scholarly journals Design of NLP Technique Fore-Customer Review

2020 ◽  
Vol 9 (3) ◽  
pp. 653-655
Keyword(s):  
Logistic Regression ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Customer Reviews ◽  
Quality Metric ◽  
Customer Review ◽  
The Right ◽  
Other Consumers

With the passage of time and the growth of ecommercea new web world needs to be built their users can share their ideas and opinions differently domains.There are thousands of websites that sell these various products. The quick growth in the number of reviews and their availability and the arrival of rich reviews for rich products for sale online, the right choice for many products has been difficult for users. Consumers will soon be able to verify the authenticity and quality of the products. What better way is there to ask people who have already bought the product? That’s where customer reviews come from. What’s worse is the popular products with thousands of updates — we don’t have the time or the patience to read all of them thousands. Therefore, our app simplifies this task by analysing and summarizing all the reviews that will help the user determine what other consumers have experienced in purchasing this product. This function focuses on mining updates from websites like Amazon, allowing the user to write freely to view. Automatically removes updates from websites. It also uses algorithms such as the Naïve Bayes classifier, Logistic Regression and SentiWordNet algorithm to classify reviews as good and bad reviews. Finally, we used quality metric parameters to measure the performance of each algo.

scholarly journals Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm

2020 ◽  
Vol 4 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Edi Sutoyo ◽  
Ahmad Almaarif
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Educational Data Mining ◽  
Naïve Bayes ◽  
Drop Out ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Student Graduation

The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735.

scholarly journals Twitter Sentiment Analysis using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 4205-4209
Keyword(s):  
Logistic Regression ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Learning Techniques ◽  
Social Media Platforms

Nowadays people share their views and opinions in twitter and other social media platforms, the way of recognizing sentiments and speculation in tweets is Twitter Sentiment Analysis. Determining the contradiction or sentiment of the tweets and then listing them into positive, negative and neutral tweets is the main classifying step in this process. The issue related to sentiment analysis is the naming of the correct congruous sentiment classifier algorithm to list the tweets. The foundation classifier techniques like Logistic regression, Naive Bayes classifier, Random Forest and SVMs are normally used. In this paper, the Naïve Bayes classifier and Logistic Regression has been used to perform sentiment analysis and classify based on the better accuracy of catagorizing Technique. The outcome shows that Naive Bayes classifier works better for this approach. Data pre-processing and feature extraction is realized as a portion of task.

scholarly journals Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier

2021 ◽  
Vol 5 (2) ◽  
pp. 233-242
Author(s):  
Loemongga Oktaria Sihombing ◽  
◽  
Hannie Hannie ◽  
Budi Arif Dermawan ◽  
◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Business World ◽  
Business People ◽  
Main Challenge ◽  
Customer Review ◽  
Bayes Algorithm

Gaining customer satisfaction and trust has become the main challenge in achieving success in the business world. Business people need to identify problems that arise from reviews given by customers. However, reading and classifying each review takes a long time and is considered ineffective. To overcome this, this study aims to analyze the customer sentiment of shopee products using the nave Bayes classifier algorithm. The data used in this study is a customer review of the Xiaomi Redmi Note 9 products which are sold on the Shopee Indonesia website. Customer review data is collected by applying the Web Scraping technique. The algorithm used in this study is the Naïve Bayes Classifier which is known to be popular and effective in classifying data. This study also applies the Knowledge Discovery in Text (KDT) methodology to extract information from text data. The results of the classification using the Naïve Bayes algorithm found an accuracy value of 85%. This study proves that by applying sentiment analysis techniques, business people are able to find out the opinions of customers as an evaluation material that needs to be done to optimize the products and services provided.

scholarly journals The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency

2018 ◽  
Vol 5 (2) ◽  
pp. 194-204
Author(s):  
Feroza Rosalina Devi ◽  
Endang Sugiharti ◽  
Riza Arifudin
Keyword(s):  
Beef Cattle ◽  
Clustering Algorithm ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Fuzzy C Means ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification ◽  
Fuzzy C Means Algorithm

The beef cattle quality certainly affects the quality of meat to be consumed. This researchperforms data processing to do the classification of beef cattle quality. The data used are196 data record taken from data in 2016 and 2017. The data have 3 variables fordetermining the quality of beef cattle in Semarang regency namely age (month), Weight(Kg), and Body Condition Score (BCS) . In this research, used the combination of NaïveBayes Classification and Fuzzy C-Means algorithm also Naïve Bayes Classification andK-Means. After doing the combinations, then conducted analysis of the results of whichtype of combination that has a high accuracy. The results of this research indicate that theaccuracy of combination Naïve Bayes Classification and K-Means has a higher accuracythan the combination of Naïve Bayes Classification and Fuzzy C-Means. This can be seenfrom the combination accuracy of Fuzzy C-Means algorithm and Naïve Bayes Classifierof 96,67 while combination of K Means Clustering and Naïve Bayes Classifier algorithmis 98,33%, so it can be concluded that combination of K Means Clustering algorithm andNaïve Bayes Classifier is more recommended for determining the quality of beef cattle inSemarang regency.

scholarly journals Naïve Bayes Classifier for Journal Quartile Classification

2019 ◽  
Vol 7 (2) ◽  
pp. 91 ◽  
Author(s):  
Aji Prasetya Wibawa ◽  
Ahmad Chandra Kurniawan ◽  
Della Murbarani Prawidya Murti ◽  
Risky Perdana Adiperkasa ◽  
Sandika Maulana Putra ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Simple Algorithm ◽  
Bayes Theorem ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Previous Knowledge ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

<span>Classification is a process for distinguishing data classes, with the aim of being able to estimate the class of an object with unknown label. One popular method that used for classifying data is Naïve Bayes Classifier. Naïve Bayes Classifier is an approach that adopts the Bayes theorem, by combining previous knowledge with new knowledge. The advantages of this method are the simple algorithm and high accuracy. In this study, it will show the ability of Naïve Bayes Classifier to classify the quality of a journal commonly called Quartile. This study use a dataset of 1491 instances. The results show an accuracy of 71.60% and an error rate of 28.40%</span>

scholarly journals Enrichment of Extremely Noisy High-Throughput Screening Data Using a Naïve Bayes Classifier

2004 ◽  
Vol 9 (1) ◽  
pp. 32-36 ◽  
Author(s):  
Meir Glick ◽  
Anthony E. Klon ◽  
Pierre Acklin ◽  
John W. Davies
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Naive Bayes ◽  
Computing Time ◽  
Poor Quality ◽  
Naïve Bayes ◽  
Classification Model ◽  
Bayes Classifier ◽  
Short Computing Time

The noise level of a high-throughput screening (HTS) experiment depends on various factors such as the quality and robustness of the assay itself and the quality of the robotic platform. Screening of compound mixtures is noisier than screening single compounds per well. A classification model based on naïve Bayes (NB) may be used to enrich such data. The authors studied the ability of the NB classifier to prioritize noisy primary HTS data of compound mixtures (5 compounds/well) in 4 campaigns in which the percentage of noise presumed to be inactive compounds ranged between 81% and 91%. The top 10% of the compounds suggested by the classifier captured between 26% and 45% of the active compounds. These results are reasonable and useful, considering the poor quality of the training set and the short computing time that is needed to build and deploy the classifier. ( Journal of Biomolecular Screening 2004:32-36)

scholarly journals Automatic Question Generation (AQG) Dari Dokumen Teks Bahasa Indonesia Berdasarkan Non-Factoid Quesion

2018 ◽  
Vol 5 (2) ◽  
pp. 217
Author(s):  
Aminudin Aminudin ◽  
Azhari SN ◽  
Baaras Ahmad
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Question Generation ◽  
Knowledge Assets ◽  
The Right ◽  
The Given ◽  
Automatic Question Generation ◽  
F Measure ◽  
Bahasa Indonesia

<p class="Abstrak"><em><span lang="IN">Automatic Question Generation</span></em><span lang="IN"> (AQG) adalah sistem yang dapat membangkitkan pertanyaan secara otomatis dari teks atau dokumen dengan menggunakan metode atau pola-pola tertentu. Diharapkan sistem AQG yang dikembangkan bekerja seperti halnya manusia membuat pertanyaan setelah diberikan suatu teks. <span class="longtext"><span>Manusia dapat membuat pertanyaan, dikarenakan manusia dapat memahami teks yang diberikan dan berdasarkan pengetahuan-pengetahuan yang dimilikinya. Untuk mengembangkan sistem AQG penelitian ini, dilakukan kombinasi beberapa metode diantaranya algoritme <em>Naive Bayes Classifier</em> untuk mengklasifikasikan kalimat ke dalam jenis kalimat <em>non-factoid</em>. Chunking labelling untuk memberikan label pada masing-masing kalimat dari hasil klasifikasi dan pendekatan template untuk mencocokan hasil kalimat dengan template pertanyaan yang dibuat. Hasil pertanyaan yang dihasilkan oleh sistem akan diukur berdasarkan paramater yang telah ditentukan yang didasarkan atas pengukuran recall, precision dan F-Measure. Dengan adanya sistem AQG ini diharapkan dapat membantu guru mata pelajaran Biologi untuk membuat pertanyaan secara otomatis dan efektif serta efisien.</span></span></span></p><p class="Abstrak"> </p><p class="Abstrak">Abstract</p><p><em>Automatic Question Generation (AQG) is a system can generate question with automatically from text or document by using methods or certain patterns. Expected system AQG developed works like it does humans make create a question after being given a text. Humans can create a question, because humans can understand the given text and based on knowledge assets. To develop the system of AQG in this research, will do a combination of several methods including Naïve Bayes Clasifier algorithm to classify the sentence into a kind of non-sentence factoid. Chunking labelling to provide labels on each sentence and template approach to match the right results sentences with question templates created. The results of the question that are generated by the system will be measured based on predetermined parameters required that is based on the measuring precision, recall and F-Measure. With the existence of the AQG system is expected to help teachers of Biology subjects to make the question automatically, effectively and efficiently.</em></p>

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