用k近邻法和卡方法对印尼语翻译中的圣训题目进行分类

Ghinaa Zain Nabiilah, Said Al Faraby, Mahendra Dwifebri Purbolaksono
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引用次数: 0

摘要

圣训是穆斯林除了古兰经之外的主要生活方式,可以应用于日常生活。圣训还包含先知穆罕默德的所有言行,这些言行被用作伊斯兰教法的来源。因此,许多读者,尤其是穆斯林,对研究圣训很感兴趣。然而,大量的圣训使得读者或不熟悉伊斯兰教的人很难阅读它们。因此,我们进行了一项研究,将圣训按教学类型进行文本分类,以便读者在阅读和搜索基于教学类型的圣训时更容易获得概述或其他参考。本研究使用KNN和卡方方法作为特征选择。我们还进行了几个测试场景,包括在预处理中实现停止词去除修改,以及为KNN选择k值以确定最佳性能的实验。在KNN上使用k = 7,不使用卡方,并进行停止词去除修改,锤子损失值为0.1042,约占正确分类数据的89.58%,获得了最佳性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Hadith Topic of Indonesian Translation Using K-Nearest Neighbor and Chi-Square
Hadith is the main way of life for Muslims besides the Qur'an whose can be applied in everyday life. Hadith also contains all the words or deeds of the Prophet Muhammad which are used as a source of the law of Islam. Therefore, many readers, especially Muslims, are interested in studying hadith. However, the large number of hadiths makes it difficult for readers or those who are still unfamiliar with Islam to read them. Therefore, we conducted a study to classify hadith textually based on the type of teaching, so that readers can get an overview or other reference in reading and searching for hadith based on the type of teaching more easily. This study uses KNN and chi-square methods as feature selection. We also carried out several test scenarios, including implementing stopword removal modifications in preprocessing and experimenting with selecting k values ​​for KNN to determine the best performance. The best performance was obtained by using the value of k = 7 on KNN without implementing chi-square and with stopword removal modification with a hammer loss value of 0.1042 or about 89.58% of the data correctly classified.
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