Optimization of SVM Classification Accuracy with Bayesian Optimization Utilizing Data Augmentation

Lubis, Arif Ridho; Lase, Yuyun Yusnida; R, Darwis A.; Witarsyah, Deden

doi:10.1109/ic2ie60547.2023.10331580

Cited by 1 publication

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several previous studies that look at Naive Bayes in solving problems [17][18] [19]. One of them is research that discusses the use of data mining to predict the number of best selling products carried out using the Naive Bayes algorithm with the conclusion that the probability of each attribute is calculated in Quarter 1, Quarter 2, Quarter 3, and Quarter 4 [12][20].…”

Section: Introductionmentioning

confidence: 99%

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

Yuyun,

Silaban,

Sitepu

et al. 2024

ENIGMA

View full text Add to dashboard Cite

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

show abstract

Section: Introductionmentioning

confidence: 99%