Fake news is a problem faced by society in this era. It is not rare for fake news to cause provocation and problem for the people. Indonesia, as a country with the 4th largest population, has a problem in dealing with fake news. More than 30% of rural and urban population are deceived by this fake news problem. As we have been studying, there is only few literatures on preventing the spread of fake news in Bahasa Indonesia. So, this research is conducted to prevent these problems. The dataset used in this research was obtained from a news portal that identifies fake news, turnbackhoax.id. Using Web Scrapping on this page, we got 1116 data consisting of valid news and fake news. The dataset can be accessed at https://github.com/JibranFawaid/turnbackhoax-dataset. This dataset will be combined with other available datasets. The methods used are CNN, BiLSTM, Hybrid CNN-BiLSTM, and BERT with Transformer Network. This research shows that the BERT method with Transformer Network has the best results with an accuracy of up to 90%.
翻译:假消息是社会在这个时代所面临的一个问题。 假消息引起人们的挑衅和问题并不罕见。 印度尼西亚是人口第四大国,在处理假新闻方面有问题。 超过30%的农村和城市人口被这个假新闻问题蒙骗了。 我们研究过,防止假新闻在印度尼西亚巴哈萨传播的文献很少。 因此, 进行这项研究是为了防止这些问题。 本研究中使用的数据集是从一个新闻门户获得的,该门户识别假新闻, turnsfackhoax.id。 利用网页的剪贴,我们得到了由有效新闻和假新闻组成的1116个数据。 数据集可以在 https://github.com/ JibranFawaid/wornackbackhoax- dataset 上查阅。 这个数据集将与其他可用的数据集合并。 所使用的方法是CNN、 BILSTM、 CompliedCNN-BILSTM和BERT与变换者网络。 这项研究显示, 变换者网络的BERT方法的准确率高达90 % 。