scholarly journals Comparison between Naïve Bayes and certainty factor to predict big five personality

2019 ◽  
Vol 1402 ◽  
pp. 077030
Author(s):  
U Syaripudin ◽  
R Zaenal ◽  
M F A Duri ◽  
E Firmansyah ◽  
A Rahman
Keyword(s):  
Big Five ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Big Five Personality ◽  
Certainty Factor

scholarly journals Text Analysis of Applicants for Personality Classification Using Multinomial Naïve Bayes and Decision Tree

2020 ◽  
Vol 12 (3) ◽  
Author(s):  
Nanda Yonda Hutama ◽  
Kemas Muslim Lhaksmana ◽  
Isman Kurniawan
Keyword(s):  
Decision Tree ◽  
Personality Traits ◽  
Big Five ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Big Five Personality ◽  
Text Data ◽  
Personality Classification ◽  
Best Parameters

Employees' qualities affect companies' performances and with a large number of applicants, it's difficult to find suitable applicants. To help with it, companies carry out psychological tests to know applicants' personalities, since personality's considered to have a relationship with work performances. But psychological testing requires a lot of effort, cost, and human resources. Thus with a system that can classify personalities through text can help reduce the effort needed. Similar studies carried out with the big five personalities as the theoretical basis and used one of the personality traits, namely using the k-NN method with 65% accuracy. Based on these studies, accuracy can improve by finding the best parameters using all of the big five personalities. This research is conducted based on the big five personality traits and related traits, namely consciousness and agreeableness. The data used is text data that's been labelled, pre-processed and feature selected. The clean text data is used to create a classification model using multinomial Naive Bayes and decision trees. There are 6 models built based on 3 work cultures, decision tree with an accuracy of 33%, 66%, 80%, and multinomial naïve Bayes with an accuracy of 83%, 50%, 60%, which resulted as better performance.

scholarly journals ANALISIS PERBANDINGAN TIGA METODE UNTUK MENDIAGNOSA PENYAKIT MATA PADA MANUSIA

2021 ◽  
Vol 8 (4) ◽  
pp. 1654-1664
Author(s):  
Ahmad Fahmi Adam
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Certainty Factor ◽  
Case Based

Untuk mendiagnosa penyakit mata pada manusia diperlukan perhitungan probabilitas yang terbaik. Karena mata merupakan salah satu bagian terpenting pada tubuh manusia yang harus di jaga kesehatannya. Penelitian ini bertujuan untuk menganalisis perbandingan dari 3 metode diantaranya : metode Case-Based Reasoning, Naïve Bayes dan Certainty Factor sehingga bisa diketahui metode mana yang terbaik untuk melakukan pendiagnosaan. Setelah melakukan perbandingan, untuk perhitungan metode Case-Based Reasoning didapatkan hasil probabilitas 61,6 %, metode Naïve Bayes didapatkan hasil 56,36% dan metode Certainty Factor didapatkan hasil 90,4%. Dapat disimpulkan, metode Certainty Factor adalah metode yang terbaik untuk melakukan pendiagnosaan penyakit mata pada manusia. Setelah itu, akan dibuatkan suatu sistem pakar menggunakan metode Certainty Factor untuk mendiagnosa penyakit mata pada manusia. Sistem pakar merupakan peniru suatu pakar dalam melakukan diagnosis suatu penyakit. Tujuan dibuatkan sistem pakar ini, supaya dapat membantu pasien untuk mendiagnosa jenis penyakit mata apa berdasarkan gejala gejala yang dialaminya.

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 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.

Knowing Personality Traits on Facebook Status Using the Naïve Bayes Classifier

2020 ◽  
Vol 2 (1) ◽  
pp. 22
Author(s):  
Mohammad Zoqi Sarwani ◽  
Muhammad Shubkhan Salafudin ◽  
Dian Ahkam Sani
Keyword(s):  
Social Media ◽  
Big Five ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Surrounding Environment ◽  
Testing Data

With the development of social media trends among students by using Facebook social media, students can communicate and pour out everything that is felt in the form of status. Personality is the character or various characters of a person - therefore, how a person to adjust to the surrounding environment for the achievement of communication smoothly. In the personality category, many things classify a person's category in the psychologist theory. In this exercise, the Big Five, the psychologist theory, is described in five codes, namely Openness, Conscientiousness, Extraversion, Agreeables, Neuroticism. Naive Bayes Classifier is used to determine the highest probability value with the aim to determine the highest value. The data used are two namely training data and testing data obtained from the Facebook status of students. From the data obtained can be tested in the system that the accuracy value is 88%.

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 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

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