scholarly journals ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION

2021 ◽  
Vol 4 (1) ◽  
pp. 23-28
Author(s):  
Endang Sri Palupi
Keyword(s):  
Particle Swarm Optimization ◽  
Online Teaching ◽  
Teaching And Learning ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Learning Activities ◽  
Swarm Optimization ◽  
Sales Prediction ◽  
Used Goods

During the pandemic, most schools, campuses, and places of education conducted online teaching and learning activities. Many teaching and learning activities are carried out using the Zoom, Google, WebEx, or Microsoft Teams applications. All of this can be done through a PC or laptop, or using a cellphone, so the need for PCs and cellphones increases, both new and used goods. Even though during the pandemic the economic situation was declining, many companies suffered losses, resulting in a reduction in employees and causing a high unemployment rate, the need for Android phones remains high. In addition to online distance learning facilities, Android phones can also be used for online sales through e-commerce, market places, social media, and other digital platforms. Currently, Android phones have many choices and according to the funds we have, with various brands and specifications. Many brands issue android cellphone products with pretty good specifications and affordable prices, so that even though purchasing power has decreased due to the pandemic, sales of android cellphones are still high. In this study, the author predicts the highest sales of android cellphones using the Naïve Bayes method and the K-Nearest Neighbor method based on Particle Swarm Optimization accuracy of 81.33%.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

scholarly journals SENTIMENT ANALYSIS OF COSMETIC REVIEW USING NAIVE BAYES AND SUPPORT VECTOR MACHINE METHOD BASED ON PARTICLE SWARM OPTIMIZATION

2020 ◽  
Vol 2 (3) ◽  
pp. 169-178
Author(s):  
Zulia Imami Alfianti ◽  
Deni Gunawan ◽  
Ahmad Fikri Amin
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Support Vector ◽  
Swarm Optimization ◽  
Advantages And Disadvantages ◽  
Improve Accuracy

Sentiment analysis is an area of ​​approach that solves problems by using reviews from various relevant scientific perspectives. Reading a review before buying a product is very important to know the advantages and disadvantages of the products we will use, besides reading a cosmetic review can find out the quality of the cosmetic brand is feasible or not be used. Before consumers decide to buy cosmetics, consumers should know in detail the products to be purchased, this can be learned from the testimonials or the results of reviews from consumers who have bought and used the previous product. The number of reviews is certainly very much making consumers reluctant to read reviews. Eventually, the reviews become useless. For this reason, the authors classify based on positive and negative classes, so consumers can find product comparisons quickly and precisely. The implementation of Particle Swarm Optimization (PSO) optimization can improve the accuracy of the Support Vector Machine (SVM) and Naïve Bayes (NB) algorithm can improve accuracy and provide solutions to the review classification problem to be more accurate and optimal. Comparison of accuracy resulting from testing this data is an SVM algorithm of 89.20% and AUC of 0.973, then compared to SVM based on PSO with an accuracy of 94.60% and AUC of 0.985. The results of testing the data for the NB algorithm are 88.50% accuracy and AUC is 0.536, then the accuracy is compared with the PSO based NB for 0.692. In these calculations prove that the application of PSO optimization can improve accuracy and provide more accurate and optimal solutions

scholarly journals ANALISA NASABAH POTENSIAL TABUNGAN DEPOSITO BERJANGKA MENGGUNAKAN TEKNIK KLASIFIKASI DATA MINING

2019 ◽  
Vol 5 (2) ◽  
pp. 105-112
Author(s):  
Candra Agustina
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Financial Institution ◽  
Unified Modeling Language ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Time Deposit ◽  
Bayes Algorithm ◽  
Potential Customers

Time deposits are a product of a financial institution, which is currently increasing. The main target of this time deposit marketing is the old customers of the Bank. To increase the effectiveness of marketing customers are grouped into potential and non-potential customers. This means that potential customers have a greater chance to open a time deposit account. Customer data is taken from the UCI repository, originating from Banks in Portugal. Data is processed with rapidminer software using the Decision Tree method with Particle Swarm Optimization, Naïve Bayes with Particle Swarm Optimization and finally processed using Neural Network with Particle Swarm Optimization. Data processing results were compared and showed that the Naïve Bayes Algorithm with Particle Swarm Optimization had the highest accuracy of 97.04%. Therefore an application designed based on Naive Bayes with Particle Swarm Optimization. From the original attribute consisting of 20, only 9 attributes can be used so that the level of accuracy is high. Attributes used have values ​​more than 0.500, while those that have these values ​​are omitted. The design was created using the Unified Modeling Language (UML) and Visual Basic 6.0 to create an User Interface.

scholarly journals Implemented PSO-NBC and PSO-SVM to Help Determine Status of Volcanoes

2019 ◽  
Vol 9 (2) ◽  
pp. 97
Author(s):  
Firman Tempola
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Swarm Optimization ◽  
Attribute Weights ◽  
The Status ◽  
Bayes Algorithm

<p class="JGI-AbstractIsi">This research is a continuation of previous research that applied the Naive Bayes classifier algorithm to predict the status of volcanoes in Indonesia based on seismic factors. There are five attributes used in predicting the status of volcanoes, namely the status of the normal, standby and alerts. The results Showed the accuracy of the resulted prediction was only 79.31%, or fell into fair classification. To overcome these weaknesses and in order to increase accuracy, optimization is done by giving criteria or attribute weights using particle swarm optimization. This research compared the optimization of Naive Bayes algorithm to vector machine support using particle swarm optimization. The research found improvement on system after application of PSO-NBC to that of 91.3 % and 92.86% after applying PSO-SVM.</p>

scholarly journals Data Mining Optimization Using Sample Bootstrapping and Particle Swarm Optimization in the Credit Approval Classification

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Andre Alvi Agustian ◽  
Achmad Bisri
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Credit Card ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Class Imbalance ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
The Status ◽  
Auc Value

Credit approval is a process carried out by the bank or credit provider company. Where the process is carried out based on credit requests and credit proposals from the borrower. Credit approval is often difficult for banks or credit providers. Where the number of requests and classifications must be made on various data submitted. This study aims to enable banks or credit card issuing companies to carry out credit approval processes effectively and accurately in determining the status of the submissions that have been made. This research uses data mining techniques. This study uses a Credit Approval dataset from UCI Machine Learning, where there is a class imbalance in the dataset. 14 attributes are used as system inputs. This study uses the C4.5 and Naive Bayes algorithms where optimization is needed using Sample Bootstrapping and Particle Swarm Optimization (PSO) in the algorithm so that the results of the research produce good accuracy and are included in the good classification. After using the optimization, it produces an accuracy rate of C4.5 which is initially 85.99% and the AUC value of 0.904 becomes 94.44% with the AUC value of 0.969 and Naive Bayes which initially has an accuracy value of 83.09% with an AUC value of 0.916 to 90 , 10% with an AUC value of 0.944.

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