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Implementing Term Frequency-Inverse Term Frequency at Tweets in Indonesian Fraud Crime Cases

机译：在印度尼西亚欺诈犯罪案件的推文中实施术语频率反向术语频率

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摘要

Crime analysis is a methodical approach to identifying and analyzing patterns and trends in crime. Using crime analysis and text mining, we can analyze the modus operandi of crime to reduce the offenders. However, many fraud victims rarely report their problems to the police and tend to share their fraud case stories with their social media. Therefore, it needs a capable dataset to further analyze the fraud case. TFIDF as a weighting approach is used to find out the importance of a word. Thus, this study makes data derived from original data into data that can be processed for analysis. The data used are data from social media Twitter with 39,964 data that have keyword “penipuan” in Indonesian. This study uses text preprocessing techniques to clean the data from information which is not useful for the analysis process. The phases are data crawling, data cleansing, stemming, filtering, tokenizing, and visualizing data. After preprocessing data, the data will be processed into the terms frequency that appears and visualizes it. As a result, in the TF-IDF approach, the word “nomer” is the first for the word that often appear. It can be hypothesized that victims usually share their experiences of fraud that had related to the victim’s personal number.
机译：犯罪分析是一种识别和分析犯罪模式和趋势的方法方法。使用犯罪分析和文本挖掘，我们可以分析犯罪的模式，以减少违法者。然而，许多欺诈受害者很少向警方报告他们的问题，倾向于与他们的社交媒体分享他们的欺诈案件。因此，它需要一个有能力的数据集，以进一步分析欺诈案。 TFIDF作为加权方法用于找出单词的重要性。因此，本研究使得从原始数据导出的数据进入可以处理分析的数据。使用的数据是来自社交媒体Twitter的数据，其中39,964个数据在印度尼西亚的关键字“Penipuan”。本研究使用文本预处理技术来清除无法对分析过程无用的信息的数据。阶段是数据爬网，数据清洁，源，滤波，令牌化和可视化数据。在预处理数据之后，数据将被处理为出现并可视化它的术语频率。结果，在TF-IDF方法中，“Nomer”单词是通常出现的单词的第一个。可以假设受害者通常分享与受害者个人号码有关的欺诈经历。

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