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Target prediction of compounds on jamu formula using nearest profile method

Jurnal Matematika, Statistika dan Komputasi · Vol. 17 No. 2 (2020) · pp. 293–303

DOI
10.20956/jmsk.v17i2.11616
ISSN
2614-8811, 1858-1382
Published
2020-12-23
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Jamu is one of Indonesia's cultural heritage, which consists of several plants that have been practiced for centuries in Indonesian society to maintain health and treat diseases. One of the scientification efforts of Jamu to reveal its mechanism is to predict the target-protein of the active ingredients of the Jamu. In this study, the prediction of the target compound for Jamu was carried out using a supervised learning approach involving conventional medicinal compounds as training data. The method used in this study is the closest profile method adopted from the nearest neighbor algorithm. This method is implemented in drug compound data to construct a learning model. The AUC value for measuring performance of the three implemented models is 0.62 for the fixed compound model, 0.78 for the fixed target model, and 0.83 for the mixed model. The fixed compound model is then used to construct a prediction model on the herbal medicine data with an optimal threshold value of 0.91. The model produced 10 potential compounds in the herbal formula and its 44 unique protein targets. Even though it has many limitations in obtaining a good performance, the closest profile method can be used to predict the target of the herbal compound whose target is not yet known.

Keywords

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34 authors · 104 co-authorship links

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Co-authorOrganisationJoint articles
Edy Saputra RusdiAffiliation unknown1
A. Muh. Amil SiddikAffiliation unknown1
Naimah ArisAffiliation unknown1
Muhammad Ardiansyah AsrifahAffiliation unknown1
Aidawayati RangkutiAffiliation unknown1
Wahyudi RusdiAffiliation unknown1
EDY SAPUTRA RUSDIAffiliation unknown1
Sartini SartiniAffiliation unknown1
Fauziah AminAffiliation unknown1
Nur Inda YantiAffiliation unknown1
Subehan LalloAffiliation unknown1
Sukamto S MamadaAffiliation unknown1
Mu'minisa BahrunAffiliation unknown1
Indriani IndrianiAffiliation unknown1
Marianti A. ManggauAffiliation unknown1
Elly WahyudinAffiliation unknown1
Muh. Nur AmirAffiliation unknown1
Yuyun SulitianiAffiliation unknown1
Inda PratiwiAffiliation unknown1
Ismail IsmailAffiliation unknown1
Ambeng - AmbengAffiliation unknown1
Jennyta Dhewi Darmansyah TanjungAffiliation unknown1
Nurul Magfirah SukriAffiliation unknown1
Ilham IlhamAffiliation unknown1
Muhammad Al AnshariAffiliation unknown1
Zarlina ZainuddinAffiliation unknown1
Siswanto SiswantoAffiliation unknown1
Putri Indi RahayuAffiliation unknown1
Musafira MusafiraAffiliation unknown1
Bagas PrasetyoAffiliation unknown1

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://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Target prediction of compounds on jamu formula using nearest profile method</dc:title>
	<dc:creator xml:lang="en"> Syahrir, Nur Hilal A</dc:creator>
	<dc:creator xml:lang="en">Sumarheni, Sumarheni</dc:creator>
	<dc:creator xml:lang="en">Amir, Supri Bin Hj </dc:creator>
	<dc:creator xml:lang="en">Kuswanto, Hedi </dc:creator>
	<dc:subject xml:lang="en">supervised-learning</dc:subject>
	<dc:subject xml:lang="en">nearest profile</dc:subject>
	<dc:subject xml:lang="en">jamu</dc:subject>
	<dc:subject xml:lang="en">compund-target protein</dc:subject>
	<dc:description xml:lang="en">Jamu is one of Indonesia's cultural heritage, which consists of several plants that have been practiced for centuries in Indonesian society to maintain health and treat diseases. One of the scientification efforts of Jamu to reveal its mechanism is to predict the target-protein of the active ingredients of the Jamu. In this study, the prediction of the target compound for Jamu was carried out using a supervised learning approach involving conventional medicinal compounds as training data. The method used in this study is the closest profile method adopted from the nearest neighbor algorithm. This method is implemented in drug compound data to construct a learning model. The AUC value for measuring performance of the three implemented models is 0.62 for the fixed compound model, 0.78 for the fixed target model, and 0.83 for the mixed model. The fixed compound model is then used to construct a prediction model on the herbal medicine data with an optimal threshold value of 0.91. The model produced 10 potential compounds in the herbal formula and its 44 unique protein targets. Even though it has many limitations in obtaining a good performance, the closest profile method can be used to predict the target of the herbal compound whose target is not yet known.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2020-12-23</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://journal.unhas.ac.id/index.php/jmsk/article/view/11616</dc:identifier>
	<dc:identifier>10.20956/jmsk.v17i2.11616</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 17 No. 2 (2021): JANUARY 2021; 293-303</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/11616/6179</dc:relation>
	<dc:relation>10.20956/j.v17i2.11616.g6179</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2020 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>