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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Riska Dewi Nurfarida
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Kemacetan Lalu Lintas di Kota Malang Pada Sosial Media Twitter Menggunakan Metode Improved K-Nearest Neighbor Riska Dewi Nurfarida; Indriati Indriati; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a social media network that has many users that can be used for communication media. And from Twitter you can also get various forms of information including negative and positive opinions and various other types of information. One of the information that can be obtained from Twitter is information about traffic conditions. Malang City community uses Twitter social media as one of the media to get information about traffic conditions. Through the @PuspitaFM account, the people of Malang City share information about the state of traffic around them. From the @PuspitaFM account, every day I will share tweets about traffic conditions in Malang City either by tweeting directly or tweets from followers that will be retweeted by the @PuspitaFM account. Of all the tweets that exist, sometimes there is confusion that occurs in the categorization of traffic jams or not jammed in the tweet. Therefore, the classification of tweets is jammed or not jammed as a solution to the problem. There are several processes carried out in this study, namely starting from prepocessing text which is divided into cleansing, case folding, tokenisation, filtering and stemming processes. The process will continue with the term weighting or weighting process, followed by normalization, cosine similiarity and classification processes with the Improved K-NN method. The results obtained from this study are recall value of 0.42857, precision value of 0.71428, f-measure value of 0.53571 and the best accuracy of 65.33%. The training data used is 600 tweet documents, and 150 test data tweet documents.
Co-Authors Indriati Indriati Rizal Setya Perdana