Spell check Ignore button not functioning properly on non-touchscreen. The best accuracy for four labels is obtained when Ina-BWR is combined with BoW method. Support for Microsoft Word, Excel, and PowerPoint password-protected Office. The experimental results show that Ina-BWR can increase Personal, Subject and Relation label accuracies. To identify complaints, four label corpuses have been created manually. The submission file is in OpenOffice, Microsoft Word, or RTF document file format. Indonesian bigram word rule is adopted from opinion identification rules with 3 additional corpuses (-)NN, (-)JJ and (-)VB to identify student complaints. As part of the submission process, authors are required to check off. Additional preprocessing techniques are conducted to formalize the text such as parsing a hashtag, correcting affixes word, correcting a conjunction word, parsing suffix people pronoun and correcting typo words. Ina-BWR consists of three processes such as preprocessing informal text, identifying complaint and object from text. Compared to other default spell checkers, Word spell check and auto-correct are miles better. Like any decent word processor, Microsoft Word has a built-in spell checker.

Here is how to turn on or turn off spell check in Word. We develop Indonesian Bigram Word Rule for Multi-label Student Complaints (Ina-BWR) to identify multi-label student problems based on Bigram Word Rule. Microsoft Word has a built-in spell checker to correct spelling and grammar mistakes. To show the effectiveness of the proposed method, data from Telkom University student data and additional relevant data by using hashtag are used as testing data. In this research bigram word rule and preprocess are proposed to increase the accuracy of multi-label classification results. One of techniques used for handling multi-label student complaints is Bag of Word (BoW) method.

Handling multi-label student complaints is one of interesting research topics.

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