PARSING STRUKTUR PARAGRAF BERBASIS NEURAL NETWORK

Agung Prasetya, Taufiq Agung Cahyono

Abstract


Parsing paragraf memiliki peran penting dalam perkembangan kecerdasan buatan. Parsing menjadi langkah awal untuk menalar paragraf agar bisa dimengerti oleh mesin. Keefektifan metode parsing paragraf bergantung pada bagaimana mendekomposisikan teks ke segmen teks. Proses segmentasi tanpa memperhitungkan struktur semantik dari suatu paragraf akan menghasilkan struktur yang tidak sinkron dengan makna sebenarnya. Untuk mengatasi masalah ini, penelitian ini mengusulkan penerapan metode berbasis recursive neural network (RvNN). Metode ini berupaya mendapatkan binary tree terbaik yang merepresentasikan struktur paragraf. Metode usulan diterapkan untuk menyelesaikan paragraf-paragraf sederhana yaitu soal cerita anak. Hasil uji coba menunjukkan bahwa metode usulan dapat memparsing paragraf dengan tingkat akurasi sebesar 0.9. Metode usulan juga lebih efisien karena tidak perlu membuat repositori kerangka struktur

Keywords


paragraf, parsing; recursive neural network; semantik; binary tree;

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DOI: https://doi.org/10.29100/jipi.v7i2.2186

DOI (PDF): https://doi.org/10.29100/jipi.v7i2.2186.g1220

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