Analysis Of Twitter Sentiment Using The Classification Of Naive Bayes Method About Television In Indonesia

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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Karawo Motifs Identification based on The Classification of User Characters with Naïve Bayes Method

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/180/1/012240 ◽

2017 ◽

Vol 180 ◽

pp. 012240

Author(s):

M H Koniyo ◽

S Lamusu ◽

L Hadjaratie ◽

A A Bouty

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Bayes Method ◽

Naive Bayes Method

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Kombinasi Metode Correlated Naive Bayes dan Metode Seleksi Fitur Wrapper untuk Klasifikasi Data Kesehatan

Jurnal Teknik Elektro ◽

10.15294/jte.v11i2.23693 ◽

2020 ◽

Vol 11 (2) ◽

pp. 50-55

Author(s):

Hairani Hairani ◽

Muhammad Innuddin

Keyword(s):

Feature Selection ◽

Performance Test ◽

Naive Bayes ◽

Health Data ◽

Naïve Bayes ◽

Bayes Method ◽

Heart Attacks ◽

Bayes Methods ◽

Naive Bayes Method

Most features of health data that have many irrelevant features can reduce the performance of classification method. One health data that has many attributes is the Pima Indian Diabetes dataset and Thyroid. Diabetes is a deadly disease caused by the increasing of blood sugar because of the body's inability to produce enough insulin and its complications can lead to heart attacks and strokes. The purpose of this research is to do a combination of Correlated Naïve Bayes method and Wrapper-based feature selection to classification of health data. The stages of this research consist of several stages, namely; (1) the collection of Pima Indian Diabetes and Thyroid dataset from UCI Machine Learning Repository, (2) pre-processing data such as transformation, Scaling, and Wrapper-based feature selection, (3) classification using the Correlated Naive Bayes and Naive Bayes methods, and (4) performance test based on its accuracy using the 10-fold cross validation method. Based on the results, the combination of Correlated Naive Bayes method and Wrapper-based feature selection get the best accuracy for both datasets used. For Pima Indian Diabetes dataset, the accuracy is 71,4% and the Thyroid dataset accuracy is 79,38%. Thus, the combination of Correlated Naïve Bayes method and Wrapper-based feature selection result in better accuracy without feature selection with an increase of 4,1% for Pima Indian Diabetes dataset and 0,48% for the Thyroid dataset.

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Classification of Balinese Traditional Clothing Using Naïve Bayes

JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) ◽

10.24843/jlk.2021.v09.i04.p11 ◽

2021 ◽

Vol 9 (4) ◽

pp. 533

Author(s):

Guruh Johan Rinaldi ◽

I Wayan Santiyasa

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Religious Activities ◽

Bayes Method ◽

Cultural Products ◽

Naive Bayes Method

Bali is an area rich in cultural products, one of the cultural products that attracts attention is traditional Balinese clothing. Balinese traditional clothing is an important cultural product for Balinese people themselves because it is used in religious activities or traditional activities in Bali. Dataset of Balinese traditional clothing obtained as many as 26 pieces by direct survey to several places of makeup as well as from the book “Busana Adat Bali”. Furthermore, the data will be classified using the Naive Bayes method. In two experiments, there was an equally large accuracy of 66.66% using the Naïve Bayes method.

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

Lontar Komputer Jurnal Ilmiah Teknologi Informasi ◽

10.24843/lkjiti.2018.v09.i02.p05 ◽

2018 ◽

pp. 104 ◽

Cited By ~ 1

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%.

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