Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

Court Decision Prediction Model Using Natural Language Processing and Random Forest

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 3 (2025) · pp. 305–316

DOI
10.22146/ijccs.108377
ISSN
2460-7258, 1978-1520
Published
2025-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The increasing number of criminal cases in Indonesia, which reached 288,472 in 2023, or rose by 15% from the previous year, has created a substantial workload for judicial professionals. This situation highlights the urgent need for artificial intelligence–based decision support systems to accelerate and improve the quality of legal decision-making. This study proposes a court decision prediction approach using the Random Forest algorithm combined with Natural Language Processing (NLP) techniques. The dataset consists of 21,630 court decisions from the Supreme Court of Indonesia, originally in PDF format and converted into XML. The research procedure includes text preprocessing, feature construction using Word2Vec and Fast Text, and Random Forest classification. Unlike previous studies employing LSTM, BiLSTM, and CNN methods with accuracy ranging from 49.14% to 77.32%, the proposed approach delivers better performance. Experimental results show that the model achieves a prediction accuracy of up to 63%-81% for Penalty Categories classification and up to 65%-80% for long punishment regression. These findings demonstrate the significant potential of applying NLP and Random Forest to develop predictive systems in Indonesian legal document analysis.

Keywords

Collaboration network of the authors

16 authors · 24 co-authorship links · 8 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • University of Brawijaya
  • National Research and Innovation Agency
  • Sumatera Institute of Technology
  • Universitas Negeri Surabaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Arief IchwaniSumatera Institute of Technology1
Windyaning UstyannieUniversitas Gadjah Mada1
Devi Valentino WaasUniversitas Gadjah Mada1
Muhammad Faqih DzulqarnainUniversitas Gadjah Mada1
Faizal MakhrusUniversitas Gadjah Mada1
Wahyono WahyonoUniversitas Gadjah Mada1
Yaqutina Marjani SantosaUniversitas Gadjah Mada1
Agung PambudiUniversitas Gadjah Mada1
Yusi Tyroni MursityoUniversity of Brawijaya1
Muhammad GhiffariUniversity of Brawijaya1
Yurizka Melia SariUniversitas Negeri Surabaya1
Janu ArlinwibowoNational Research and Innovation Agency1
Gupita Nadindra FatimaAffiliation unknown1
Dwi PurwokoNational Research and Innovation Agency1

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.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Court Decision Prediction Model Using Natural Language Processing and Random Forest</dc:title>
	<dc:creator>Nasution, Nasution</dc:creator>
	<dc:creator>Suprapto, Suprapto</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Court decision prediction; Natural Language Processing; Random Forest; Machine Learning</dc:subject>
	<dc:description xml:lang="en-US">The increasing number of criminal cases in Indonesia, which reached 288,472 in 2023, or rose by 15% from the previous year, has created a substantial workload for judicial professionals. This situation highlights the urgent need for artificial intelligence–based decision support systems to accelerate and improve the quality of legal decision-making. This study proposes a court decision prediction approach using the Random Forest algorithm combined with Natural Language Processing (NLP) techniques. The dataset consists of 21,630 court decisions from the Supreme Court of Indonesia, originally in PDF format and converted into XML. The research procedure includes text preprocessing, feature construction using Word2Vec and Fast Text, and Random Forest classification. Unlike previous studies employing LSTM, BiLSTM, and CNN methods with accuracy ranging from 49.14% to 77.32%, the proposed approach delivers better performance. Experimental results show that the model achieves a prediction accuracy of up to 63%-81% for Penalty Categories classification and up to 65%-80% for long punishment regression. These findings demonstrate the significant potential of applying NLP and Random Forest to develop predictive systems in Indonesian legal document analysis.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">-</dc:contributor>
	<dc:date>2025-07-31</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://journal.ugm.ac.id/ijccs/article/view/108377</dc:identifier>
	<dc:identifier>10.22146/ijccs.108377</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 3 (2025): July; 305-316</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>info:eu-repo/grantAgreement/EC/FP7/999999</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/108377/42107</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/downloadSuppFile/108377/39179</dc:relation>
	<dc:rights xml:lang="en-US">info:eu-repo/semantics/openAccess</dc:rights>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>