hasil pengujian, dengan begitu keakurasian hasil klasifikasi dapat meningkat. Dari 120 data siswa yang diuji, terbukti bahwa hasil klasifikasi penerapan teknik unsupervised discretization pada metode naive bayes naik dari 90% menjadi 92.8%.</p><p class="Abstrak"> </p><p class="Abstrak">Abstract</p><p><em>Selection of majors for students is a positive step that is done to focus students in accordance with their potential, it is considered important because with the majors, students are expected to develop academic ability according to the controlled field. In previous research, Naive Bayes method has been tested to classify the students department based on the supportive criterias (case study on Madrasah Aliyah PAB 6 Helvetia), and the test result of 100 students data, the classification accuracy is about 90% . in this study, optimizaton is done with a method used earlier by applying Unsupervised Discretization techniques that would transform numerical / continuous criteria into categorical criteria and eliminating one criterion that is considered not affect the accuracy of test results. thus the accuracy of classification results could increase. 120 students data is tested, it is evident that the results of the classification of the application of unsupervised discretization techniques on the Naive Bayes method rose from 90% to 92.8%.</em></p>

Classification of Trends in Lecturer Research Fields Using Naive Bayes Method

2021 ◽  
Author(s):  
Sulthan Rafif ◽  
Pramana Yoga Saputra ◽  
Moch Zawaruddin Abdullah
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Research Fields ◽  
Naive Bayes Method

scholarly journals Naive Bayes Method for Classification of Student Interest Based on Website Accessed

2020 ◽  
Vol 1655 ◽  
pp. 012104
Author(s):  
Alwis Nazir ◽  
Amany Akhyar ◽  
Muhammad Ramadhani ◽  
Herlina
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Student Interest ◽  
Bayes Method ◽  
Naive Bayes Method

ANALISIS MODEL NAIVE BAYES UNTUK IDENTIFIKASI PENGGOLONGAN DAYA LISTRIK DI KOTA LHOKSUMAWE

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Muhammad Saidi ◽  
Fajriana Fajriana ◽  
Wahyu Fuadi ◽  
Ermatita Ermatita ◽  
Iwan Pahendra
Keyword(s):  
Naive Bayes ◽  
Electrical Power ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Method ◽  
Customer Data ◽  
Poor Households ◽  
House Area ◽  
Naive Bayes Method

Electricity subsidy is provided for all 450 VA power household customers and 900 VA power household customers who are poor and disadvantaged. However, there are many facts that household customers with 450 VA power are capable and 900 VA power household customers consist of capable households, boarding houses or luxury rented. Households are able to use more electricity than poor households. This paper describe to the identification of household customers' electrical power in the Lhokseumawe city to facilitate PLN in classifying customer power by using the Naive Bayes method. Naive bayes value variables used in this study are: monthly income, highest diploma, last job, house area, subscription fee and government registered household. The classification of household customer power is grouped into three categories, namely low (450 VA down), medium (900 VA) and high (above 1300 VA).. Based on household customer data that is used as training data, the Naive Bayes method is able to classify the customer data tested. So the Naive Bayes method successfully predicts the magnitude of the probability of household electrical power with an accuracy percentage of 80%.Keywords: Electricity, Naive Bayes,  CBS, low birth weight, subsidy

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