Classification Analysis of Product Sales Results at Alfamart Using the Naïve Bayes Method DOI Creative Commons

Yuyun Yusnida Lase Yuyun,

Citra Wasti Silaban,

Alex Sander Sitepu

et al.

Electronic Integrated Computer Algorithm Journal, Journal Year: 2024, Volume and Issue: 1(2), P. 69 - 74

Published: April 19, 2024

This research focuses on the analysis of number products sold, especially stock items from distribution center to Alfamart stores. The main problem discussed in this study is result unsold and sold products, which causes overstocking warehouse area. To overcome problem, it will be solved using Naive Bayes classification method. uses sample data 100 collection techniques such as observation interviews. collected analysed through a approach. aims predict goods that sell do not Rapidminer NaïveBayes And produce more accurate for product sales process. reason naïve bayes algorithm process processing analysing because way works statistical methods probability predicting future results. validation results show method implemented provides significant explanation with fairly high accuracy positive effect prediction based consumer demand needs.

Language: Английский

Sentiment Analysis for Political Debates on YouTube Comments using BERT Labeling, Random Oversampling, and Multinomial Naïve Bayes DOI Creative Commons
Apriandy Angdresey, Lanny Sitanayah,

Ignatius Lucky Henokh Tangka

et al.

Journal of Computing Theories and Applications, Journal Year: 2025, Volume and Issue: 2(3), P. 342 - 354

Published: Jan. 1, 2025

The 2024 Indonesian Presidential Election marked the fifth general election in country, aimed at electing a new President and Vice for 2024–2029 term. Candidates competed to succeed outgoing president, who had served two constitutional terms. A key aspect of this was candidate debates, where each presented their vision, allowing public assess policies. These debates were broadcast on platforms like YouTube, giving space comment. However, analyzing YouTube comments presents challenges due volume data, language diversity, informal expressions. Sentiment analysis, crucial understanding opinion, uses algorithms such as Naïve Bayes, which is based Bayes' Theorem assumes feature independence. Bayes widely used text analysis its speed simplicity. When applied from algorithm demonstrated effectiveness, especially with balanced dataset through random oversampling. It achieved 85.155% accuracy, high precision, recall, an AUC 96.8% 80:20 data split. Its fast classification time (0.000998 seconds) makes it suitable real-time sentiment validating use political events. Future applications may incorporate advanced techniques BERT more sophisticated analysis.

Language: Английский

Citations

0
Classification Analysis of Product Sales Results at Alfamart Using the Naïve Bayes Method DOI Creative Commons

Yuyun Yusnida Lase Yuyun,

Citra Wasti Silaban,

Alex Sander Sitepu

et al.

Electronic Integrated Computer Algorithm Journal, Journal Year: 2024, Volume and Issue: 1(2), P. 69 - 74

Published: April 19, 2024

This research focuses on the analysis of number products sold, especially stock items from distribution center to Alfamart stores. The main problem discussed in this study is result unsold and sold products, which causes overstocking warehouse area. To overcome problem, it will be solved using Naive Bayes classification method. uses sample data 100 collection techniques such as observation interviews. collected analysed through a approach. aims predict goods that sell do not Rapidminer NaïveBayes And produce more accurate for product sales process. reason naïve bayes algorithm process processing analysing because way works statistical methods probability predicting future results. validation results show method implemented provides significant explanation with fairly high accuracy positive effect prediction based consumer demand needs.

Language: Английский

Citations

0