Naive Bayes Classifier Method Research Articles

Klasifikasi Mahasiswa Berprestasi Menggunakan Fuzzy C-Means Dan Naive Bayes

Success in the world of education is often associated with successful academic achievements. Therefore, processing information is very important to determine the selection of students who excel. However, study programs and student services often face difficulties in recognizing students who have achievements. In this research, outstanding students from the Faculty of Engineering, Makassar State University were determined using the Naive Bayes classification method combined with the Fuzzy C-Means (FCM) method to identify data patterns before classification. The criteria measured are GPA, achievements achieved, organizations attended, and the number of Semester Credit Units (SKS) that have been programmed. By using the Confusion Matrix, the evaluation results show an accuracy level of 98%, recall of 97%, precision of 100%, and F1-Score of 99%.

Analisis Sentimen Ulasan Aplikasi Smart Campus Unisbank di Google Playstore Menggunakan Algoritma Naive Bayes

This study explores sentiment analysis on SmartCampus Unisbank application reviews on Google Play Store using the Naive Bayes classification method. Through Python programming language and web scraping techniques employing the google-play-scraper library, review data was automatically obtained and organized in CSV format. Text preprocessing techniques such as case folding, stopwords removal, tokenization, and stemming were applied to ensure accurate analysis. The data was divided into training and testing sets, and TF-IDF (Term Frequency-Inverse Document Frequency) was used for feature extraction. A Naive Bayes model was constructed and evaluated, achieving an accuracy of 84.6%. While the model demonstrated proficiency in identifying negative sentiments with 100% precision, it requires refinement for recognizing positive sentiments. These findings provide valuable insights for SmartCampus Unisbank developers to understand user perspectives and improve application quality

Nutritional Status Classification Of Stunting In Toddlers Using Naive Bayes Classifier Method

Stunting in toddlers is one of the prevalent issues of malnutrition in Indonesia. The causes of Stunting are diverse, and one contributing factor is the insufficient nutritional intake required for toddlers. The categorization of Stunting nutritional status in toddlers is crucial to identify those experiencing Stunting, enabling appropriate interventions to prevent more serious health problems in the future. This research aims to develop a classification model for short nutritional status in toddlers using the Naive Bayes Classifier method. The data utilized in this study originate from anthropometric measurements of toddlers in the Malebo area, Kandangan, Temanggung, Central Java. The anthropometric data include weight, height, and age of the toddlers. This data is then processed using the Naive Bayes Classifier method to classify the nutritional status of Stunting in toddlers. The results of this research are expected to assist in identifying toddlers experiencing Stunting, facilitating appropriate interventions to prevent more serious health issues in the future. Additionally, the Naive Bayes Classifier method employed can be applied in similar studies to enhance the quality of life, especially for children in Indonesia, particularly in the Malebo area, Kandangan, Temanggung, Central Java.

IMPLEMENTASI METODE NAIVE BAYES CLASSIFIER UNTUK KLASIFIKASI SENTIMEN ULASAN PENGGUNA APLIKASI NETFLIX PADA GOOGLE PLAY

The COVID-19 pandemic has led to restrictions on activities in public places or facilities, such as cinemas. This has resulted in increased users of streaming service applications such as Netflix where users can access videos or movies online. Netflix users continue to increase from year to year, but its users began to decrease along with other streaming applications. Related to this, sentiment analysis was carried out on the classification of positive and negative reviews given by users on the Google Play website. The classification is expected to produce good accuracy and be analyzed so that it can be useful information for Netflix and potential users of streaming applications. The Naive Bayes Classifier method is a classification algorithm that is easy to apply and has high effectiveness for classifying text. This method utilizes the concept of conditional probability and has a strong assumption of independence. This study uses 2.850 Netflix application review data on Google Play which is then processed and divided into training data and test data with a ratio of 80:20. Classification with the Naive Bayes Classifier produces an accuracy value of 87,33%, a precision value of 87,6%, a recall value of 93,53%, and an F-measure value of 90,47% so it can be concluded that the performance of the Naive Bayes method is good for classifying user reviews of the Netflix.

Application of Naive Bayes Classifier Method to Analyze Social Media User Sentiment Towards the Presidential Election Phase

This research aims to analyze the sentiment of social media users towards the election. The author collected data in this research through a literature study and observation. The author uses a classification method with the Naïve Bayes Classifier Algorithm and Support Vector Machine to analyze sentiment results. Next, this research extracts word assessment features using TextBlob, which changes text into positive or negative classes. Based on the research results, after going through the text preprocessing stage of more than 15,000 tweets, 11,000 clean tweets were obtained, which were then labelled using the text blob library in Python. The labelling results show that 4,000 tweets are positive, and the rest are harmful, indicating that most social media users' sentiment towards the election is positive. Words that often appear in the positive class express support and confidence in implementing elections that are considered honest and fair. On the other hand, words in the negative class reflect negative sentiment towards implementing elections, which are considered unsuccessful and time-consuming. The Naïve Bayes method provides accuracy, precision, and recall values of 85%, 80%, and 75%. In the Support Vector Machine method, testing is carried out with three kernels (linear, RBF, and poly), where the poly kernel with the best parameter values C is ten and degree is 1 produces the highest accuracy, precision, and recall of 90%, 90%, and 85%, respectively.

Application of the Naive Bayes Classifier Method and Fuzzy Analytical Hierarchy Process in Determining Books Eligible for Publishing

The manuscript selection process is the process of assessing manuscripts worthy of publication. The Editor's job is to provide an evaluation of each manuscript based on the assessment criteria and sub-criteria. By using a decision support system, it can make it easier for policymakers to determine the suitability of a manuscript. In this research, a decision support system is applied to select papers that are worthy of publication, namely the Fuzzy Analytical Hierarchy Process (F-AHP) method for selecting the suitability of manuscripts using subjective criteria and the Naïve Bayes method for classifying books based on their genre. The test results using the F-AHP method produced an accuracy rate of 83.33% using 30 books out of 150 books and using the Naïve Bayes method produced an accuracy rate of 80% using 30 books from the internet. This system uses the Visual Studi Code IDE, Firebase, and Pythonanywhere as its database with an Android display.

Naive Bayes Classifier Method on Sentiment Analysis of Bibit Application Users in Play Store

The increasing public interest in investment and supported by technological advances has begun to appear investment applications in the community which aim to facilitate the public in making investments. One of the investment applications that is widely used today is the Bibit application. This application is widely used by novice investors because of its ease of opening accounts, disbursing funds, purchasing mutual funds and easy-to-understand application design. Because investment applications are still new to the community, there are still many people who doubt and worry about the quality of the Bibit application, marked by the number of reviews in the review column available on the play store. Reviews on the application become a forum for criticism and suggestions to the application and become one of the considerations for potential users. Because reviews can be positive or negative towards the Seedling application. Sentiment analysis is needed to analyze whether the sentiment tends to be positive or negative. Then, classification is carried out to obtain a classification model that can be used to predict user sentiment using the Naive Bayes Classifier method. The results obtained obtained seed application users tend to have positive sentiments with an accuracy value of 79.45%.

ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER DENGAN STUDI KASUS KARTU PRAKERJA

Kartu Prakerja is one of the government's flagship programs in providing training to the workforce. In its implementation there is a lot of information scattered, especially on social media Twitter both in the pros and cons of Kartu Prakerja program. Based on information in the form of tweets that have not been analyzed in depth, it is necessary to analyze sentiment on the Kartu Prakerja in order to obtain appropriate information based on the opinions of netizen s on Twitter. This study discusses sentiment analysis of tweet data with the keyword “Kartu Prakerja” which uses data as many as 6658 tweet data taken in the period May 27 - August 5, 2021. This research uses the Naive Bayes Classification method which has several stages, namely data retrieval, data preprocessing, manual labeling, data training and testing. The solution offered in this study is to create an analysis model that can be used to perform sentiment analysis about Kartu Prakerja on Twitter. Based on the results of this study obtained that the calculation of accuracy obtained a value of 86% for training data and 87% for data testing. This study concluded that the Kartu Prakerja has a positive sentiment by Twitter netizens based on the results of Classification that discusses many positive sentiments such as the benefits, effectiveness and addition of the Kartu Prakerja budget.

PENDEKATAN DATA MINING UNTUK MEMILIH PRODUK TERLARIS MENGGUNAKAN ALGORITMA NAIVE BAYES

Technological advances today can be exploited to process data into more useful information. In data collection, information collection is especially useful to maximize profits and develop marketing strategies. One way to increase profits is by using data mining techniques to help business actors in making decisions about stocks, increased profits and more. The Matahari Department Store is the largest retail platform in Indonesia, one of the retail stores located in Batam is the Matahari Department store Nagoya Hill Batam. The transaction data on the store that is still processed does not use a method that causes the processing of product sales data to be less effective and less efficient. Seeing from the number of transactions, a system is needed to predict the sale of the best-selling product as long as it can determine the correct stock for the products sold and can increase the profit, sale and purchase of the product. This research was conducted with the aim of applying data mining methods using the Naive Bayes Classifier algorithm to select the best-selling products in the outlet of the Matahari Nagoya Hill Batam Department Store. By using the collected sales data, the system is expected to increase profits steadily and avoid shortages of product stocks. Through analysis using the Naive Bayes Classifier method, the study achieved an accuracy of 67% and obtained a bag sales result to be the best-selling sale during January 2023 through March 2023 with a sales percentage of 20%.

Wood Type Identification System using Naive Bayes Classification

Wood, a forest product and natural resource, is a raw material used to make household goods. Some types of wood have almost the same pattern or structure. Wood quality varies greatly depending on the tree species and the environmental conditions in which it grows. This makes it challenging to identify the type of wood, especially for wooden furniture users. Therefore, wood classification is essential to ensure that the wood used meets the required quality standards and requirements. Automatic classification of wood using image processing has several advantages and can make it easier for humans. One of the image processing methods for wood classification is the Naïve Bayes method. Feature extraction technique using GLCM using contrast, correlation, energy, and homogeneity attributes. The GLCM methods can be combined to create a system design to distinguish five wood species using an image-based wood type identification system. The study results have successfully designed a system to determine five types of wood using the framework of an image-based wood type identification system. An application system has been produced to distinguish five types of wood using the framework of an image-based wood type identification system with the GLCM feature extraction method and the Naive Bayes classification method. The application system successfully identified wood species with a test accuracy rate of 88%.

Sentiment Analysis of Electric Cars Using Naive Bayes Classifier Method

In recent years, electric cars have become increasingly popular as an alternative to environmentally friendly vehicles in the automotive industry. These vehicles use electric power as an energy source that can reduce dependence on fossil fuels so as to contribute to efforts to reduce greenhouse gas emissions and air pollution. However, the presence of electric cars raises pro and con opinions from the public. Where, the conversation about electric cars has become one of the hot conversations on social media twitter. Twitter is a microblogging-based social media that facilitates its users to write short messages and share them easily and quickly. These opinions require sentiment analysis. The purpose of conducting sentiment analysis is to find out how people's perceptions and opinions on electric cars are leading in a positive direction or in a negative direction. Thus, sentiment analysis can help companies in designing marketing strategies, product development, and making better business decisions. Then the opinions will be classified based on positive and negative categories. This research uses the naive bayes classifier method to generate positive and negative sentiment towards electric cars on Twitter. The accuracy results of naive bayes obtained by using a confusion matrix in this research are 78.57% with a dataset split composition of 80%:20%.

Analisis Sentimen Pencitraan Perguruan Tinggi di Yogyakarta Menggunakan Metode Naїve Bayes Classifier

This research utilizes data from Twitter to analyze sentiment in Yogyakarta's universities using the Naïve Bayes Classifier method. The Naive Bayes Classifier method is one of the text classification methods based on the probability of keywords in comparing training and testing documents. The data used consists of tweets in Indonesian language with keywords from the top 10 universities in Yogyakarta based on webometrics, as well as four other relevant keywords about Yogyakarta that are frequently searched through Google. From the conducted research, there are 1710 data collected from Twitter, which are used for classification and categorized into 3 labels: positive, negative, and neutral. The data is divided into 70% for training and 30% for testing randomly. The result of sentiment analysis classification from the test data shows that 82.1% of the data is categorized as neutral, 14.8% as positive, and 3.1% as negative, with an accuracy value of 73%.

Penerapan Metode Naïve Bayes Classifier Pada Klasifikasi Sentimen Terhadap Anies Baswedan Sebagai Bakal Calon Presiden 2024

Anies Baswedan is a political figure who has been declared as a 2024 presidential candidate. Public opinion is a valuable source of information to analyze sentiment towards Anies Baswedan as a 2024 presidential candidate. Limited human power, emotional instability, and the length of time required are difficulties in analyzing sentiment on large amounts of data manually. Machine learning is utilized to provide convenience in sentiment classification. This research applies the Naïve Bayes Classifier method in the classification of sentiment towards Anies Baswedan as a 2024 presidential candidate. This study aims to determine the performance of the Naïve Bayes Classifier method in the classification of sentiment towards Anies Baswedan as a 2024 presidential candidate. The dataset used was 3,400 which were labeled by crowdsourcing resulting in 2,130 positive (62.65%) and 1,270 negative (37.35%). Tests were conducted using the 10-fold cross-validation and 5-fold cross-validation methods, each consisting of two experimental scenarios, namely using an unbalanced dataset and using a balanced dataset.The Naive Bayes Classifier method produces the best model in the 10-fold cross-validation test with an accuracy of 89.76%, precision of 89.92%, recall of 89.76%, and f1-score of 89.75% on the sixth fold by determining a threshold value of 13 in an experiment using a balanced dataset consisting of 1,270 positives and 1,270 negatives with an average accuracy rate of 79.88%.

Classification of Traffic Congestion in Indonesia Using the Naive Bayes Classification Method

The purpose of this research is to analyze the accuracy of congestion data using Google Colab in detecting congestion by the province in Indonesia the author tries to test strategies for dealing with congestion in the Indonesian region by utilizing the Naïve Bayes method. In this journal, apply with Google Collab . This research uses data that comes from crawling data on Twitter. Using the Naive Bayes method to find the shortest route is efficient and not congested. Implementation of online school transportation using the naive Bayes method in minimizing travel costs to pick up students can reduce traffic jams, reduce accidents, reduce student tardiness, and minimize travel costs. The Naive Bayes method can be used to identify relevant information about traffic jams in Indonesia through Twitter data with a good degree of accuracy. These results can assist decision-making and strategic planning in overcoming the problem of traffic congestion in Indonesia. Therefore, this research implies that it can help improve the accuracy of traffic congestion data in Indonesia. By using Google Colab, more advanced analysis methods and machine learning algorithms can be applied to process the existing traffic data. Additionally, utilizing Google Colab allows for fast and efficient data processing.

CLASSIFICATION OF ROASTING RATES IN COFFEE BEANS BY DIGITAL IMAGE PROCESSING USING THE NAIVE BAYES CLASSIFIER (NBC) METHOD

Coffee is the bean of the coffee plant and is the source of coffee drinks. Coffee beans must pass through the coffee roasting stage or also called coffee roasting, from this stage of the process the coffee will be roasted and this process also has its own level. At this stage of the roasting process coffee shop business people often do not know this process, then in this case used Local Binary Pattern (LBP) method. LBP is a simple and very efficient texture operator by labeling pixels by doing thresholding on each pixel neighbors and considers the result as a binary number. This method is used to obtain the texture ektrasi of an image. While for the classification method using the Naive Bayes Classifier (NBC) method. Naive Bayes is a simple probabilistic classifier that calculates a set of probabilities by summing the frequencies and combinations of values from a given dataset. The algorithm uses Bayes' theorem and assumes all the independent or non-interdependent attributes given by the values on the class variables. From the test results by comparing training data and testing data obtained an accuracy rate of 81%. For an image-based developed system with display recognition difficulties, this performance is good.

K-Nearest Neighbor and Naive Bayes Classifier Methods for Expedition Service Comparison Analysis of User Sentiments


 
 
 
 Depending on the service chosen, expedition is one of the freight forwarding companies that operate in the domestic market. The availability of expedition services can make it easier for traders to transfer items to purchasers who conduct online transactions, as well as encourage shipping businesses to collaborate with online dealers. The JNE, JNT, and Pos Indonesia excursions were utilized in this study. The goal of this project is to develop an analytical model that will make it simpler for online merchants to find collaborators for effectively and securely transporting their goods. Based on user sentiment on the social networking site Twitter, this study uses sentiment analysis. With the keywords "JNT, JNE, and Pos Indonesia," this study compares the accuracy results using the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. According to this study, testing accuracy for the NBC method was 80% and training accuracy was 83%. While the accuracy of the KNN approach is 68%. According to public opinion, the JNE expedition is the best one for distributing products, scoring 68.58% in favor of it and 30.64% against it.
 
 
 
 

Application of the Naïve Bayes Algorithm in Determining Sales Of The Month

One important factor for creating a healthy and growing company is the existence of sales rewards for employees to achieve sales targets every month. Assessing employees is not an easy thing when there are so many employees. This will make the assessment team have to look at the criteria carefully and carefully. Data manipulation can occur because it is difficult to make decisions with such large criteria and data without automated data mining. As a result, the company will not get competitive human resources. Sales targets are one of the keys to sales success because with sales targets, the sales prediction value can be used as a guide as a reference in determining product sales. One way to make better sales predictions is by utilizing data mining processing using the Naive Bayes algorithm. The Naive Bayes algorithm calculates the probability value of each of the attributes examined including attendance, sales targets and sales returns. Research with employee absence criteria, monthly sales and monthly sales invoice returns. From the results of the research that has been done, it can be concluded that the application of the Naive Bayes classifier method to the target data set for sales of goods achieves an optimization level of 95.78%, with attendance criteria greatly affecting employee performance so that product sales targets each month can be achieved

Sentiment Analysis of Twitter Cases of Riots at Kanjuruhan Stadium Using the Naive Bayes Method

Sentiment analysis is a process carried out to analyze opinions, sentiments, judgments, and emotions from the riot case at the kanjuruhan stadium. The purpose of this research is to find out public opinion about the tragedy that is currently happening at the Kanjuruhan Stadium. the data was obtained from social media Twitter using the Twitter API, then after that, an analysis was carried out. data from the results of the analysis will be classified using the Naive Bayes method. The classification process is divided into 7 (seven) stages, namely Crawling, Cleansing, pre-processing, labeling, classification, data training, and data testing. In the labeling process, data is classified into 2 (two) classes, namely the positive class and the negative class. The data obtained before the preprocessing process was 1963 tweets, after the preprocessing the data obtained was 1001 tweets. The data will be trained and tested using the naive Bayes classification method. classification results obtained precision values of 82% for negative data and 65% for positive data, recall values obtained 74% for negative data and 75% for positive data, F1-score values obtained 78% for negative data and 70% for positive data, while accuracy value obtained 74%.

Analisis Sentimen Ulasan Produk Layanan Internet di Twitter Menggunakan Naive Bayes Classifier

This research aimed to conduct sentiment analysis on user reviews of Indihome, one of Indonesia's leading internet service providers, using data collected from Twitter. With a growing user base of 7 million, Indihome achieved an impressive revenue growth rate of 48% by the end of 2019. To assess user satisfaction, sentiment analysis was performed on tweets containing the keyword "indihome" between July 1 and July 11, 2022. A total of 10,000 tweets and retweets were collected and subjected to data preprocessing, manual labeling, and training using the Naive Bayes Classifier method. The training phase involved 1,000 tweets, equally divided between positive and negative sentiments, manually labeled for accuracy. For evaluation, 200 labeled tweets were used as testing data. The sentiment analysis results were remarkable, with 98% accuracy achieved for the training data and 91% for the testing data. These findings demonstrate the effectiveness of the Naive Bayes Classifier in accurately determining sentiment from Indonesian-language tweets about Indihome's services. By analyzing user opinions expressed on Twitter, this study provides valuable insights into the level of user satisfaction with Indihome as an internet service provider. Leveraging sentiment analysis applications on Twitter social media enables companies like Indihome to gain a deeper understanding of customer sentiment and make informed decisions to improve their services. The research showcases the importance and effectiveness of sentiment analysis in evaluating user feedback and enhancing customer satisfaction in the internet service industry.

Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results

Abstract. The application of information technology in the field of education produces big data. It retains information that can be treated as useful. Having data mining, can be used to model highly useful student performance for educators performing corrective actions against weak students. 
 Purpose: The study was to identify the application and accuracy algorithm Naive Bayes Classifier to predict students' study results.
 Methods: The prediction system for student learning outcomes was built using the Naive Bayes Classifier and Laplace Smoothing methods using a combination of two Information Gain and Chi Square feature selections. The experiment was carried out 2 times using different dataset comparisons.
 Result: In the first experiment using a dataset of 80:20, the accuracy Naive Bayes Classifier method with Laplace Smoothing and without Laplace Smoothing showed the same results as 94.937%. On the second experiment to equate dataset 60:40 results of the Naive Bayes Classifier accurate method without Laplace Smoothing only 86.076%, then score a 91.772% accuracy using the Laplace Smoothing. The improvement is caused by a probability of zero that can be worked out with Laplace Smoothing.
 Novelty: The selection feature process is very important in the classification process. Thus, in this study, information gain and chi square double selections of such features as information gain and so promote accuracy.