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PREFERENCE BASED TERM WEIGHTING FOR ARABIC FIQH DOCUMENT RANKING

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 8 No. 1 (2015) · pp. 45–52

DOI
10.21609/jiki.v8i1.283
ISSN
2502-9274, 2088-7051
Published
2015-03-26
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

In document retrieval, besides the suitability of query with search results, there is also a subjective user assessment that is expected to be a deciding factor in document ranking. This preference aspect is referred at the fiqh document searching. People tend to prefer on certain fiqh methodology without rejecting other fiqh methodologies. It is necessary to investigate preference factor in addition to the relevance factor in the document ranking. Therefore, this research proposed a method of term weighting based on preference to rank documents according to user preference. The proposed method is also combined with term weighting based on documents index and books index so it sees relevance and preference aspect. The proposed method is Inverse Preference Frequency with α value (IPFα). In this method, we calculate preference value by IPF term weighting. Then, the preference values of terms that is equal with the query are multiplied by α. IPFα combined with the existing weighting methods become TF.IDF.IBF.IPFα. Experiment of the proposed method uses dataset of several Arabic fiqh documents. Evaluation uses recall, precision, and f-measure calculations. Proposed term weighting method is obtained to rank the document in the right order according to user preference. It is shown from the result with recall value reach 75%, precision 100%, and f-measure 85.7% respectively.

Keywords

Collaboration network of the authors

40 authors · 95 co-authorship links · 13 across different organisations · showing the 40 most connected

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  • Institut Teknologi Sepuluh Nopember
  • Lampung University
  • University of Brawijaya
  • Universitas Islam Negeri Raden Intan Lampung
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Chastine FatichahInstitut Teknologi Sepuluh Nopember8
Dini Adni NavastaraInstitut Teknologi Sepuluh Nopember5
Rarasmaya IndraswariInstitut Teknologi Sepuluh Nopember4
Agus Budi RaharjoInstitut Teknologi Sepuluh Nopember4
Anny YuniartiInstitut Teknologi Sepuluh Nopember3
Khoirul UmamInstitut Teknologi Sepuluh Nopember3
Gulpi Qorik Oktagalu PratamasunuAffiliation unknown3
Rizka Wakhidatus SholikahInstitut Teknologi Sepuluh Nopember3
Wawan GunawanUniversitas Islam Negeri Raden Intan Lampung2
Munjiah Nur SaadahInstitut Teknologi Sepuluh Nopember2
Rigga Widar AtmagiInstitut Teknologi Sepuluh Nopember2
Dyah S. RahayuInstitut Teknologi Sepuluh Nopember2
Darlis HerumurtiInstitut Teknologi Sepuluh Nopember2
Safri AdamInstitut Teknologi Sepuluh Nopember2
Yudhi PurwanantoInstitut Teknologi Sepuluh Nopember2
Fahrur RoziInstitut Teknologi Sepuluh Nopember2
Hilya TsaniyaInstitut Teknologi Sepuluh Nopember2
Nanik SuciatiInstitut Teknologi Sepuluh Nopember2
Apriantoni ApriantoniInstitut Teknologi Sepuluh Nopember2
Fidi Wincoko PutroInstitut Teknologi Sepuluh Nopember2
Christian Sri Kusuma AdityaInstitut Teknologi Sepuluh Nopember2
⁠Undang RosidinLampung University1
Nina KadaritnaLampung University1
Naser JawasInstitut Teknologi Sepuluh Nopember1
Randy Cahya WihandikaUniversity of Brawijaya1
Go Frendi GunawanInstitut Teknologi Sepuluh Nopember1
Sonny Christiano GosariaInstitut Teknologi Sepuluh Nopember1
Yuita Arum SariUniversity of Brawijaya1
Evy Kamilah RatnasariInstitut Teknologi Sepuluh Nopember1
Siti MutrofinInstitut Teknologi Sepuluh Nopember1
Mutmainnah MuchtarInstitut Teknologi Sepuluh Nopember1
Jayanti Yusmah SariInstitut Teknologi Sepuluh Nopember1
Mohammad ZarkasiInstitut Teknologi Sepuluh Nopember1
Andi Baso KaswarInstitut Teknologi Sepuluh Nopember1
Saprina MamaseInstitut Teknologi Sepuluh Nopember1
Saiful Bahri MusaInstitut Teknologi Sepuluh Nopember1
Ahmad Mustofa HadiInstitut Teknologi Sepuluh Nopember1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">PREFERENCE BASED TERM WEIGHTING FOR ARABIC FIQH DOCUMENT RANKING</dc:title>
	<dc:creator>Holle, Khadijah Fahmi Hayati</dc:creator>
	<dc:creator>Arifin, Agus Zainal</dc:creator>
	<dc:creator>Purwitasari, Diana</dc:creator>
	<dc:subject xml:lang="en-US">document ranking</dc:subject>
	<dc:subject xml:lang="en-US">document retrieval</dc:subject>
	<dc:subject xml:lang="en-US">preference</dc:subject>
	<dc:subject xml:lang="en-US">term weighting</dc:subject>
	<dc:subject xml:lang="en-US">IPFα</dc:subject>
	<dc:description xml:lang="en-US">In document retrieval, besides the suitability of query with search results, there is also a subjective user assessment that is expected to be a deciding factor in document ranking. This preference aspect is referred at the fiqh document searching. People tend to prefer on certain fiqh methodology without rejecting other fiqh methodologies. It is necessary to investigate preference factor in addition to the relevance factor in the document ranking. Therefore, this research proposed a method of term weighting based on preference to rank documents according to user preference. The proposed method is also combined with term weighting based on documents index and books index so it sees relevance and preference aspect. The proposed method is Inverse Preference Frequency with α value (IPFα). In this method, we calculate preference value by IPF term weighting. Then, the preference values of terms that is equal with the query are multiplied by α. IPFα combined with the existing weighting methods become TF.IDF.IBF.IPFα. Experiment of the proposed method uses dataset of several Arabic fiqh documents. Evaluation uses recall, precision, and f-measure calculations. Proposed term weighting method is obtained to rank the document in the right order according to user preference. It is shown from the result with recall value reach 75%, precision 100%, and f-measure 85.7% respectively.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2015-03-26</dc:date>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/283</dc:identifier>
	<dc:identifier>10.21609/jiki.v8i1.283</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 8 No. 1 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 45-52</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v8i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/283/328</dc:relation>
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