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Ahmad Efriza Irsad
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Seleksi Fitur Information Gain untuk Klasifikasi Informasi Tempat Tinggal di Kota Malang Berdasarkan Tweet Menggunakan Metode Naive Bayes dan Pembobotan TF-IDF-CF Ahmad Efriza Irsad; Yuita Arum Sari; M. Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Malang city is a city that has a significant increase in population, which is around 50 thousands people in just period of 5 years. One of the reasons is because Malang city is a city of education, the reasons its called city of education is because in this City there are a lot of public university and private university that are quite popular, such as Universitas Brawijaya (UB), Universitas Islam Malang (Unisma), etc. This resulted many migrants from outside the area of Malang city study in Malang city. There are some things that might be the reasons why migrants choose Malang city, such as the Malang city have one of the best quality university in Indonesia. When becoming a migrant, the most needed thing is certainly a place to live in a long term, because of that the migrants need information on where to live in the form of boarding house or rent house to live in, we can get this kind of information trough social media like Twitter, but on Twitter there is still no category for this kind of information. By seeing this problem, we can use Classification technique to classified the information in the form of living quarters in the city of Malang. In this study Naive Bayes method is used as the classification method, and Information gain as the feature selection method. Before entering the classification process the weighting is done first using TF-IDF-CF method. This study uses 150 training data and 60 testing data. The highest accuracy value in this study are 71,66% using 33% of feature, using TF-IDF-ICF weighthing and, without using number feature.
Co-Authors M. Ali Fauzi Yuita Arum Sari