Improving Indonesian Named Entity Recognition for Domain Zakat Using Conditional Random Fields


  • Nur Febriana Widiyanti Department of Informatics, UIN Syarif Hidayatullah Jakarta, Indonesia, Indonesia
  • Husni Teja Sukmana Department of Informatics, UIN Syarif Hidayatullah Jakarta, Indonesia, Indonesia
  • Khodijah Hulliyah Department of Informatics, UIN Syarif Hidayatullah Jakarta, Indonesia, Indonesia
  • Dewi Khairani Department of Informatics, UIN Syarif Hidayatullah Jakarta, Indonesia, Indonesia
  • Lee Kyung Oh Department of Computer Engineering, Sun Moon University, Korea, Republic of



Named Entity Recognition, Conditional Random Fields, Natural Language Processing, Charity, Zakat


In Indonesia, where the majority of the population is Muslim, one of the obligations of a Muslim is zakat. To reduce illiteracy about zakat among Muslims, they need to have access to basic information about it. In order to facilitate the acquisition of this information, this study utilized named entity recognition (NER) and defined 12 named entity classes for the zakat domain, including the pillars of Islam, various types of zakat, and zakat management institutions. The Conditional Random Fields method was used for testing Indonesian-NER in three scenarios. In the specific context of the Zakat domain, NER can extract information about organizations, individuals, and locations involved in collecting and distributing Zakat funds. This information can improve the Zakat system’s efficiency and transparency and support research and analysis on Zakat-related topics. The average performance evaluation of the Indonesian-NER model showed a precision of 0.902, recall of 0.834, and an F1-score of 0.867.

Author Biographies

Nur Febriana Widiyanti, Department of Informatics, UIN Syarif Hidayatullah Jakarta, Indonesia

Scopus ID :23398706200Fakultas Sains dan TeknologiUIN Sunan Gunung Djati Bandung

Husni Teja Sukmana, Department of Informatics, UIN Syarif Hidayatullah Jakarta, Indonesia

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