Classifying News Based on Indonesian News Using LightGBM

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI:10.1109/CENIM56801.2022.10037401

Arif Ridho Lubis, S. Prayudani, Y. Fatmi, Okvi Nugroho

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引用次数: 6

Abstract

There were several news categories that present editors with challenges. Some news categories, such megapolitan, national, celebrity, news, lifestyle, and economics, used vocabulary that was quite similar to that of the other categories. International required an editor to be familiar with the article's contents in order for it to be uploaded and placed in the proper category. We had to first digest the news before we could label it compared to other kinds of data. The text mining approach, which attempts to make text or documents may be processed so that it will aid in the process of news classification, will be used to categorize and determine the type of news in this context. The Light Gradient Boosted Machine (LightGBM) model was used in this study to increase the gradient point with a learning stage and obtain the optimal value. This model's training process was intended to be quick while consuming less storage space and processing information more accurately. The accuracy of the classifications made using a confusion matrix to quantify the findings of this investigation, which were news type classifications, was 86%.

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基于LightGBM的印尼新闻分类

有几个新闻类别给编辑带来了挑战。一些新闻类别，如大都市、国家、名人、新闻、生活方式和经济，使用的词汇与其他类别非常相似。国际要求编辑熟悉文章的内容，以便将其上传并放在适当的类别中。我们必须首先消化新闻，然后才能将其与其他类型的数据进行比较。文本挖掘方法试图使文本或文档可以被处理，从而有助于新闻分类的过程，将用于在此上下文中对新闻进行分类和确定新闻的类型。本研究采用光梯度增强机(Light Gradient boosting Machine, LightGBM)模型，通过一个学习阶段增加梯度点，获得最优值。该模型的训练过程旨在快速，同时消耗更少的存储空间和更准确地处理信息。使用混淆矩阵来量化本调查结果的分类的准确性为86%，这是新闻类型分类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)

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