Implementasi Sistem Crowdsourced Labelling Berbasis Web dengan Metode Weighted Majority Voting

Antonius Rachmat, Yuan Lukito

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Crowdsourced Labelling is a large scale data labelling process, solicits a large group of people to label the data, usually via Internet.  This paper discusses about design and implementation of Web-based Crowdsourced Labelling.  Supervised learning classification methods need labelled training data for its training phase.  Unfortunately, in many cases, there aren’t any already available labelled training data.  Large scale data labelling is a tedious and time consuming work.  This research develops a web-based crowdsourced labelling which able to solicit a large group of people as data labeler to speed up the data labelling process.  This system also allows multiple labeler for every data.  The final label is calculated using Weighted Majority Voting method.  We grabbed and used Facebook comments from the two candidates’ Facebook Page of 2014 Indonesian Presidential Election as testing data.  Based on the testing conducted we can conclude that this system is able to handle all the labelling steps well and able to handle collision occurred when multiple labeler labelling a same data in the same time. The system successfully produces final label in CSV format, which can be processed further with many sentiment analysis tools or machine learning tools.

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