Penerapan Named Entity Recognition Untuk Mengenali Fitur Produk Pada E-Commerce Menggunakan Rule Template Dan Hidden Markov Model

Dirgantara, M. Yusron Syauqi (2018) Penerapan Named Entity Recognition Untuk Mengenali Fitur Produk Pada E-Commerce Menggunakan Rule Template Dan Hidden Markov Model. Sarjana thesis, Universitas Brawijaya.

Abstract

Teknologi informasi dengan adanya internet memberikan dampak perkembangan perdagangan elektronik atau e-commerce yang memperoleh banyak popularitas. Data APJII tahun 2016 menyatakan sebanyak 130,8 juta penduduk Indonesia menggunakan internet untuk menawarkan barang dan jasa. Di dalam manajemen e-commerce terdapat customer service yang bertugas untuk menangani segala macam pertanyaan yang disampaikan oleh pelanggan. Penyampaian informasi oleh customer service biasanya melalui call center atau aplikasi chatting. Didorong kemampuan intelligent digital assistants chatbot banyak digunakan untuk membantu pekerjaan customer services. Dibutuhkan suatu analisis terhadap bahasa pelanggan pada chatbot agar dapat dikenali informasi apa saja yang terdapat pada pertanyaan tersebut, sehingga dibutuhkan klasifikasi dan ektrasi informasi agar mendapatkan informasi penting yang dibutuhkan oleh chatbot dalam menjawab pertanyaan dari pelanggan. Named Entity Recognition (NER) merupakan bagian dari ekstraksi informasi yang bertugas untuk pengklasifikasi teks dari sebuah dokumen atau korpus yang dikategorikan kedalam beberapa kelas seperti nama orang, lokasi, bulan, tanggal, waktu dan sebagainya. Ekstrasi nama secara otomatis dapat berguna untuk mengatasi beberapa permasalahan seperti pada mesin terjemahan, pencarian informasi, tanya jawab dan peringkasan teks. Pada penelitian ini NER yang dilakukan menggunakan metode Hidden Markov Model dan Rule Template dengan 6 entitas yaitu MEREK, TIPE, HARGA, SPEK, N_SPEK dan N_TAG. Secara keseluruhan pengenalan entitas yang dilakukan pada penelitian ini menghasilkan nilai akurasi pada Rule Template sebesar 97.20% dan nilai akurasi pada Hidden Markov Model sebesar 92.23%.

English Abstract

Information technology with the Internet gives the impact of the development of electronic commerce or e-commerce that gained a lot of popularity. APJII data in 2016 states as many as 130.8 million Indonesians use the internet to offer goods and services. In e-commerce management there is customer service that is tasked to handle all of questions submitted by customers. Submission of information by customer service is usually through a call center or chat application. In thrust the ability of intelligent digital assistants chatbot is widely used to help the work of customer services. It takes an analysis of the customer's language on chatbot in order to be able to recognize what information is contained in the question, so it takes the classification and extracting of information in order to get important information needed by chatbot in answering questions from customers. Named Entity Recognition (NER) is part of the extraction of information assigned to the classification of text from a document or corpus categorized into classes such as person's name, location, month, date, time and so on. Automatic name extraction can be useful for addressing some issues such as translation engines, information retrieval, frequently asked questions and text summary. In this study NER is done using the method of Hidden Markov Model and Rule Template with 6 entities i.e. BRAND, TYPE, PRICE, SPEK, N_SPEK and N_TAG. Overall introduction of entities conducted in this study resulted in the accuracy value in the Rule Template of 97.20% and the accuracy value in the Hidden Markov Model of 92.23%.

Item Type:	Thesis (Sarjana)
Identification Number:	SKR/FTIK/2018/154/051801141
Uncontrolled Keywords:	E-Commerce, Named Entity Recognition, Hidden Markov Model, Rule Template, Information Extraction
Subjects:	000 Computer science, information and general works > 006 Special computer methods > 006.4 Computer pattern recognition
Divisions:	Fakultas Ilmu Komputer > Teknik Informatika
Depositing User:	Yusuf Dwi N.
Date Deposited:	31 May 2018 03:07
Last Modified:	26 Oct 2021 08:53
URI:	http://repository.ub.ac.id/id/eprint/11278

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