Pengenalan Entitas Bernama untuk Identifikasi Transaksi Akuntansi Menggunakan Hidden Markov Model - Rika Raudhotul Rizqiyah; Lailil Muflikhah; Mochammad Ali Fauzi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Accounting is a task which has an important role in supporting economic continuity, due to the recording of any business process that occurred was done in accounting. However, the recording of financial transactions in accounting for identification into journal is still done manually, so that required classification and extraction of information contained in the accounting transaction text to make it easier. Named Entity Recognition (NER) is the first step needed to perform information extraction. To solve this problem, named entity recognition done for identification of accounting transaction. In this research used method of Hidden Markov Model (HMM), because HMM can resolve labeling task and and known robustly in performing named entity recognition. The main process in this named entity recognition is divided into modeling process using Hidden Markov Model and decoding process using Viterbi Algorithm. In this research will be recognize 12 entities namely DATE, TITLE, PER, TRANS, EXP_MON, TYP_COMP, FIRST_ORG, SECOND_ORG, EXP_DATE, NO_DATE, MONTH and YEAR. Overall entity recognition with addition Laplace Smoothing and Regular Expression techniques produce a value of average precision, recall and f-measure consecutive 81.75%, 87.88%, and 82.39%.