scholarly journals Sistem Pakar Dalam Diagnosa Penyakit Kanker Rahim Menggunakan Metode Naïve Bayes dan Certainty Factor

2021 ◽  
Vol 5 (1) ◽  
pp. 251
Author(s):  
Yendrizal Yendrizal
Keyword(s):  
Health Care ◽  
Cervical Cancer ◽  
Naive Bayes ◽  
Uterine Cancer ◽  
Reproductive Organs ◽  
Naïve Bayes ◽  
Uterine Disease ◽  
Certainty Factor ◽  
Bayes Method ◽  
The World

The uterus is one of the reproductive organs, namely the mouth of the uterus which is very susceptible to cancer and very often women experience cancer due to a lack of health care. suffered by women in the uterus can be like cysts, cervical cancer, uterine cancer, vaginal cancer and others, cancer is also very difficult to cure so that patients eventually have to give up and face death. From this it is necessary to make a diagnosis from the beginning in order to minimize the number of deaths caused by cervical cancer which is faced by many women in the world, especially in Indonesia, in diagnosing this disease can use a computer system in collaboration with experts to produce a system called the system. experts as an effort to help solve problems that occur with uterine disease that is being experienced by some women. The results of this study will show the percentage level of disease experienced by patients by 88% with the help of implementing the naïve Bayes method and certainty factory

scholarly journals Implementasi Sistem Pakar untuk Mendeteksi Virus Covid-19 dengan Perbandingan Metode Naïve Bayes dan Certainty Factor

2021 ◽  
Vol 5 (3) ◽  
pp. 338
Author(s):  
Rio Al Dzahabi Yunas ◽  
Agung Triayudi ◽  
Ira Diana Sholihati
Keyword(s):  
Naive Bayes ◽  
Medical Personnel ◽  
Naïve Bayes ◽  
Certainty Factor ◽  
Bayes Method ◽  
Web Based ◽  
Factor Method ◽  
Technological Advances ◽  
The World ◽  
Naive Bayes Method

The Covid -19 virus spread in the world, especially in Indonesia, very fast. This epidemic is of concern around the world because it has a quite bad impact in various sectors. With existing technological advances, the Expert System can assist medical personnel in detecting the Covid -19 Virus. The purpose of the author in conducting the study was to detect the Covid-19 virus as easily as possible with symptom data obtained from patients who had consultations. The Naïve Bayes method is a method that uses probability and statistics that can predict a person's chance of being exposed to Covid-19 in the future based on symptoms experienced in the previous period packed with a web-based program. For comparison, the author uses the Certainty Factor Method. Certainty Factor is a method that aims to determine the certainty value which is based on the previous calculation of CF value by manual calculation. The Naïve Bayes method can group the symptoms obtained from the official WHO website which has been given an indicator of the percentage of someone exposed to the Covid-19 Virus based on the symptom data experienced to determine a person exposed to the Covid-19 Virus. While the Certainty Factor method gets the confidence of someone exposed to the symptoms of the Covid-19 virus by using the calculation indicator on the CF value that has been consulted by the user, which can provide a percentage level of confidence that is 86%.Keywords:Expert System, Covid-19, Naive Bayes, Certainty Factor.

scholarly journals Development of Android-based Rabbit Disease Expert System

2018 ◽  
Vol 7 (4.44) ◽  
pp. 82
Author(s):  
Dyah Ayu Irawati ◽  
Yan Watequlis Syaifudin ◽  
Fabiola Ester Tomasila ◽  
Awan Setiawan ◽  
Erfan Rohadi
Keyword(s):  
Naive Bayes ◽  
Diagnostic System ◽  
Naïve Bayes ◽  
Certainty Factor ◽  
Bayes Method ◽  
Trust Value ◽  
User Input ◽  
Sample Data ◽  
Testing Data ◽  
Naive Bayes Method

Many rabbit keepers or breeders are panics when their rabbit has an illness. This paper proposed an expert diagnostic system application for Android-based rabbit disease using the Naïve Bayes method to determine the illness and Certainty Factor for the trust value of the condition by combining the rate of the trust of users and experts due to diagnose the diseases of the rabbit.The testing was using 65 data learning and 160 data learning to test the naïve Bayes method. Furthermore, the certainty factor is using CF user 1 and its variation.The results obtained for 65 data learning is 53%, while 160 data learning is 73%. With the naïve Bayes method, it can be concluded that the more data learning, the better and more accurate the system. The results of conformity with the testing data obtained from the variative CF user value, namely 53% accordingly, 13% inappropriate, 33% near. The effect of compliance with the sample data collected from the CF value of user 1 is 53% appropriate, 7% inappropriate, 40% is near. With the certainty factor method, it can be concluded that differences in user input values affect the overall CF value. 

scholarly journals Sistem Pakar Diagnosa Virus Corona Dengan Metode Naïve Bayes

2021 ◽  
Vol 9 (1) ◽  
pp. 81
Author(s):  
Fareza Aditiyanto Nugroho ◽  
Arif Fajar Solikin ◽  
Mutiara Dwi Anggraini ◽  
Kusrini Kusrini
Keyword(s):  
Expert System ◽  
Natural Disaster ◽  
Naive Bayes ◽  
Virus Disease ◽  
Diagnostic System ◽  
Naïve Bayes ◽  
The Body ◽  
Human Being ◽  
Bayes Method ◽  
The World

Humans being are faced with non-natural disasters which have bad effect for population on the world. This non-natural disaster is called Corona Virus Disease (COVID-19). This COVID-19 will become a pandemic in 2020. This types of COVID-19 is coming from the Orthocronavirinae. It belongs to the Coronaviridae and the Nidovirales. This type of that virus has caused some disease to birds, mammals and also human being. Therefore, the research was conducted. The result of this research will give the information about system which related the classification human being according to their transmission to the body. This research used naïve bayes method. The result of this research is diagnostic system with the level of accuracy 94%. Thus, COVID-19 diagnostic expert system used to know the level of COVID -19 infections to human being. It can help the user knowing the next treatment.Keywords : Expert System, Naïve Bayes, Coronavirus, Covid-19

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 INTERNET NETWORK CLASSIFICATION IN MALIKUSSALEH UNIVERSITY USING NAÏVE BAYES METHOD

2021 ◽  
Vol 5 (1) ◽  
pp. 48-53
Author(s):  
Eva Darnila ◽  
◽  
Zara Yunizar ◽  
Dhyra Gibran Alinda
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Browsing ◽  
Bayes Method ◽  
Test Information ◽  
The World ◽  
The Web ◽  
Web Systems ◽  
Internet Network

The utilize of web systems at this time is exceptionally critical, particularly for the world of instruction. In expansion to the significance of the web arrange, issues frequently emerge on the web arrange due to an expansive number of clients, the issues can be gotten at the UNIMAL campus incorporate moderate, harmed, and indeed not sent information to its goal since the organize activity isn't ideal. To be able to optimize the web organize by prioritizing organize activity. In this consider, the Credulous Bayes calculation is utilized for the classification handle of organized activity capture information. The application utilized to capture organize activity is the Wireshark application. Utilizing the Credulous Bayes calculation to watch the comes about of organizing test information through a calculation prepare that has tall precision. To be specific at the Workforce of Designing 95.73% with a likelihood of testing comes about is 0.00015946 web browsing, 0.00000007 downloads, 0.00008691 gushing, 0.00008497 social media, 0.00000014 floodings.

scholarly journals Penerapan Metode Certainty Factor Dan Naïve Bayes Untuk Mendiagnosa Penyakit Akibat Gigitan Nyamuk

2020 ◽  
Vol 4 (2) ◽  
pp. 49-60
Author(s):  
Yudo Bismo ◽  
Giofani Harsanto
Keyword(s):  
Expert System ◽  
System Application ◽  
Naive Bayes ◽  
Human Life ◽  
Naïve Bayes ◽  
Application Software ◽  
Certainty Factor ◽  
Bayes Method ◽  
Initial Symptoms ◽  
Calculation Results

Health is the main point for the expensive human life. However, many stakeholders ignore their health, which in the end the disease they suffer is too late to be diagnosed, thus reaching a chronic stage that makes it difficult to treat. Same is the case with mosquito bites. The poor behavior of stakeholders toward healthy living habits, especially littering in gutters or in rivers, causes mosquitoes to form colonies by making their nests and environment dirty, dirty and unsightly. The initial symptoms that often arise from the bite of a dengue mosquito, malaria and chikungunya are generally the same and difficult to distinguish. To overcome the above problems, it is necessary to build a system where the system can help stakeholders to diagnose diseases caused by mosquito bites on android. The research method used in this research is the Research and Development method, because the final result of this research is to produce a product in the form of an expert system application software to diagnose diseases caused based mosquito bites by android. The results obtained in this study are by applying the certainty factor method in the application of this expert system is able to provide the percentage calculation results in detecting diseases caused by mosquito bites, while the naïve bayes method is able to detect the type of mosquito. The accuracy of the expert system application that has been made is 90% in diagnosing diseases caused by mosquito bites.

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

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%.

scholarly journals Analisis Metode K-Nearest Neighbors (K-NN) Dan Naive Bayes Dalam Memprediksi Kelulusan Mahasiswa

2021 ◽  
Vol 3 (2) ◽  
pp. 107-113
Author(s):  
Kartarina Kartarina ◽  
Ni Ketut Sriwinarti ◽  
Ni luh Putu Juniarti
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Entry And Exit ◽  
Accuracy Rate ◽  
Bayes Method ◽  
K Nearest Neighbors ◽  
Bayes Methods ◽  
Accuracy Test ◽  
Student Graduation ◽  
Selection Of

In this research the author aims to apply the K-NN and Naive Bayes algorithms for predicting student graduation rates at Sekolah Tinggi Pariwisata (STP) Mataram, The comparison of these two methods was carried out because based on several previous studies it was found that K-NN and Naive Bayes are well-known classification methods with a good level of accuracy. But which one has a better accuracy rate than the two algorithms, that's what researchers are trying to do. The output of this application is in the form of information on the prediction of student graduation, whether to graduate on time or not on time. The selection of STP as the research location was carried out because of the imbalance between the entry and exit of students who had completed their studies. Students who enter have a large number, but students who graduate on time according to the provisions are far very small, resulting in accumulation of the high number of students in each period of graduation, so it takes the initial predictions to quickly overcome these problems. Based on the results of designing, implementing, testing, and testing the Student Graduation Prediction Application program using the K-NN and Naive Bayes Methods with the Cross Validation method, the result is an accuracy for the K-NN method of 96.18% and for the Naive Bayes method an accuracy of 91.94% with using the RapideMiner accuracy test. So based on the results of the two tests between the K-NN and Naive Bayes methods which produce the highest accuracy, namely the K-NN method with an accuracy of 96.18%. So it can be concluded that the K-NN method is more feasible to use to predict student graduation

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