Comparison of preprocessing approaches for text data in digital shop floor management systems

Müller, Mark-Christoph; Longard, Lukas; Metternich, Joachim

doi:10.1016/j.procir.2022.04.030

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…By performing the steps in the figure above, the data becomes more structured, clean, and ready to use, improving the accuracy and performance of the model, and avoiding potential distortions or errors in the analysis results [21]. This research uses K-NN, Naive Bayes, and ANN classification algorithms.…”

Section: Methodsmentioning

confidence: 99%

Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms

Muta'alimah,

Zarry,

Kurniawan

et al. 2024

PREDATECS

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Advancements in technology have made online shopping popular among many. However, the use of offline marketing models is still considered a profitable and important way of business development. This can be seen in the 2022 Association of Retail Entrepreneurs of Indonesia (APRINDO), which states that 60% of Indonesians shop offline, and in 2023, more than 75% of continental European consumers will prefer to shop offline. This is because many benefits can be achieved through offline marketing that cannot be obtained from online marketing. Therefore, classification of patterns and trends is performed to compare the results of the algorithms under study. Furthermore, this research was conducted to help offline retailers understand consumption patterns and trends that affect purchases. The algorithms analyzed in this study are K-Nearest Neighbor (K-NN), Naive Bayes, and Artificial Neural Network (ANN). As a result, the ANN algorithm obtained the highest confusion matrix results with an Accuracy value of 96.38%, Precision of 100.00%, and Recall of 100.00%. Meanwhile, when the Naive Bayes algorithm was used, the lowest Accuracy value was 57.39%, the Precision value was 57.86%, and when the K-NN algorithm was used, the Recall value was as low as 92.00%. These results indicate that the ANN algorithm is better at classifying offline shopping image data than the K-NN and Naive Bayes algorithms

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Section: Methodsmentioning

confidence: 99%

Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms

Muta'alimah,

Zarry,

Kurniawan

et al. 2024

PREDATECS

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show abstract

“…The tokenization stage separates words in sentences based on spaces, enter, tabulation, commas and periods. From this separation, tokenization will produce a term [7].…”

Section: Tokenizationmentioning

confidence: 99%

Essay Answer Classification With Smote Random Forest and Adaboost in Automated Essay Scoring

Satria,

Riasetiawan

2023

Indonesian J. Comput. Cybern. Syst.

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Automated essay scoring (AES) is used to evaluate and assessment student essays are written based on the questions given. However, there are difficulties in conducting automatic assessments carried out by the system, these difficulties occur due to typing errors (typos), the use of regional languages , or incorrect punctuation. These errors make the assessment less consistent and accurate. Based on the dataset analysis that has been carried out, there is an imbalance between the number of right and wrong answers, so a technique is needed to overcome the data imbalance. Based on the literature, to overcome these problems, the Random Forest and AdaBoost classification algorithms can be used to improve the consistency of classification accuracy and the SMOTE method to overcome data imbalances.The Random Forest method using SMOTE can achieve an F1 measure of 99%, which means that the hybrid method can overcome the problem of imbalanced datasets that are limited to AES. The AdaBoost model with SMOTE produces the highest F1 measure reaching 99% of the entire dataset. The structure of the dataset is something that also affects the performance of the model. So the best model obtained in this study is the Random Forest model with SMOTE.

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Faradaic deionization technology: Insights from bibliometric, data mining and machine learning approaches

et al. 2023

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Comparison of preprocessing approaches for text data in digital shop floor management systems

Cited by 7 publications

References 14 publications

Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms

Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms

Essay Answer Classification With Smote Random Forest and Adaboost in Automated Essay Scoring

Faradaic deionization technology: Insights from bibliometric, data mining and machine learning approaches

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