Group Buying Application Mobile Based with Naïve Bayes Methods

2019 ◽  
Author(s):  
Alexander Setiawan ◽  
Anita Nathania ◽  
Andreas Gilbert Halim
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Group Buying ◽  
Bayes Methods

scholarly journals Analisis Metode K-Nearest Neighbors (K-NN) Dan Naive Bayes Dalam Memprediksi Kelulusan Mahasiswa

2021 ◽  
Vol 3 (2) ◽  
pp. 107-113
Author(s):  
Kartarina Kartarina ◽  
Ni Ketut Sriwinarti ◽  
Ni luh Putu Juniarti
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Entry And Exit ◽  
Accuracy Rate ◽  
Bayes Method ◽  
K Nearest Neighbors ◽  
Bayes Methods ◽  
Accuracy Test ◽  
Student Graduation ◽  
Selection Of

In this research the author aims to apply the K-NN and Naive Bayes algorithms for predicting student graduation rates at Sekolah Tinggi Pariwisata (STP) Mataram, The comparison of these two methods was carried out because based on several previous studies it was found that K-NN and Naive Bayes are well-known classification methods with a good level of accuracy. But which one has a better accuracy rate than the two algorithms, that's what researchers are trying to do. The output of this application is in the form of information on the prediction of student graduation, whether to graduate on time or not on time. The selection of STP as the research location was carried out because of the imbalance between the entry and exit of students who had completed their studies. Students who enter have a large number, but students who graduate on time according to the provisions are far very small, resulting in accumulation of the high number of students in each period of graduation, so it takes the initial predictions to quickly overcome these problems. Based on the results of designing, implementing, testing, and testing the Student Graduation Prediction Application program using the K-NN and Naive Bayes Methods with the Cross Validation method, the result is an accuracy for the K-NN method of 96.18% and for the Naive Bayes method an accuracy of 91.94% with using the RapideMiner accuracy test. So based on the results of the two tests between the K-NN and Naive Bayes methods which produce the highest accuracy, namely the K-NN method with an accuracy of 96.18%. So it can be concluded that the K-NN method is more feasible to use to predict student graduation

scholarly journals Sentiment Analysis of Online Transportation Service using the Naïve Bayes Methods

2020 ◽  
Vol 1641 ◽  
pp. 012093
Author(s):  
M Tika Adilah ◽  
Hendra Supendar ◽  
Rahayu Ningsih ◽  
Sri Muryani ◽  
Kusmayanti Solecha
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Methods ◽  
Transportation Service

scholarly journals Optimasi Naive Bayes Dengan Pemilihan Fitur Dan Pembobotan Gain Ratio

2016 ◽  
pp. 22
Author(s):  
I Guna Adi Socrates ◽  
Afrizal Laksita Akbar ◽  
Mohammad Sonhaji Akbar ◽  
Agus Zainal Arifin ◽  
Darlis Herumurti
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Feature Selection Method ◽  
Simple Algorithm ◽  
Selection Method ◽  
Naïve Bayes ◽  
Computation Complexity ◽  
Bayes Method ◽  
Gain Ratio ◽  
Bayes Methods

Naïve Bayes is one of data mining methods that are commonly used in text-based document classification. The advantage of this method is a simple algorithm with low computation complexity. However, there is weaknesses on Naïve Bayes methods where independence of Naïve Bayes features can’t be always implemented that would affect the accuracy of the calculation. Therefore, Naïve Bayes methods need to be optimized by assigning weights using Gain Ratio on its features. However, assigning weights on Naïve Bayes’s features cause problems in calculating the probability of each document which is caused by there are many features in the document that not represent the tested class. Therefore, the weighting Naïve Bayes is still not optimal. This paper proposes optimization of Naïve Bayes method using weighted by Gain Ratio and feature selection method in the case of text classification. Results of this study pointed-out that Naïve Bayes optimization using feature selection and weighting produces accuracy of 94%.

scholarly journals Comparative Analysis of K-NN and Naïve Bayes Methods to Predict Stock Prices

2021 ◽  
Vol 2 (2) ◽  
pp. 49-53
Author(s):  
Budi Soepriyanto
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Share Price ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Bayes Methods

Abstract— Buying and selling shares is a transaction that is widely carried out at this time, especially buying and selling stocks online which are widely available in the market, to make buying and selling shares require ability or knowledge so that the buying and selling of shares are profitable, to be able to help economic players predict prices. Profit shares or not purchased in the future, this research will conduct stock price predictions using classification methods, namely K-Nearest Neighbor and Naïve Bayes, to predict the stock price data used for one month in minute levels totalling 39065 data, based on prediction results. The highest results obtained were using Naïve Bayes with an accuracy value of 69.38 then the K-Nearest Neighbor method with a K = 5 value of 67.25%, based on these results it can be concluded that the use of the K-Nearest Neighbor and Naïve Bayes methods for prediction share price not yet owned I high accuracy, so it can be combined with other methods or by using other variable predictors.

Sentiment Analysis of Snack Review Using the Naïve Bayes Method

2020 ◽  
Vol 8 (3) ◽  
pp. 333
Author(s):  
I Gede Cahya Purnama Yasa ◽  
Ngurah Agus Sanjaya ER ◽  
Luh Arida Ayu Rahning Putri
Keyword(s):  
Sentiment Analysis ◽  
Fast Food ◽  
Naive Bayes ◽  
Food Products ◽  
Naïve Bayes ◽  
Level Of Service ◽  
Snack Food ◽  
Bayes Method ◽  
Bayes Methods

Fast food is a product that we often encounter in stores such as convenience stores. Ready-to-eat products can now be easily found by consumers. One of the reason is due to the expansion of minimarkets in areas that are easily reached, such as housing complexes, school areas, and offices. Sentiment analysis is used to determine whether an opinion or comment on a product has a positive or negative interest and can be used as a reference in improving service, or improving product quality. In this research, we study the sentiments of consumers towards snack food products as a reference to improve the level of service and quality of these products.. We classify the sentiment of a review on snack food products as positive and negative. To classify the sentiments we apply the Naïve Bayes and Multinomial Naïve Bayes methods. We compare the two methods to study the most effective and efficient method for classifying sentiments on reviews of snack food products. Keywords: Sentiment Analysis, TF-IDF, Naïve Bayes,Multinomial, Review, Snack, Preprocessing

scholarly journals Comparison J48 And Naïve Bayes Methods in Educational Analysis

2021 ◽  
Vol 1933 (1) ◽  
pp. 012062
Author(s):  
Agung Triayudi ◽  
Wahyu Oktri Widyarto
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Methods
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