Predicting levels of legal case difficulties using machine learning

Sari, Ilmiyati; Kosasih, Rifki; Indarti, Dina

doi:10.11591/ijai.v13.i4.pp4364-4371

Open access Journal SINTA 1

Predicting levels of legal case difficulties using machine learning

IAES International Journal of Artificial Intelligence · Vol. 13 No. 4 (2024) · pp. 4364–4371

DOI: 10.11591/ijai.v13.i4.pp4364-4371
ISSN: 2252-8938, 2089-4872
Published: 2024-12-01
Publisher: Institute of Advanced Engineering and Science
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

Lawyers play a crucial role in the courtroom, assisting clients in their defense. Because of their lack of legal expertise, a person or organization facing legal issues requires professional aid. However, we need to know how much money will be spent on paying lawyers. The level of complexity in a case can be used to determine lawyer costs. Therefore, in this research, we propose employing machine learning methodologies, i.e., random forest classifiers and support vector machines (SVM), to determine the level of legal case difficulties. The novelty of this research is applying a machine learning approach in predicting the level of difficulty of legal cases. The data utilized consists of 990 records, which are divided into training and testing data in a 90:10 ratio. The term frequency-inverse document frequency (TF-IDF) approach was then utilized to perform text preprocessing. The text-preprocessing findings are utilized as input in the classification process. According to the research findings, an accuracy value of 85%, a value of weighted average precision is 88%, and a value of weighted average recall is 85%, for support vector machine. Using random forest, we achieve an accuracy value of 89%, a value of weighted average precision is 85.6%, and a value of weighted average recall is 80%.

Keywords

Collaboration network of the authors

17 authors · 36 co-authorship links · 5 across different organisations

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

Gunadarma University
University of Indonesia
Universitas Islam Negeri Imam Bonjol Padang
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

Show as table

Co-author	Organisation	Joint articles
Diana Ikasari	Affiliation unknown	2
Sarifuddin Madenda	Gunadarma University	2
Dewi Agushinta R	Gunadarma University	2
Paranita Asnur	Affiliation unknown	2
Radityo Hendratmojo Jati Notonegoro	Affiliation unknown	1
Dewi Agushinta Rahayu	Affiliation unknown	1
Djati Kerami	University of Indonesia	1
Asep Juarna	Gunadarma University	1
Suryadi Harmanto	Gunadarma University	1
Radityo H. J. Notonegoro	Affiliation unknown	1
Ahmad Sabri	Universitas Islam Negeri Imam Bonjol Padang	1
Achmad Fahrurozi	Gunadarma University	1
Sudaryanto Sudaryanto	Affiliation unknown	1
Dewi A. Rahayu	Affiliation unknown	1

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) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Predicting levels of legal case difficulties using machine learning</dc:title>
	<dc:creator>Sari, Ilmiyati</dc:creator>
	<dc:creator>Kosasih, Rifki</dc:creator>
	<dc:creator>Indarti, Dina</dc:creator>
	<dc:subject xml:lang="en-US">machine learning, law sector</dc:subject>
	<dc:subject xml:lang="en-US">Lawyers; Legal case difficulties; Machine learning; Random forest; Support vector machine</dc:subject>
	<dc:description xml:lang="en-US">Lawyers play a crucial role in the courtroom, assisting clients in their defense. Because of their lack of legal expertise, a person or organization facing legal issues requires professional aid. However, we need to know how much money will be spent on paying lawyers. The level of complexity in a case can be used to determine lawyer costs. Therefore, in this research, we propose employing machine learning methodologies, i.e., random forest classifiers and support vector machines (SVM), to determine the level of legal case difficulties. The novelty of this research is applying a machine learning approach in predicting the level of difficulty of legal cases. The data utilized consists of 990 records, which are divided into training and testing data in a 90:10 ratio. The term frequency-inverse document frequency (TF-IDF) approach was then utilized to perform text preprocessing. The text-preprocessing findings are utilized as input in the classification process. According to the research findings, an accuracy value of 85%, a value of weighted average precision is 88%, and a value of weighted average recall is 85%, for support vector machine. Using random forest, we achieve an accuracy value of 89%, a value of weighted average precision is 85.6%, and a value of weighted average recall is 80%.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">This research was funded by PTUPT 2023 Research Grant, Ministry of Education, Culture, Research and Technology, Indonesia to Gunadarma University No. 073/E5/PG.02.00.PL/2023 in 12 April 2023</dc:contributor>
	<dc:date>2024-12-01</dc:date>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24922</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i4.pp4364-4371</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 4: December 2024; 4364-4371</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i4</dc:source>
	<dc:language>eng</dc:language>
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	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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