[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119576-en":3,"doc-seo-119576-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119576,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Multiclass Classification of Marketplace Products with Machine Learning - research article","Marketplace product data combined with machine learning enables Statistics Indonesia to expand commodity directories across surveys through automated product categorization. This study trains a multiclass classification model to assign new product records into KBKI categories using a dataset of 32,000+ products from 26 classes sourced from two major Indonesian marketplaces. Classification performance is evaluated using Random Forests, Support Vector Machines, and Multinomial Naive Bayes. Results show Multinomial Naive Bayes delivers the best accuracy–time trade-off, with micro-average F1 up to 95.4% and execution time around 5 seconds.","MULTICLASS CLASSIFICATION OF MARKETPLACE PRODUCTS WITH  \nMACHINE LEARNING  \nFarhan Satria Aditama1,3, Dewi Krismawati2, Setia Pramana3  \n1 Directorate of Statistical Dissemination, BPS Statistics Indonesia, Jakarta, Indonesia  \n2 Directorate of Analysis and Statistics Development, BPS Statistics Indonesia, Jakarta, Indonesia  \n3 Politeknik Statistika STIS, Jakarta, Indonesia  \ne-mail: [farhan.satria@bps.go.id](farhan.satria@bps.go.id)  \n[DOI: 10.14710/medstat.17.1.25-35](DOI: 10.14710/medstat.17.1.25-35)  \nArticle Info:  \nReceived: 8 January 2024  \nAccepted: 20 September 2024  \nAvailable Online: 14 October 2024  \nKeywords:  \nMachine Learning, Marketplace, Multiclass Classification.  \nAbstract: The use of marketplace data and machine learning in the collection of commodity data can provide an opportunity for Statistics Indonesia to complete the commodity directories for various surveys. This research adopts machine learning to train a product classification model based on existing datasets to predict whether a new dataset falls into which KBKI category. The dataset contains more than 32,000 products from 26 classes consisting of product data from two biggest marketplaces in Indonesia. Algorithms used for classification include Random Forests (RF), Support Vector Machines (SVM), and Multinomial Naive Bayes (MNB) . Results indicate that MNB is the most effective algorithm when considering the trade-off between accuracy and processing time. MNB achieved the highest micro-average F1 scores, with 91.8% for Tokopedia and 95.4% for Shopee, and has the fastest execution time approximately 5 seconds.  \n1. INTRODUCTION  \nBPS Statistics Indonesia has initiated the utilization of big data sources to modernize its statistics business process (Saleh et al., 2019) . Big data offers an innovative source of information that provides deeper insight into the production of official statistics (Badan Pusat Statistik, 2020). Commodity data collection by BPS (Badan Pusat Statistik) is very important to obtain accurate and comprehensive information regarding the production and consumption of certain commodities in Indonesia. Collecting data from marketplaces offers a more efficient and reliable approach, especially in situations requiring a quick response. Marketplace data also provides valuable insights into ongoing phenomena (Srimulyani et al., 2021) .  \nHowever, utilizing marketplace data for official statistics presents several challenges, particularly in the classification of products into standard categories such as the Standard Classification of Indonesian Commodities (KBKI) . The KBKI system is used to categorize various goods and services traded in Indonesia, facilitating the organization of trade data and supporting the collection of statistical information (Badan Pusat Statistik, 2012) . Effective  \nclassification is critical for ensuring the accuracy and relevance of the data used in economic analysis.  \nThe primary challenges in product classification include the unbalanced distribution of product categories, inconsistent product descriptions provided by sellers, and the high dimensionality of the classification task due to the large number of categories. These challenges complicate the classification process, increasing the need for sophisticated methods to handle complex and large-scale datasets (Yu et al., 2018) .  \nTo address these challenges, this research proposes the development of machine learning models tailored to the characteristics of marketplace data. By applying algorithms such as Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), and Random Forest (RF), this research aims to improve the accuracy and efficiency of product classification based on the KBKI system. These algorithms are selected for their proven effectiveness in text classification tasks, with each offering distinct advantages in handling different aspects of the classification problem (Laksana & Purwarianti, 2014) .  \nThis study contributes to the fie","cbCaisbElLaVxZeT","https://ap.wps.com/l/cbCaisbElLaVxZeT","pdf",319023,1,11,"English","en",105,"# Introduction\n## Problem background: KBKI-based product classification\n## Challenges in multiclass product classification\n## Proposed machine learning approach\n## Related multiclass studies\n# Literature Review\n## Multiclass classification model and algorithms","[{\"question\":\"What task does the multiclass classification model perform in this research?\",\"answer\":\"It predicts the KBKI category for new marketplace product data by learning from existing labeled datasets.\"},{\"question\":\"Which algorithms are compared for product classification?\",\"answer\":\"Random Forest (RF), Support Vector Machines (SVM), and Multinomial Naive Bayes (MNB) are used and evaluated.\"},{\"question\":\"Why is Multinomial Naive Bayes considered the best option in the results?\",\"answer\":\"MNB achieves the highest micro-average F1 scores while also having the fastest execution time, about 5 seconds.\"}]","Multiclass Classification of Marketplace Products with Machine Learning - 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