Analysis of machine learning approaches to determine online shopping ratings using naïve bayes and svm Article Swipe
Muit Sunjaya
,
Zulham Sitorus
,
Khairul
,
Muhammad Naeem Iqbal
,
Andysah Putera Utama Siahaan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.61306/ijecom.v3i1.60
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.61306/ijecom.v3i1.60
This research aims to identify and compare the effectiveness of Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying ratings based on customer comments on the Lazada online shopping platform. The main issues identified include data uncertainty, model selection and optimization, as well as improving efficiency and scalability. Using a dataset of comments and reviews from Lazada, this study conducts an analysis using both algorithms to determine which is most effective in classifying comments into appropriate ratings. The research methodology includes data collection, text preprocessing, algorithm implementation, and evaluation using a confusion matrix to measure accuracy, precision, recall, and F-measure. This analysis is supported by data visualization using Python, allowing for in-depth interpretation and understanding of the results. The research findings show significant differences in the performance of both algorithms, with each having strengths in certain aspects of classification. The discussion in this study interprets these results to address the research questions formulated and demonstrates the practical application of machine learning theory in real-world data processing. This study concludes that both algorithms have significant potential in sentiment classification but require further adjustment and optimization to improve accuracy and efficiency. Recommendations for further research include the development of hybrid models or new approaches that can address the identified limitations, as well as exploration of more diverse datasets to test the scalability of the proposed solutions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.61306/ijecom.v3i1.60
- https://ijecom.org/index.php/IJECOM/article/download/60/62
- OA Status
- diamond
- Cited By
- 2
- References
- 28
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4399155489Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.61306/ijecom.v3i1.60Digital Object Identifier
- Title
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Analysis of machine learning approaches to determine online shopping ratings using naïve bayes and svmWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-30Full publication date if available
- Authors
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Muit Sunjaya, Zulham Sitorus, Khairul, Muhammad Naeem Iqbal, Andysah Putera Utama SiahaanList of authors in order
- Landing page
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https://doi.org/10.61306/ijecom.v3i1.60Publisher landing page
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https://ijecom.org/index.php/IJECOM/article/download/60/62Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ijecom.org/index.php/IJECOM/article/download/60/62Direct OA link when available
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Computer science, Machine learning, Naive Bayes classifier, Artificial intelligence, Support vector machine, Python (programming language), Confusion matrix, Preprocessor, Data mining, Scalability, Data pre-processing, Database, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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