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Tugas Akhir Informatika

KLASIFIKASI ARTIKEL BERDASARKAN TINGKATAN UMUR PEMBACA MENGGUNAKAN METODE MULTINOMIAL NAÏVE BAYES CLASSIFIER

The availability of various Indonesian-language
articles on the internet allows anyone to easily access the
reading sources such as documents that can be downloaded
and articles available on a website. People who access the
article can also come from diverse ages of readers, namely the
age groups of children, adolescents and adults. Therefore, the
classification of the article can be applied so that the article
can be categorized according to the target age of the reader.
The article classification process is done using the text mining
method, which is by applying the term frequency and inverse
document frequency (TF-IDF) features as well as the
Multinomial Naive Bayes Classifier algorithm. In this study,
the article data used was sourced from 3 sites namely,
bobo.grid.id which is a site with the target of elementary
school children, for the category of teenagers aged 15-24
years obtained from the site hai.grid.id, while for the
category of groups Adult age is obtained from the site
www.detik.com. The results obtained in this study are 93%
accuracy, 94% precision, and 93% recall.

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Informasi Detail
Judul Seri
-
No. Panggil
519.535Muh.K
Penerbit
Universitas Mataram : Fakultas Teknik Unram.,
Deskripsi Fisik
xiii,57 Hlm: Illus: 21x29 cm
Bahasa
Indonesia
ISBN/ISSN
-
Klasifikasi
519.535
Tipe Isi
other
Tipe Media
computer
Tipe Pembawa
audio disc
Edisi
Edisi 1 Jilid 1
Subjek
Klasifikasi Artikel Berdasarkan Tingkatan Umur Pem
Document Classification, Text Mining, Document Ret
Info Detail Spesifik
KLASIFIKASI ARTIKEL BERDASARKAN TINGKATAN UMUR PEMBACA MENGGUNAKAN METODE MULTINOMIAL NAÏVE BAYES CLASSIFIER
Pernyataan Tanggungjawab
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