ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION
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 cel...
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| Format: | Article |
| Language: | English |
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Kresnamedia Publisher
2021-12-01
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| Series: | Jurnal Riset Informatika |
| Subjects: | |
| Online Access: | https://ejournal.kresnamediapublisher.com/index.php/jri/article/view/279 |
| _version_ | 1798020405736243200 |
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| author | Endang Sri Palupi |
| author_facet | Endang Sri Palupi |
| author_sort | Endang Sri Palupi |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-04-11T16:56:58Z |
| format | Article |
| id | doaj.art-ad25529fced9408fa1e108db37d5fe33 |
| institution | Directory Open Access Journal |
| issn | 2656-1743 2656-1735 |
| language | English |
| last_indexed | 2024-04-11T16:56:58Z |
| publishDate | 2021-12-01 |
| publisher | Kresnamedia Publisher |
| record_format | Article |
| series | Jurnal Riset Informatika |
| spelling | doaj.art-ad25529fced9408fa1e108db37d5fe332022-12-22T04:13:14ZengKresnamedia PublisherJurnal Riset Informatika2656-17432656-17352021-12-0141232810.34288/jri.v4i1.279279ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATIONEndang Sri Palupi0Universitas Bina Sarana InformatikaDuring 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%.https://ejournal.kresnamediapublisher.com/index.php/jri/article/view/279k-nearest neighbornaïve bayes |
| spellingShingle | Endang Sri Palupi ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION Jurnal Riset Informatika k-nearest neighbor naïve bayes |
| title | ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION |
| title_full | ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION |
| title_fullStr | ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION |
| title_full_unstemmed | ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION |
| title_short | ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION |
| title_sort | android sales prediction during pandemic using naive bayes and k nn methods based on particle swarm optimization |
| topic | k-nearest neighbor naïve bayes |
| url | https://ejournal.kresnamediapublisher.com/index.php/jri/article/view/279 |
| work_keys_str_mv | AT endangsripalupi androidsalespredictionduringpandemicusingnaivebayesandknnmethodsbasedonparticleswarmoptimization |