scholarly journals KLASIFIKASI PESAN GANGGUAN PELANGGAN MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER

Kilat ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 100-108
Author(s):  
Haryono Haryono ◽  
Pritasari Palupiningsih ◽  
Yessy Asri ◽  
Andi Nikma Sri Handayani
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Bayes Method ◽  
Machine Learning Methods ◽  
Data Processor ◽  
Selection Of

The application of customer disturbance message classifiers is made because of the process of reporting the interruption by the customer must be done by selection of data disorders by one by the admin to be able to follow-up from the existing customer reports. Naive Bayes is one of machine learning methods that uses probability calculations where the algorithm takes advantage of probability and statistical methods that predict future probabilities based on past experience. The application of the naive bayes classifier method with text mining as the initial data processor of the disorder messaging application can be concluded that this study yields an accuracy of probability values of 95 percent and proves that the Naive Bayes method can be used to help classify interference messages sent by customers.

scholarly journals COMPATIBILITY OF SELECTION OF STUDENT DEPARTMENTS USING k-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFIER IN INFORMATICS PRIVATE VOCATIONAL SCHOOL, SERANG CITY

2021 ◽  
Vol 4 (1) ◽  
pp. 33-39
Author(s):  
Budi Pangestu ◽  
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Prospective Students ◽  
Naïve Bayes Classifier ◽  
K Nearest Neighbors ◽  
Selection Of

Selection of majors by prospective students when registering at a school, especially a Vocational High School, is very vulnerable because prospective students usually choose a major not because of their individual wishes. And because of the increasing emergence of new schools in cities and districts in each province in Indonesia, especially in the province of Banten. Problems experienced by prospective students when choosing the wrong department or not because of their desire, so that it has an unsatisfactory value or value in each semester fluctuates, especially in their Productive Lessons or Competencies. To provide a solution, a departmental suitability system is needed that can provide recommendations for specialization or major suitability based on students' abilities through attributes that can later assist students in the suitability of majors. The process of classifying the suitability of majors in data mining uses the k-Nearest Neighbor and Naive Bayes Classifier methods by entering 16 (sixteen) criteria or attributes which can later provide an assessment of students through this test when determining the majors for themselves, and there is no interference from people. another when choosing a major later. Research that has been carried out successfully using the k-Nearest Neighbors method has a higher recall of 99%, 81% accuracy and 82% precision compared to the Naïve Bayes Classifier whose recall only yields 98% while the accuracy and precision is the same as the k- Nearest Neighbors.

scholarly journals Perbandingan Seleksi Fitur Term Frequency & Tri-Gram Character Menggunakan Algoritma Naïve Bayes Classifier (Nbc) Pada Tweet Hashtag #2019gantipresiden

Kilat ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 103-114
Author(s):  
Arini - Arini ◽  
Luh Kesuma Wardhani ◽  
Dimas - Octaviano
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Term Frequency ◽  
Selection Of

Towards an election year (elections) in 2019 to come, many mass campaign conducted through social media networks one of them on twitter. One online campaign is very popular among the people of the current campaign with the hashtag #2019GantiPresiden. In studies sentiment analysis required hashtag 2019GantiPresiden classifier and the selection of robust functionality that mendaptkan high accuracy values. One of the classifier and feature selection algorithms are Naive Bayes classifier (NBC) with Tri-Gram feature selection Character & Term-Frequency which previous research has resulted in a fairly high accuracy. The purpose of this study was to determine the implementation of Algorithm Naive Bayes classifier (NBC) with each selection and compare features and get accurate results from Algorithm Naive Bayes classifier (NBC) with both the selection of the feature. The author uses the method of observation to collect data and do the simulation. By using the data of 1,000 tweets originating from hashtag # 2019GantiPresiden taken on 15 September 2018, the author divides into two categories: 950 tweets as training data and 50 tweets as test data where the labeling process using methods Lexicon Based sentiment. From this study showed Naïve Bayes classifier algorithm accuracy (NBC) with feature selection Character Tri-Gram by 76% and Term-Frequency by 74%,the result show that the feature selection Character Tri-Gram better than Term-Frequency.

scholarly journals Analisis Sentimen Pada Media Sosial Twitter Menggunakan Naive Bayes Classifier Dengan Ekstrasi Fitur N-Gram

2018 ◽  
Vol 2 (2) ◽  
pp. 200
Author(s):  
Agung Nugroho
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Bayes Method ◽  
N Gram ◽  
Naive Bayes Method

Social media is currently an online media that is widely accessed in the world. Microblogging services such as Twitter allow users to write about various things they experience or write reviews of a product, service, public figures and so on. This can be used to take opinion or sentiment towards an entity that is being discussed on social media such as Twitter. This study utilizes these data to determine public opinion or sentiment regarding public perceptions of the issue of rising electricity tariffs. Opinion taking is based on three classes namely positive, negative and neutral. Users often use non-standard word abbreviations or spelling, this can complicate the process and accuracy of classification results. In this study the authors apply text-preprocessing in handling these problems. For feature extraction, n-gram and classification methods are used using the Naive Bayes classifier. From the results of the research that has been done, the most negative sentiments are formed in response to the issue of the increase in basic electricity tariffs. In addition, from the results of testing with the method of cross validation and confusion matrix it is known that the accuracy of the naïve Bayes method reaches 89.67% before applying n-gram, and the accuracy rate increases 2.33% after applying n-gram characters to 92.00%. It is proven that the application of the n-gram extraction feature can increase the accuracy of the naïve Bayes method.

scholarly journals PENERAPAN DATA MINING DENGAN ALGORITMA NAIVE BAYES CLASSIFIER DALAM MEMPREDIKSI PEMBELIAN CAT

2021 ◽  
Vol 9 (01) ◽  
pp. 19-23
Author(s):  
Fitriana Harahap ◽  
Nidia Enjelita Saragih ◽  
Elida Tuti Siregar ◽  
Husin Sariangsah
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Sales Manager ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Bayes Method ◽  
Paint Product ◽  
Product Sales ◽  
Naive Bayes Method

Companies need several types of communication technology that can predict customer purchase interest, the goal is that the company can properly consider product sales and determine the company's paint product supply. So far, the decision of the Home Smart sales manager has been made by looking at the closeness of the supplier relationship and how many sponsors are funding the company. So that sometimes the product cannot compete with other companies. The Naive Bayes classifier algorithm is one of the algorithms included in the classification technology. The application of the Naive Bayes method is expected to predict paint purchases from suppliers. From 60 paint purchase data tested with the Naive Bayes method, the results reached 80% of the accuracy of the predictions. Of the 60 tested paint purchase data, 48 paint purchase data were successfully classified correctly.

scholarly journals Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier

2019 ◽  
Vol 2 (1) ◽  
pp. 34
Author(s):  
Lingga Aji Andika ◽  
Pratiwi Amalia Nur Azizah ◽  
Respatiwulan Respatiwulan
Keyword(s):  
Presidential Election ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Bayes Method ◽  
Bayes Model ◽  
General Elections ◽  
Democratic System

<p>Indonesia is one of the countries that adheres to a democratic system. In the course of a democratic system it is marked by periodic general elections. In 2019 Indonesia held a general election simultaneously to elect the President, DPR, DPRD and DPD. After the election, a lot of opinion arise within the community, including on social media twitter. One of the topics discussed was the results of the quick count of the presidential election. Therefore, a method that can be used to analyze sentiment from the quick count opinion is needed, that is naive Bayes method. The aims of this study are to find the best naive Bayes model and to classify sentiments. The result shows the best accuracy of 82.90% with α = 0.05. The classification obtained is 34.5% (471) positive tweets and 65.5% (895) negative tweets on the results of the quick count.</p><p><strong>Keywords :</strong> sentiment analysis, naive Bayes classifier, elections, quick count</p>

scholarly journals SISTEM REKOMENDASI PENERIMAAN MAHASISWA BARU MENGGUNAKAN NAIVE BAYES CLASSIFIER DI INSTITUT PENDIDIKAN INDONESIA

JOUTICA ◽  
2018 ◽  
Vol 3 (2) ◽  
pp. 171
Author(s):  
Andri Suryadi ◽  
Erwin Harahap
Keyword(s):  
Recommendation System ◽  
Naive Bayes ◽  
Selection Process ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Prospective Students ◽  
Naïve Bayes Classifier ◽  
New Student ◽  
Selection Of

The quality of a university in creating qualified graduates is determined by the prospective students who enter the college. One of the things that can determine the quality is how the selection process of candidates for good student acceptance. However, the selection process of admissions in every college of course is different. Often the input of prospective students who enter the university is not in accordance with the expected so that the impact of graduate results. Therefore it is necessary for a system that can support the decision in the selection of new student candidates in order to get a good student input. This research builds a Recommendation System that will assist in the selection process of universities for the selection team of new student candidates. This recommendation system uses the naïve Bayes classifier method where the test scores of incoming selection of students who have been accepted will be used as training data and then classified based on the value of ipk that has been obtained. The value of the ipk will be a benchmark for the formation of classes - classes that are recommendations to the selection team. The classes that are formed are classes whose ipk value is at the accepted point and the class whose ipk value is not accepted. Then given a new student data, if the prospective student enters the safe class then it will be recommended to be accepted but otherwise it will be recommended to be rejected

scholarly journals A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2083
Author(s):  
Sylwia Rapacz ◽  
Piotr Chołda ◽  
Marek Natkaniec
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Spam Filtering ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Classifier Performance ◽  
Selection Of

The paper elaborates on how text analysis influences classification—a key part of the spam-filtering process. The authors propose a multistage meta-algorithm for checking classifier performance. As a result, the algorithm allows for the fast selection of the best-performing classifiers as well as for the analysis of higher-dimensionality data. The last aspect is especially important when analyzing large datasets. The approach of cross-validation between different datasets for supervised learning is applied in the meta-algorithm. Three machine-learning methods allowing a user to classify e-mails as desirable (ham) or potentially harmful (spam) messages were compared in the paper to illustrate the operation of the meta-algorithm. The used methods are simple, but as the results showed, they are powerful enough. We use the following classifiers: k-nearest neighbours (k-NNs), support vector machines (SVM), and the naïve Bayes classifier (NB). The conducted research gave us the conclusion that multinomial naïve Bayes classifier can be an excellent weapon in the fight against the constantly increasing amount of spam messages. It was also confirmed that the proposed solution gives very accurate results.

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