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Indonesian part of speech tagging using maximum entropy markov model on Indonesian manually tagged corpus

IAES International Journal of Artificial Intelligence · Vol. 11 No. 1 (2022) · hlm. 336–344

DOI
10.11591/ijai.v11.i1.pp336-344
ISSN
2252-8938, 2089-4872
Terbit
2022-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This research discusses the development of a part of speech (POS) tagging system to solve the problem of word ambiguity. This paper presents a new method, namely maximum entropy markov model (MEMM) to solve word ambiguity on the Indonesian dataset. A manually labeled “Indonesian manually tagged corpus” was used as data. Furthermore, the corpus is processed using the entropy formula to obtain the weight of the value of the word being searched for, then calculating it into the MEMM Bigram and MEMM Trigram algorithms with the previously obtained rules to determine the part of speech (POS) tag that has the highest probability. The results obtained show POS tagging using the MEMM method has advantages over the methods used previously which used the same data. This paper improves a performance evaluation of research previously. The resulting average accuracy is 83.04% for the MEMM Bigram algorithm and 86.66% for the MEMM Trigram. The MEMM Trigram algorithm is better than the MEMM Bigram algorithm.

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  • State University of Malang
  • Universiti Teknologi MARA
  • Sebelas Maret University
  • Universitas Pradita
  • University of South Australia
  • Universiti Malaysia Perlis
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Rekan penulisOrganisasiArtikel bersama
Aji Prasetya WibawaState University of Malang2
Langlang GumilarState University of Malang2
Samsul SetuminUniversiti Teknologi MARA2
Ito WasitoUniversitas Pradita1
Andrew NafalskiUniversity of South Australia1
Didik Dwi PrasetyaState University of Malang1
Arif Nur AfandiState University of Malang1
Ahmad Kadri JunohUniversiti Malaysia Perlis1
Muhammad Fa'iz AlfarisiState University of Malang1
Siti Juliana Abu BakarUniversiti Teknologi MARA1
Muhammad Abbas Abu TalibUniversiti Teknologi MARA1
Adi Izhar Che AniUniversiti Teknologi MARA1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Indonesian part of speech tagging using maximum entropy markov model on Indonesian manually tagged corpus</dc:title>
	<dc:creator>Cahyani, Denis Eka</dc:creator>
	<dc:creator>Mustikaningtyas, Winda</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Bigram; Maximum entropy markov model; N-gram; Part of speech tagging; Trigram</dc:subject>
	<dc:description xml:lang="en-US">This research discusses the development of a part of speech (POS) tagging system to solve the problem of word ambiguity. This paper presents a new method, namely maximum entropy markov model (MEMM) to solve word ambiguity on the Indonesian dataset. A manually labeled “Indonesian manually tagged corpus” was used as data. Furthermore, the corpus is processed using the entropy formula to obtain the weight of the value of the word being searched for, then calculating it into the MEMM Bigram and MEMM Trigram algorithms with the previously obtained rules to determine the part of speech (POS) tag that has the highest probability. The results obtained show POS tagging using the MEMM method has advantages over the methods used previously which used the same data. This paper improves a performance evaluation of research previously. The resulting average accuracy is 83.04% for the MEMM Bigram algorithm and 86.66% for the MEMM Trigram. The MEMM Trigram algorithm is better than the MEMM Bigram algorithm.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21200</dc:identifier>
	<dc:identifier>10.11591/ijai.v11.i1.pp336-344</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 11, No 1: March 2022; 336-344</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v11.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21200/13315</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>