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TERM WEIGHTING BASED ON POSITIVE IMPACT FACTOR QUERY FOR ARABIC FIQH DOCUMENT RANKING

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 10 No. 1 (2017) · pp. 29–35

DOI
10.21609/jiki.v10i1.408
ISSN
2502-9274, 2088-7051
Published
2017-02-28
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Query becomes one of the most decisive factor on documents searching. A query contains several words, where one of them will become a key term. Key term is a word that has higher information and value than the others in query. It can be used in any kind of text documents, including Arabic Fiqh documents. Using key term in term weighting process could led to an improvement on result’s relevancy. In Arabic Fiqh document searching, not using the proper method in term weighting will relieve important value of key term. In this paper, we propose a new term weighting method based on Positive Impact Factor Query (PIFQ) for Arabic Fiqh documents ranking. PIFQ calculated using key term’s frequency on each category (mazhab) on Fiqh. The key term that frequently appear on a certain mazhab will get higher score on that mazhab, and vice versa. After PIFQ values are acquired, TF.IDF calculation will be done to each words. Then, PIFQ weight will be combine with the result from TF.IDF so that the new weight values for each words will be produced. Experimental result performed on a number of queries using 143 Arabic Fiqh documents show that the proposed method is better than traditional TF.IDF, with 77.9%, 83.1%, and 80.1% of precision, recall, and F-measure respectively.

Keywords

Collaboration network of the authors

40 authors · 90 co-authorship links · 11 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 Nopember9
Dini Adni NavastaraInstitut Teknologi Sepuluh Nopember4
Agus Budi RaharjoInstitut Teknologi Sepuluh Nopember4
Rarasmaya IndraswariInstitut Teknologi Sepuluh Nopember3
Khoirul UmamInstitut Teknologi Sepuluh Nopember3
Gulpi Qorik Oktagalu PratamasunuAffiliation unknown3
Munjiah Nur SaadahInstitut Teknologi Sepuluh Nopember2
Rigga Widar AtmagiInstitut Teknologi Sepuluh Nopember2
Dyah S. RahayuInstitut Teknologi Sepuluh Nopember2
Anny YuniartiInstitut 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
Khadijah Fahmi Hayati HolleInstitut Teknologi Sepuluh Nopember2
Christian Sri Kusuma AdityaInstitut Teknologi Sepuluh Nopember2
Wawan GunawanUniversitas Islam Negeri Raden Intan Lampung1
⁠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

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">TERM WEIGHTING BASED ON POSITIVE IMPACT FACTOR QUERY FOR ARABIC FIQH DOCUMENT RANKING</dc:title>
	<dc:creator>Sholikah, Rizka</dc:creator>
	<dc:creator>Kartika, Dhian</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">Arabic</dc:subject>
	<dc:subject xml:lang="en-US">Term Weighting</dc:subject>
	<dc:subject xml:lang="en-US">Query</dc:subject>
	<dc:subject xml:lang="en-US">PIFQ</dc:subject>
	<dc:description xml:lang="en-US">Query becomes one of the most decisive factor on documents searching. A query contains several words, where one of them will become a key term. Key term is a word that has higher information and value than the others in query. It can be used in any kind of text documents, including Arabic Fiqh documents. Using key term in term weighting process could led to an improvement on result’s relevancy. In Arabic Fiqh document searching, not using the proper method in term weighting will relieve important value of key term. In this paper, we propose a new term weighting method based on Positive Impact Factor Query (PIFQ) for Arabic Fiqh documents ranking. PIFQ calculated using key term’s frequency on each category (mazhab) on Fiqh. The key term that frequently appear on a certain mazhab will get higher score on that mazhab, and vice versa. After PIFQ values are acquired, TF.IDF calculation will be done to each words. Then, PIFQ weight will be combine with the result from TF.IDF so that the new weight values for each words will be produced. Experimental result performed on a number of queries using 143 Arabic Fiqh documents show that the proposed method is better than traditional TF.IDF, with 77.9%, 83.1%, and 80.1% of precision, recall, and F-measure respectively.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2017-02-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/408</dc:identifier>
	<dc:identifier>10.21609/jiki.v10i1.408</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 10 No. 1 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 29-35</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v10i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/408/365</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2017 Jurnal Ilmu Komputer dan Informasi</dc:rights>
</oai_dc:dc>