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

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 Application Design of Dengue Hemorrhagic Fever Patients Screening Using Naive Bayes Method

2017 ◽  
Vol 34 ◽  
pp. 20-28
Author(s):  
Cindy Astelia Ramadhan Suparman ◽  
Endah Purwanti ◽  
Prihartini Widiyanti
Keyword(s):  
Dengue Hemorrhagic Fever ◽  
Naive Bayes ◽  
Public Health Problem ◽  
Hemorrhagic Fever ◽  
Naïve Bayes ◽  
Training Data ◽  
Design System ◽  
Design Calculation ◽  
Bayes Method ◽  
Naive Bayes Method

Dengue Hemorrhagic Fever is a disease which is endemic in most districts / cities still becomes a public health problem in Indonesia. The awareness of people to the dengue viral infection and its symptoms are needed to decrease the fatality of this disease. The community need to be known the symptoms thereby they could intervened and prevent from falling in to worse condition. This study was conducted to design system which could diagnose the onset of the disease with 3 levels of possibilities namely Grade 1 Dengue Hemorrhagic Fever, Grade 2 Dengue Hemorrhagic Fever, and Non Dengue Hemorrhagic Fever. The system is build based on patient medical records of Dr. Wahidin Sudiro Husodo General Hospital, Mojokerto, East Java using the Naive Bayes method. The method of this study including several steps such as collecting data, preprocessing data, designing database, interface design, calculation and processing data, classification and analyzing data and evaluating application. Determining the results of the application diagnose requires posterior calculation which searches the highest values in three degrees as the results of the initial diagnose. The application as a device for an early diagnosis of dengue hemorrhagic fever has a high accuracy value of 97% out of the 30 tested data. The homogenization of the training data and the test data by sex and age can be considered in future research.

scholarly journals Application Of Naïve Bayes to Predict the Potential of Rain in Ternate City

2021 ◽  
Vol 328 ◽  
pp. 04011
Author(s):  
Alwin Ali ◽  
Amal Khairan ◽  
Firman Tempola ◽  
Achmad Fuad
Keyword(s):  
Test Data ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Method ◽  
Calculation Results ◽  
Bayes Algorithm ◽  
Correct Data ◽  
Rain Data ◽  
Naive Bayes Method

The amount of rainfall that occurs cannot be determined with certainty, but it can be predicted or estimated. In predicting the potential for rain, data mining techniques can be used by classifying data using the naive Bayes method. Naïve Bayes algorithm is a classification method using probability and statistical methods. The purpose of this study is how to implement the naive Bayes method to predict the potential for rain in Ternate City, and be able to calculate the accuracy of the Naive Bayes method from system created. The highest calculation results with new data with a total of 400 training data and 30 test data, obtained 30 correct data with 100% precision, 100% recall and 100% accuracy and the lowest calculation results with new data with a total of 500 training data and 50 test data, obtained 38 correct data and 12 incorrect data with a percentage of precision 61.29%, recall 100% and accuracy 76%.

scholarly journals Perbandingan Naïve bayes dan Certainty factor pada Sistem Pakar Untuk Mendiagnosa Dini Penyakit Glaukoma

2020 ◽  
Vol 5 (3) ◽  
pp. 291
Author(s):  
Hanif Rahman Burhani ◽  
Iskandar Fitri ◽  
Andrianingsih Andrianingsih
Keyword(s):  
Expert System ◽  
Naive Bayes ◽  
Comparison Method ◽  
Naïve Bayes ◽  
Training Data ◽  
Certainty Factor ◽  
Bayes Method ◽  
Lack Of Information ◽  
Statistics And Probability ◽  
Naive Bayes Method

Glaucoma is an eye disease that causes the second largest blindness after cataracts, this disease can cause decreased vision and can even be fatal, namely permanent blindness if it is not realized and treated immediately. Lack of information and education to the public to always maintain eye health is the basis for the purpose of making this expert system which aims to provide early diagnosis to people who are indicated to have glaucoma based on the symptoms or characteristics previously felt. The Naïve bayes method is a method that uses statistics and probability in predicting a person's chance of suffering from glaucoma based on the symptoms previously felt. It is made based on a website with PHP as the programming language and uses MySQL for the database. As for the comparison method used is the Certainty factor, which is a method that functions to determine a certainty value based on the calculation of the predetermined CF value by applying manual calculations. In the Naïve bayes method, the application can group symptom data and types of disease and can diagnose based on previous training data, while for the Certainty factor method based on the calculation of the value of the expert and the CF value that has been inputted by the user, it can produce a percentage of the diagnosis of the disease glaucoma in 96%.Keywords:Certainty factor, Expert System, Glaucoma, MySQL, Naïve bayes, PHP.

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

scholarly journals Penerapan Algoritma Naïve Bayes Dalam Penentuan Kelayakan Penerima Kartu Jakarta Pintar Plus

2020 ◽  
Vol 6 (1) ◽  
pp. 15-20
Author(s):  
Endah Widya Ningsih ◽  
Hardiyan Hardiyan
Keyword(s):  
Smart Card ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Method ◽  
Using Data ◽  
Naive Bayes Method

The eligibility of Jakarta Smart Card Plus recipients is still not on target due to subjective receipts. Schools has an important role in making decisions about the eligibility of Jakarta Smart Plus Card recipients. Therefore, the authors make this research using data that already exists or is called training data. The author uses the Naïve Bayes method with 7 independent attributes to knowing eligibility. The author also uses the another application  Rapidmined 5.3 to test the accuracy of the Naïve Bayes method. The result of this research the accuracy of determining the eligibility of Jakarta Smart Plus Card recipients are good with 98.88% with an error of presentation 2.22%.  So it can be concluded that the naive bayes method can help detrermine the eligibility of jakarta smart plus card recipients. Keywords: Jakarta Smart Card, Naïve Bayes, eligibility

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