Bayes Classifier Method Research Articles

Pengukuran Sentimen Sosial Terhadap Teknologi Kendaraan Listrik: Bukti Empiris di Indonesia

Currently, climate change due to global warming is a concern for many parties. The greenhouse gas emissions level is increasing day by day. The major contributors to Air pollution are the greenhouse effect. Transportation accounts for about 27% of air pollution, and governments in various countries use electric vehicles to reduce air pollution. However, the success of using electric vehicles is depending on perception, people’s sentiment, and understanding. The main purpose of this research is to find out how public sentiment towards electric vehicles is through tweets and comments on the Twitter social media platform using sentiment analysis. The data obtained are 1084 tweets and comments. The data were classified using the Naïve Bayes method, K-Nearest Neighbor, and Decision Tree. The results showed that the Naïve Bayes Classification Method gave better results than K-Nearest Neighbor, and Decision Tree with an accuracy rate of 94% positive sentiment by 53%, negative sentiment by 38%, and neutral sentiment by 9%. So, it can be concluded that public sentiment towards electric vehicles is quite good based on conversations on the Twitter social media platform. In addition, the author also visualizes the results of the analysis in the form of graphs and word clouds so that they can help the electric vehicle industry players to understand public sentiment better and more accurately.

Sistem Pakar Diagnosis Masalah Kulit Yang Berbasis Web Dengan Metode Naive Bayes Classifier

As we all know, the world of technology and information has become very advanced in recent years. This has led to numerous new ideas that make it easier for a person or organization to do their work and achieve their goals. Progress in technology and information greatly affects all areas, from culture and education to the economy, health, and even beauty. So, in this study, the researcher brought up a problem at a beauty clinic in Maguwoharjo, DIY, called Emila Aesthetic Solution. The problem is that consulting with patients is still done face-to-face. In other words, the clinic does not have a computerized system to help with consultations so that patients do not have to wait in line and can get treatment right away based on the results of their consultation. As a result, this study focused on developing an expert system to diagnose skin problems using the naïve Bayes classifier method.In this study, the system was made using the naïve Bayes classifier and the waterfall model for system development. The model was then coded into the system to be used. The goal of making this expert system was to make it easier for patients to get advice and for the clinic to handle patients.Based on the patient history data obtained, namely 20 patient data that experts have tested, this expert system, by implementing the naïve Bayes classifier method, yielded results indicating that the system's accuracy was 100 percent.

Perancangan Sistem Klasifikasi Wilayah Layanan Samsat Keliling Yang Berpotensi Menggunakan Metode Naïve Bayes Classification

This research builds a classification system for the potential service area of Samsat around Mataram. SAMSAT Mobile is a type of service at the joint office of SAMSAT Mataram which is a Technical Implementation Unit (UPT). samling can make it easier for people to make payments without the need to go to the Mataram SAMSAT office, but in the past year 4 service units owned by SAMSAT Mataram have provided services at the same place and there are several mobile SAMSATs that do not meet the target the place still has potential or there are other places that are more potential than the places that have been occupied by means of classification. Therefore, it is necessary for the application of the Potential Mobile SAMSAT Service Area Classification System Design Using the Naive bayes Classification Method. Naive Bayes is a classification method that is known to be simple but has high accuracy and to build this system requires python programming language with the Django framework. The system created can be used to identify areas that still have the potential to provide Mobile SAMSAT services

Analisis Sentimen Masyarakat Terhadap Virus Corona Berdasarkan Opini Masyarakat Menggunakan Metode Naïve Bayes Classifier

The current use of social media is not only to communicate between friends, but is often also used as a means to convey an aspiration to the community, especially the Indonesian people regarding government issues, or problems related to health and other problems. One of the uses of this social media is to use it as a means of conveying digital aspirations, such as some slogans that are used as hashtags, namely #dirumahaja #lockdown, #usemasker, #protocol, #imun, #vaccine. From the slogan used as a hashtag, researchers are interested in conducting research on how much negative sentiment and positive sentiment there are, using the Naïve Bayes Classifier method, which is a machine learning method that uses probability calculations. The basic concept used by Nave Bayes is the Bayes Classifier Theorem, which is a theorem in statistics to calculate probability, the Bayes Optimal Classifier calculates the probability of one class from each existing attribute group, and determines which class is the most optimal, as for the advantages of using Nave Bayes Classifier in document classification can be viewed from the process that takes action based on existing data to provide solutions to these sentiments.

Sentiment Analysis of Technology Utilization by Pekanbaru City Government Based on Community Interaction in Social Media

Government services for the public are currently utilizing technology, especially in the city of Pekanbaru. The government has currently centralized all services for the public, both online and offline, in public service malls. The type of service that uses technology, especially for online services, has received criticism in online media such as Twitter. To see the public's response to Pekanbaru city government services, especially in terms of technology, this study will use sentiment analysis to see positive, negative, and neutral comments. The method used is to see the accuracy generated using the Naïve Bayes Classifier (NBC) method. Bayes classifier is a statistical classifier, where the classifier can predict the probability of class membership of a data tuple that will fall into a certain class, according to the probability calculation. Accuracy results are obtained by dividing training data and testing data with a comparison of 70%:30% with an accuracy value of 55.56%, Precision 64%, recall 80%, f-score 71.2%.

Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device

The need for monitoring of children today is very important, especially for children under five, whose physical and verbal abilities are still inadequate to be able to communicate effectively with their parents or caregivers about the conditions they are experiencing. Previous studies related to child safety that were studied generally focused on responses to events that could potentially harm children.This study aims to design a prototype child monitoring system consisting of a wearable device that is used on a child's wrist equipped with sensors and connected to a server via a wireless network. Monitoring software that runs on the server will collect all data parameters from wearable devices with built-in audio signal, temperature, and heart rate sensor then with machine learning algorithm implemented in software will allow the system to predict if stress conditions happen on children and then system can give warnings to child-caregiver through monitoring applications or SMS messages. Using the Decision Tree and Naive Bayes classification methods the system can effectively predict stress conditions in children with an accuracy of 82.8 percent using audio, temperature, and heart rate parameters. This shows that the system has the capability to contribute to increasing child safety in the supervision environment.

Visualisasi dan Analisa Data Penyebaran Covid-19 dengan Metode Klasifikasi Naïve Bayes

The covid-19 virus became a pandemic in 2020. The spread of covid cases has hit the whole world, reaching 63 million cases in 190 countries as of November 2020. Information regarding the spread of covid is necessary for the general public. This research will produce a system that can provide information on the geographic distribution of covid cases. The data on the distribution of covid cases in this study were also used to analyze the classification using the Naive Bayes Classifier method. The Naive Bayes Classifier method works by using probability calculations so that this research can be used to classify the covid status in an area. The results of this study have succeeded in providing information on the status of the covid pandemic based on data on covid cases that have occurred around the world. Covid case data becomes training data for the analysis of the Naive Bayes classifier method so that it can determine the status of the Covid pandemic based on test data provided by system users. This research has succeeded in helping users to know the status of the Covid pandemic in an area well because it has reliable training data.Keywords:System, Covid, Naïve Bayes Classifier.

Sentiment Visualization of Covid-19 Vaccine Based On Naive Bayes Analysis

COVID-19 is one of the topics that is being discussed intensively. The virus which was declared a global pandemic on March 11 by WHO caused around 2.09 million Indonesians to be infected with the COVID-19 virus. To overcome this, the government carried out a vaccination program. The data taken for this study is public opinion about the COVID-19 vaccine written on Twitter. The number of opinions written on Twitter requires classification according to the sentiments they have, whether they tend to be negative opinions or positive opinions using lexicon-based The idea of this research is to classify the covid vaccination dataset using the naive Bayes classifier method and visualization using word cloud. Crawling to obtain the dataset from Twitter, text pre-processing and labelling to determine the positive and negative classes, TFIDF feature extraction, data splitting with a percentage of 80% for train data and 20% for data testing, and finally classification using nave Bayes are the stages in this research The system's sentiment analysis research yielded significant results, the accuracy value is 73.1%, the precision value is 73% and the recall value is 83%.

PRISMA Method For Predicting Breast Cancer

Breast cancer is one of the most common types of cancer in Indonesia. There are no definite symptoms that can be accurately diagnosed to detect breast cancer. Several studies on breast cancer have been carried out with various methods, but not all methods can predict breast cancer accurately. So the contribution of this research is to find a method that is accurate enough to detect breast cancer. This study uses the PRISMA method to find the right method for detecting breast cancer. The results show that the Naïve Bayes Classifier method is most often used because this algorithm is known to have a high level of accuracy with simple calculations and works very well compared to other classifier models. Another advantage is that this method only requires a small amount of training data to determine the parameter estimates needed in the classification process.
 Keywords : Breast Cancer, PRISMA, Naïve Bayes Classifier.

Topic Classification of Islamic Question and Answer Using Naïve Bayes and TF-IDF Method

Information spread through the internet is widely used by people to find anything. One of the most searched information on the internet is information related to Islamic religious knowledge. However, the large amount of information available from various sources makes it difficult for people to find the correct information. Previous researchers have researched this topic, but the dataset used only comes from one source. Therefore, in this study, a classification system for Islamic question and answer topics was built using the Naïve Bayes and TF-IDF methods. This study using 1000 question and answer article data taken from Islamic consultation websites, namely rumahfiqih.com and islamqa.info. The multi-class classification uses five categories which are manually labeled using the category classes on the website. From several test scenarios in this study, the Naïve Bayes classification method using TF-IDF (n-gram level) with a maximum feature of 1000 at a data separation ratio of 70:30 produces the highest accuracy of 81%. The 81% accuracy value was also generated by the SVM classification method, but the difference was in the SVM the highest accuracy value using TF-IDF (word level). It is expected that in the subsequent research will be used more website sources and the use of other classification and feature extraction methods with more optimal value than previous research.

Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter

Twitter is one of the social media that is currently in great demand by internet users. The number of tweets circulating on Twitter is not yet known whether these tweets contain more positive, negative, and neutral opinions. For that we need a system that can process data by applying sentiment analysis. This study uses the Naive Bayes Classifier (NBC) method to analyze the level of sentiment towards data carried out by crawling on Twitter. The data studied as a simple case study uses only 8 tweet data which is divided into 5 training data and 3 test data. The data is processed using the preprocessing stage, then classified using the NBC method, the calculation of performance uses confusion matrix techniques. This study resulted in a structured exposure to the process and results of NBC implementation and performance testing using the confusion matrix which obtained 82% accuracy, 93% precision, and 52% recall. However, these results are more focused on ease explaining for each stage and process in more detail, not on the numbers obtained. Research with larger data will be carried out later by developing a computer-based application system.

Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

In the era of globalization, the internet has become a human need in doing various things. Many internet users are an opportunity for internet service providers, PT Telekomunikasi Indonesia (Telkom). One of PT Telkom's products is IndiHome. As the only state-owned enterprise engaged in telecommunications, PT Telkom is expected to meet the needs of the Indonesian people. However, based on the rating obtained by IndiHome products through the myIndiHome application on Google Play, it is 3.5 out of 87,000 more reviews. The reviews focus on how important the effect of word-of-mouth is on choosing and using internet provider products. The review data was collected on November 1, 2020 to December 15, 2020, with a total of 2,539 reviews as a sample. The sentiment analysis process that has been carried out shows that the number of reviews included in the negative sentiment class was 1.160 reviews, and the positive class was 1.374 reviews out of a total of 2,539 reviews. The results indicate that service errors in IndiHome services are still quite high, reaching 46.7% as indicated by the number of negative reviews. The classification results show that the average value of the total accuracy of the Support Vector Machine (SVM) method is 86.54% greater than Naïve Bayes Classifier (NBC) method which has an average total accuracy of 84.69%. Based on fishbone diagram analysis, there are 12nd problems on negative reviews that classify problems 5P factors: Price, People, Process, Place, and Product.

Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

The strength of the company's competitiveness is needed because the current industrial development is very rapid. It is necessary to maintain the quality and quantity of the products produced according to company standards. One of the companies that must maintain the quality and quantity is PT. XYZ is a clay tile company. The classification of products used by this company to maintain good quality is three classes: good tile, white stone tile, and cracked tile. However, quality control based on classification still uses the traditional way by relying on sight. It can increase errors and slow down the process. It can be overcome with artificial visual detectors. It is a result of the rapid development of automation. So to detect defects, this research can use image preprocessing, supervised learning algorithms, and measurement methods. Support Vector Machine (SVM) is used in this study to perform classification, while feature extraction on clay tiles used the Local Binary Pattern (LBP) method. The algorithm is made using python, while for image retrieval, raspberry pi is used. The linear kernel on the SVM algorithm is used in this study. The conclusion in this study obtained 86.95% is the highest accuracy with a linear kernel. It takes 10.625 seconds to classify.

The Detection of Brain Tumors Using Chan-Vese Active Contour Without Edges Method in Magnetic Resonance (MR) Images

Accurate and automatic detections of brain tumors are vital. The aim of this study is to detect brain tumors in Magnetic Resonance (MR) images and to classify these tumors with a high degree of accuracy. After removing skull, the suspicious regions including tumors in the MR images were detected by using K-means clustering, K-means clustering in Lab color space, and the Chan-Vese without edges algorithm. At this stage, a performance evaluation of these three different methods was investigated, and it was seen that the best result was obtained in the Chan-Vese active contour without edges algorithm. For the classification stage, various features such as shape-based features, gray level co-occurrence matrix features, histogram of oriented gradients features, local binary pattern features, and statistical features were extracted from the detected suspicious regions. Finally, the suspicious regions were classified by k-nearest neighbor (k-NN), Fisher’s linear discriminant analysis (FLDA), random forest, decision tree, support vector machines (SVM), logistic linear classifier (LLC), and Naive Bayes classification methods. As a result of this study, it was determined that the FLDA classifier provided the best results with 93.01% accuracy, 93.46% sensitivity, and 96.50% specificity rates in classification for benign tumors, malignant tumors, and healthy (without tumor) cases.

IMPLEMENTASI OPINION MINING UNTUK PROVIDER INTERNET MENGGUNAKAN METODE NAIVE BAYES.

The development of information technology is growing from year to year. To support the smooth flow of information, there are many internet service providers circulating in Indonesia to support their needs. Some of the largest internet service providers in Indonesia such as Indihome, First media, and Biznet Home definitely have their own advantages and disadvantages.At this time, internet provider providers only accept customer complaints or suggestions through the customer service (CS) call center. Meanwhile, many young Indonesians currently use one of the popular Social Media services, namely Twitter as a user-friendly microblogging service so that users can easily use it, especially in delivering messages in the form of tweets. Therefore, a sentiment analysis program was designed for several internet providers in Indonesia. Opinions or Opinions will be analyzed to determine public sentiment. These sentiments will be classified into 3 sentiments, namely negative, positive, and neutral sentiments. The sentiment classification process can be done manually, but if there is too much data, it requires a system equipped with a classification method, so that the determination of classification can be done quickly. The design of this program applies the Naive Bayes Classifier method. Because this method is supervised learning, it requires training datasets with labels. Labeling will be done automatically using the K-means method. K-Means will sort tweets into groups which are divided into 3 labels. The results of the K-means clustering accuracy are 73.4%. The results of this application are divided into 2 parts, namely a pie chart which is divided into slices that describe the results of the percentage of tweet classifications and a table of classification results containing the number, content of the tweet, and the results of the classification. The best level of accuracy in testing uses 220 training data, and 54 training data. The results of the accuracy of 83.3%.

Path Loss Measurement Validation Using Naïve Bayes Classification for High-Speed Rail

This work proposes path loss measurement validation for high-speed rail wireless communication using Naïve Bayes Classification Method. Path loss propagation model is a major component in wireless network planning in order to meet expected service level requirements and optimized cell towers distribution. However, path loss in high-speed rail propagates rapidly because of unexpected environmental shifts, high mobility, and unique channel modelling. All these unique characteristics lead to inaccuracies and uncertainties in the path loss measurement. Naïve Bayes Classification with Gaussian Classifier offers a better solution in validating path loss measurement than the conventional method for typical wireless communication, which provides statistical descriptions of site-specific environments. The measurement dataset has been taken as continuous values and it is assumed to be distributed according to normal distribution. The dataset has been collected from a running high-speed train. Data discretization has been used as a pre-processing step of transforming continuous data attribute values into a finite set of intervals consisting of path loss in maximum and minimum speed for accuracy or inaccuracy class. The satisfying result of the best performance has been obtained in Accuracy Value (0.9114), Precision Value (0.9147), Recall Value (0.8772), F1-score (0.8956), and Area Under Curve score (0.83). The results have shown that the Naive Bayes method can potentially replace the conventional method in validating path loss measurement, especially for high-speed rail.

Sentiment analysis using fuzzy naïve bayes classifier on covid-19

Fuzzy Naive Bayes Classifier method has been widely applied for classification. The Fuzzy Naive Bayes method which consists of a combination of two methods including fuzzy logic and Naive Bayes is used to create a new system that is expected to be better. This research aim to find out the society's sentiments about COVID-19 in Indonesia and the use of the results of the Fuzzy Naive Bayes Classifier. The data of this research is obtained by scraping on Twitter in the period from January 1, 2020 to April 30, 2020. The classification method used in this research is the Fuzzy Naive Bayes Classifier method by applying the fuzzy membership function. In this research, sentiment analysis uses input data whose source is taken from tweets and the output data consists of sentiment data which is classified into three classes, namely positive class, negative class, and neutral class. In the distribution of training and testing data of 70%: 30%, the accuracy of the classification model using the confusion matrix is 83.1% based on 1199 tweet data consisting of 360 testing data and 839 training data. Also the presentation of each sentiment class was obtained which was dominated by positive sentiments, namely the positive class by 36.7%, the negative class by 35.0%, and the neutral class by 28.3%. Based on the results of the presentation, it can be concluded that there are still many people who have positive opinions or give positive responses to the presence of COVID-19 in Indonesia.

Penerapan Metode Naive Bayes Classifier (Nbc) Untuk Klasifikasi Kondisi Internal Program Studi

The low interest of prospective students in study programs at universities could be influenced by internal factors within the study program. These factors become the main variables in assessing the condition of the study program. For this reason, it is necessary to classify the internal conditions of the study program. A good method is needed in terms of accuracy and minimal misclassification to obtain the final classification results of the assessment. The purpose of this research is to classify the internal conditions of the study program. Classification of the internal conditions of the study program was carried out using the Naive Bayes Classifier (NBC) method which is a simple form of Bayesian Network with the assumption that all features are independent of each other. The NBC method shows an overall superior performance in terms of accuracy and misclassification rate. The NBC method can be used to determine the internal conditions of the study program, which could help identify factors that need to be addressed to increase the interest of prospective students enrolling in the study program.

Sentiment analysis of e-commerce application in Traveloka data review on Google Play site using Naïve Bayes classifier and association method

E-commerce is a business operation model which rapidly growing today. Many business actors and the customer take advantage of E-commerce itself. Thus, it influences people’s socially and economically. Traveloka is one of the best e-commerce applications that is often visited in Indonesia. Each application allows users to post an application review. The review aims to evaluate and improve the quality of the future product. For that purpose, analysis sentiment can be used to classify the review into positive or negative sentiment. Sentiment analysis can provide information that can be extracted. From the observed data, it can provide useful information for those who need it. Some sentiment analysis stages contain sentiment data collection, data preprocessing, term weighting using TF-IDF, sentiment labeling using sentiment scoring, review data classification using the Naïve Bayes Classifier method, and text association. The model was evaluated using 10 Fold Cross-Validation. Measurements were made with the Confusion Matrix. The results obtained from the reviews given by Traveloka users on Google Play using the Multinomial Naïve Bayes was obtained overall accuracy in 91.20% and kappa accuracy in 59,56 %. The higher overall accuracy value and kappa accuracy obtained, the better performance of the classification model.

Improvised Spam Detection in Twitter Data Using Lightweight Detectors and Classifiers

Receiving spam messages is one of the most serious issues in social media, especially in Twitter, which is a widely used platform to reflect the opinions and emotions of an individual publicly as well as focused to a specific group of members with similar thoughts or discussion topic. In such focused discussion groups, getting spam message through social media sites is the most annoying issue. In this paper, a system is developed to detect spam tweets by using four lightweight detectors, namely blacklist domain detector, near duplicate detector, reliable ham detector, and multiclass detector. The detected tweets are then classified using ensemble classifiers such as naïve Bayes, logistic regression, and random forest. Voting method is applied to decide the labels for the tweets obtained after classification process. The proposed system has achieved an accuracy of 79% to detect spam tweets with the help of naïve Bayes classifier method and the value seems to be optimizing further with the availability of more sample data.