Ekstraksi Relasi Antar Entitas di Bahasa Indonesia Menggunakan Neural Network

Ananta Tio Putra; Eunike Kardinata; Hartarto Junaedi; Francisca Chandra; Joan Santoso

doi:10.37823/insight.v3i02.156

Authors

Ananta Tio Putra Institut Sains dan Teknologi Terpadu Surabaya
Eunike Kardinata Institut Sains dan Teknologi Terpadu Surabaya
Hartarto Junaedi Institut Sains dan Teknologi Terpadu Surabaya
Francisca Chandra Institut Sains dan Teknologi Terpadu Surabaya
Joan Santoso Institut Sains dan Teknologi Terpadu Surabaya

DOI:

https://doi.org/10.37823/insight.v3i02.156

Keywords:

Convulution Recurrent Neural Network, Information Extraction, Relation Extraction, Bahasa Indonesia.

Abstract

Dengan perkembangan zaman yang begitu pesat, berdampak pada perkembangan data pula. Salah satu bentuk data yang paling banyak saat ini berupa data tekstual seperti artikel sederhana maupun dokumen lain yang terdapat di internet. Agar data tekstual tersebut dapat dimengerti dan dimanfaatkan dengan baik oleh manusia, maka perlu di proses dan disederhanakan agar menjadi informasi yang ringkas dan jelas. Oleh karena itu, semakin berkembang pula penelitian dalam bidang Information Extraction (IE) dan salah satu contoh penelitian di IE adalah Relation Extraction (RE). Penelitian RE sudah banyak dilakukan terutama pada Bahasa Inggris dimana resourcenya sudah termasuk banyak. Metode yang digunakan pun bermacam-macam seperti kernel, tree kernel, support vector machine, long short-term memory, convulution recurrent neural network, dan lain sebagainya. Pada penelitian kali ini adalah penelitian RE pada Bahasa Indonesia dengan menggunakan metode convulution recurrent neural network yang sudah dipergunakan untuk RE Bahasa Inggris. Dataset yang digunakan pada penelitian ini adalah dataset Bahasa Indonesia yang berasal dari file xml wikipedia. File xml wikipedia ini kemudian diproses sehingga menghasilkan dataset seperti yang digunakan pada CRNN dalam Bahasa inggris yaitu dalam format SemEval-2 Task 8. Uji coba dilakukan dengan berbagai macam perbandingan data training dan testing yaitu 80:20, 70:30, dan 60:40. Selain itu, parameter pooling untuk CRNN yang digunakan ada dua macam yaitu ‘att’ dan ‘max’. Dari uji coba yang dilakukan, hasil yang didapatkan adalah bervariasi mulai dari mendekati maupun lebih baik bila dibandingkan dengan CRNN dengan menggunakan dataset Bahasa inggris sehingga dapat disimpulkan bahwa dengan CRNN ini bisa digunakan untuk proses RE pada Bahasa Indonesia apabila dataset yang digunakan sesuai dengan penelitian sebelumnya.

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