scholarly journals Web-Based System for Bali Tourism Sentiment Analysis during The Covid-19 Pandemic using Django Web Framework and Naive Bayes Method

2022 ◽  
Author(s):  
Gede Aditra Pradnyana ◽  
I Gede Mahendra Darmawiguna
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Web Based ◽  
Web Framework ◽  
Web Based System ◽  
Naive Bayes Method

scholarly journals Expert System for Diagnosing Human Skin Diseases Using Web-Based Naïve Bayes Method

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Khoirunnisa devita Sari ◽  
Ade Eviyanti
Keyword(s):  
Expert System ◽  
Human Skin ◽  
Skin Disease ◽  
Skin Diseases ◽  
Naive Bayes ◽  
Expert Knowledge ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Web Based ◽  
Naive Bayes Method

Skin disease is a disease that often found in tropical countries like Indonesia. According to the survey, skin disease is the third of the ten most outpatient diseases. Lack of public knowledge about skin diseases and how to prevent and treat them can cause a person to develop acute skin diseases. The purpose of this research is to create an expert system application for diagnosis of human skin diseases using the web-based naïve Bayes method. With expert system, it hoped that human skin diseases can be detected early and can minimize the occurrence of more dangerous diseases. The calculation in this expert system uses the naïve Bayes method. This expert system makes diagnosis by analyzing input of symptoms experienced by patient and then processing it using certain rules according the expert knowledge that has been stored in the knowledge base. The result of this research is to build an expert system for diagnosing human skin diseases using website-based naïve Bayes. The results of the system trial of 20 respondents were the website could provide diagnosis results based on the inputted rules and could diagnose skin diseases properly. This website can used as an alternative use of technology so it can be used to diagnose skin diseases quickly, precisely and accurately. So in the future the handling of skin diseases can be faster and more efficient.

Sentiment Analysis of the Enforcement of PSBB Part II in Jakarta

2020 ◽  
Vol 9 (2) ◽  
pp. 259
Author(s):  
Gede Putra Aditya Brahmantha ◽  
I Wayan Santiyasa
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Crawling ◽  
K Nearest Neighbor ◽  
Bayes Method ◽  
The Public ◽  
Naive Bayes Method

In addition to communicating, Social Media is a place to issue opinions by the public on many things that are currently taking place, Twitter is one of these social medias that is widely used in conveying opinions regardless of whether these opinions are negative, positive, or even neutral. Tweets data about the Enforcement of PSBB Part II in Jakarta were obtained as many as 200 opinions using web crawling then advanced to the preprocessing stage before being classified using the K-Nearest Neighbor and Multinomial Naive Bayes algorithms. In 3 tests, the highest accuracy was 65.00% for K-Nearest Neighbor and the highest accuracy was 85.00% for Multinomial Naive Bayes method.

scholarly journals Sistem Pakar Diagnosa Gangguan Hiperaktif Pada Anak Dengan Metode Naive Bayes Berbasis Web

2019 ◽  
Vol 3 (2) ◽  
pp. 90
Author(s):  
Yunia Ervinaeni ◽  
Aziz Setyawan Hidayat ◽  
Eri Riana
Keyword(s):  
Expert System ◽  
System Application ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Web Based ◽  
The Public ◽  
Hyperactivity Disorder ◽  
Psychiatric Problems ◽  
Naive Bayes Method

Disorder concentration attention or better known as ADHD (Attention Deficit Hyperactivity Disorder) is one of the main psychiatric problems that are often found in children. Hyperactive disorders are usually seen in children and as we get older the more difficult to deal with. There were 3 hyperactive disorders taken in this study namely hyperactivity, Impulsivity, Inattetion (Personality Problems). To help diagnose these hyperactive disorders, an expert system application is made to diagnose hyperactive disorders in children that can facilitate the public in diagnosing hyperactive disorders in children. Making this web-based expert system application uses the Naive Bayes method. The Naive Bayes method is a simple opportunity classification based on the application of the Bayes theorem with the assumption that between variables (independent) the presence or absence of a particular event from a group is not related to the presence or absence of other events. The final results are given in the form of a percentage of the diagnosis of hyperactivity in children.

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 Sentiment Analysis of Novel Review Using Long Short-Term Memory Method

2019 ◽  
Vol 13 (3) ◽  
pp. 209
Author(s):  
Muh Amin Nurrohmat ◽  
Azhari SN
Keyword(s):  
Sentiment Analysis ◽  
Short Term Memory ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Short Term ◽  
Bayes Method ◽  
Text Data ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Naive Bayes Method

The rapid development of the internet and social media and a large amount of text data has become an important research subject in obtaining information from the text data. In recent years, there has been an increase in research on sentiment analysis in the review text to determine the polarity of opinion on social media. However, there are still few studies that apply the deep learning method, namely Long Short-Term Memory for sentiment analysis in Indonesian texts.This study aims to classify Indonesian novel novels based on positive, neutral and negative sentiments using the Long Short-Term Memory (LSTM) method. The dataset used is a review of Indonesian language novels taken from the goodreads.com site. In the testing process, the LSTM method will be compared with the Naïve Bayes method based on the calculation of the values of accuracy, precision, recall, f-measure.Based on the test results show that the Long Short-Term Memory method has better accuracy results than the Naïve Bayes method with an accuracy value of 72.85%, 73% precision, 72% recall, and 72% f-measure compared to the results of the Naïve Bayes method accuracy with accuracy value of 67.88%, precision 69%, recall 68%, and f-measure 68%.

scholarly journals Design of Expert System for Diagnosis of Gonorrhea Disease by using Web-Based Naïve Bayes Method

2021 ◽  
Vol 3 (2) ◽  
pp. 223-233
Author(s):  
Teri Ade Putra ◽  
Raja Ayu Mahessya ◽  
Pradani Ayu Widya Purnama ◽  
Reza Suriadinata
Keyword(s):  
Expert System ◽  
System Application ◽  
Naive Bayes ◽  
Transmitted Disease ◽  
Naïve Bayes ◽  
The Body ◽  
Bayes Method ◽  
Web Based ◽  
The Public ◽  
Naive Bayes Method

Gonorrhea is a sexually transmitted disease caused by the bacterium Neisseria gonorrhea that infects the lining of the bladder, cervix, rectum, throat, and the whites of the eyes. This disease is spread through the bloodstream to other parts of the body, especially the skin and joints and is generally transmitted through sexual contact. Gonorrhea not only affects adult men and women, but babies and even newborns can get gonorrhea from their mothers. An expert system is a system that seeks to adopt human knowledge into computers, so that computers can solve problems like an expert. With this expert system, the public can obtain information or can solve the problems they face which can only be obtained with the help of experts in their fields. This study explains how the expert system diagnoses Gonorrhea using the Naïve Bayes method. By using the Naïve Bayes method, it can predict future probability values based on previous experiences experienced by people with gonorrhea, Document classification can be personalized, tailored to the needs of each person. By using this expert system application, it can provide information and make it easier for the public to be more familiar with Gonorrhea and can handle problems based on the expertise of doctors who are experts in their fields. This expert system can provide solutions and prevention of gonorrhea disease with the diagnosis process carried out efficiently and save time in helping the community in the prevention that occurs. This web-based expert system application is built with the PHP programming language and MySQL data storage.

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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