Named Entity Recognition Menggunakan Hidden Markov Model dan Algoritma Viterbi pada Teks Tanaman Obat - Agung Setiyoaji; Lailil Muflikhah; Mochammad Ali Fauzi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2017)
Media to convey information can be through television, radio, social media, and website. Website is a work of someone located in a domain that contains information. The development of websites more and more information is not unstoppable so that the problem arises difficult to find information in accordance with the needs of Internet users, so that the required classification and extraction of information for information on the website. Named Entity Recognition which derives from the extraction of information, NER aims to facilitate the search for information by naming entities on each word in a text. In this research will be done the introduction of four entities namely the NAME, PLACE, SUBSTANCE, and FUNCTION of the text on medicinal plants. The algorithm used Hidden Markov Model (HMM) and Viterbi algorithm. Overall entity recognition count the lowest value with f-measure 0.41, and the highest with f-measure 0.72.