Pengembangan Sistem Pemantauan Sentimen Berita Berbahasa Indonesia Berdasarkan Konten dengan Long-Short-Term Memory

Penulis

Dewi Yanti Liliana, Nadia Nurul Hikmah, Maykada Harjono

Abstrak

Kementerian Komunikasi dan Informatika (Kemkominfo) memiliki tugas salah satunya untuk mengawasi konten berita yang beredar di media digital. Dengan terus bertambahnya berita online di internet, Kemkominfo dihadapkan pada permasalahan pengklasifikasian sentimen berita yang masih dilakukan secara manual dengan membaca konten berita satu persatu lalu menangkap sentimen dari berita, yaitu sentimen positif, negatif, atau netral. Hal ini sangat melelahkan dan memakan waktu mengingat volume dan kecepatan pertumbuhan berita setiap harinya semakin masif. Untuk itu diperlukan pengembangan sistem pengklasifikasi sentimen berita daring secara otomatis untuk pemantauan berita berbahasa Indonesia. Sistem pengklasifikasi secara otomatis berbasis machine learning dilakukan dengan membangun model pembelajaran dari korpus berita yang berasal dari situs berita daring. Korpus data tersebut kemudian diproses menggunakan algoritma Long Short-Term Memory (LSTM). LSTM biasa digunakan untuk menangani kasus klasifikasi dalam berbagai bidang khususnya dengan input berupa teks sekuensial. Model LSTM diimplementasikan ke dalam aplikasi berbasis web untuk menentukan jenis dari sentimen berita. Berdasarkan hasil pengujian yang dilakukan, model LSTM yang dibuat memiliki tingkat akurasi sebesar 86%. Dengan demikian implementasi LSTM mampu menjadi suatu solusi untuk mengatasi masalah pengklasifikasian sentimen berita daring secara otomatis untuk pemantauan sentimen berita di Kemkominfo.

 

Asbtract

The Ministry of Communication and Informatics (Kemkominfo) has one duty to monitor news content circulating in digital media. With the increasing number of online news in the internet, Kemkominfo is facing the problem of classifying news sentiment which is still done manually by reading the contents of the news one by one, and then capturing the sentiment of the news; either positive, negative, or neutral. This is very exhausting and time consuming considering the volume and speed of growth of news every day is getting massive. This requires the development of an automatic online news sentiment classification system for monitoring Indonesian news. Machine learning-based automatic classification systems are carried out by building a learning model from a news corpus originating from news sites. The data is then processed using the Long Short Term Memory (LSTM) algorithm. LSTM is commonly used to handle classification in various fields especially in a sequential input. The LSTM model is implemented into a web-based application to determine the types of news sentiment. Based on the results of the tests carried out, the LSTM model created has an accuracy rate of 86%. Thus, the implementation of LSTM is potentially become a solution to overcome the problem of automatic online news sentiment classification for the news content monitoring system at the Ministry of Communication and Information.


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Referensi


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DOI: http://dx.doi.org/10.25126/jtiik.2021854624