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 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

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

scholarly journals PENGELOMPOKAN SEBARAN TRANSFORMATOR DISTRIBUSI BERDASARKAN KAPASITAS DAYA MENGGUNAKAN METODE NAÏVE BAYES Studi Kasus: PT. PLN RAYON KOTA SAMARINDA

2020 ◽  
Vol 8 (1) ◽  
pp. 36-44
Author(s):  
Sentikom 2019
Keyword(s):  
Naive Bayes ◽  
Electrical Energy ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Method ◽  
Voltage Level ◽  
Human Needs ◽  
Calculation Results ◽  
Industrial Needs ◽  
Naive Bayes Method

Human needs for energy are mostly obtained from electrical energy, both for daily needs and for industrial needs. PT. PLN (Persero) is one of the state electricity companies that serves the community's need for electricity. Transformer or better known as "transformer" or "transformer" is actually an electrical device that converts AC power at one voltage level to one voltage level based on the principle of electromagnetic induction without changing its frequency. Because of the lack of distribution of transformers around the Samarinda area, it can result in electricity demand services to the community. Therefore we need a method that can facilitate the distribution of PT. PLN Rayon Kota Samarinda, one of the methods is by applying Naïve Bayes. The purpose of this study is to facilitate the distribution in each region and the type of transformer used. The results of calculations using the Naïve Bayes method, the results obtained the probability of grouping training data is P (160) = 0.006441224, P (100) = 0.016304348, P (80) = 0.001610306, P (50) = 0.001610306, P (40) = 0.000402576, P P (20) = 0,000679348. From the calculation results, it appears that the probability value P (100) is more dominant, then 100 is recommended for real consumption which is used as training data. The Naïve Bayes method produces an accuracy rate of 92%.

Prediction of User Loyalty Using the Naive Bayes Method in the "Goprint" Online Printing Marketplace

2020 ◽  
Vol 8 (3) ◽  
pp. 227
Author(s):  
Gede Widiastawan ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Decision Making ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Test Results ◽  
Bayes Method ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Naive Bayes Method

Goprint is an Online Printing Marketplace that connects printing services with users who want to print documents quickly without the need to queue. In the span of time from April 2019 to September 2019 it was found that the number of Goprint users reached 407 users, 24 partners, and 256 orders. From transactions that have been carried out by users, not a few orders are often canceled due to ineffective Goprint features or poor partner performance. This causes Goprint users to feel dissatisfied with the services provided by the Goprint application. The Naive Bayes algorithm is one of the algorithms used for classification or grouping of data, but can also be used for decision making. With this algorithm and the problems that occur, the authors make a system to predict the loyalty of Goprint users to anticipate users who stop leaving Goprint because they are not satisfied or loyal users. The data used as training data is 20 and testing data is 10. From the test results it is found that the value of precision is 80%, 100% recall, and 90% accuracy.

scholarly journals Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia

2018 ◽  
Vol 5 (4) ◽  
pp. 427 ◽  
Author(s):  
Riri Nada Devita ◽  
Heru Wahyu Herwanto ◽  
Aji Prasetya Wibawa
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Bayes Method ◽  
K Nearest Neighbors ◽  
Naive Bayes Method ◽  
Main Factor

<p class="Abstrak">Kecocokan isi artikel dengan sebuah tema jurnal menjadi faktor utama diterima tidaknya sebuah artikel. Tetapi masih banyak mahasiswa yang bingung untuk menentukan jurnal yang sesuai dengan artikel yang dimilikinya. Untuk itu diperlukannya sebuah metode klasifikasi dokumen yang dapat mengelompokkan artikel secara otomatis dan akurat. Terdapat banyak metode klasifikasi yang dapat digunakan. Metode yang digunakan dalam penelitian ini adalah <em>Naive Bayes</em> dan sebagai <em>baseline </em>digunakan metode <em>K-Nearest Neighbor</em>. Metode <em>Naive Bayes </em>dipilih karena dapat menghasilkan akurasi yang maksimal dengan data latih yang sedikit. Sedangkan metode <em>K-Nearest Neighbor</em> dipilih karena metode tersebut tangguh terhadap data <em>noise</em>. Kinerja dari kedua metode tersebut akan dibandingkan, sehingga dapat diketahui metode mana yang lebih baik dalam melakukan klasifikasi dokumen. Hasil yang didapatkan menunjukkan metode <em>Naive Bayes </em>memiliki kinerja yang lebih baik dengan tingkat akurasi 70%, sedangkan metode <em>K-Nearest Neighbor </em>memiliki tingkat akurasi yang cukup rendah yaitu 40%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em>One way to be accepted in a journal conference and get the publication is to create an article with perfect suitability content of the journal. Matching the content of the article with a journal theme is the main factor for acceptability an article. But there are still many students who are confused to choose the journal in accordance with the articles it has. So we need a method to classification article documents category automatically and accurately group articles. There are many classification methods that can be used. The method used in this study is Naive Bayes and as a baseline the K-Nearest Neighbor method. Naive Bayes method is chosen because it can produce maximum accuracy with little training data. While K-Nearest Neighbor method was chosen because the method is robust to data noise. The performance of the two methods will be compared, so we can be known which method is better in classifying the document. The results show that the Naive Bayes method performs is more accurate with 70% accuracy and K-Nearest Neighbors method has a fairly low accuracy of 40% on classification test.</em></p>

scholarly journals PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA)

2020 ◽  
Vol 3 (1) ◽  
pp. 22-34
Author(s):  
Komang Aditya Pratama ◽  
Gede Aditra Pradnyana ◽  
I Ketut Resika Arthana
Keyword(s):  
Naive Bayes ◽  
Intelligent System ◽  
Naïve Bayes ◽  
The State ◽  
State University ◽  
Bayes Method ◽  
New Students ◽  
Computer Based ◽  
Entrance Test ◽  
Naive Bayes Method

Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)”. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.

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