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A systematic literature review of machine learning methods in predicting court decisions

IAES International Journal of Artificial Intelligence · Vol. 10 No. 4 (2021) · hlm. 1091–1102

DOI
10.11591/ijai.v10.i4.pp1091-1102
ISSN
2252-8938, 2089-4872
Terbit
2021-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.

Kata kunci

Jaringan kolaborasi para penulis

40 penulis · 108 tautan kepenulisan bersama · 32 lintas organisasi · menampilkan 40 yang paling banyak terhubung

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  • Universiti Teknikal Malaysia Melaka
  • University of Technology Malaysia
  • Universitas Ahmad Dahlan
  • Islamic Azad University, Tehran
  • Universiti Malaysia Perlis
  • Universiti Putra Malaysia
  • Universiti Sains Malaysia
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Auzani JidinUniversiti Teknikal Malaysia Melaka34
Nik Rumzi Nik IdrisUniversity of Technology Malaysia18
Mohammad JannatiUniversity of Technology Malaysia16
Mohd Hatta JopriUniversiti Teknikal Malaysia Melaka15
Awang JusohUniversity of Technology Malaysia15
Nik Rumzi Nik IdrisAfiliasi tidak diketahui14
Mohd Junaidi Abdul AzizUniversity of Technology Malaysia10
Mustafa ManapUniversiti Teknikal Malaysia Melaka10
Kasrul Abdul KarimAfiliasi tidak diketahui9
Lina HandayaniUniversitas Ahmad Dahlan9
Mohamed SalemUniversiti Sains Malaysia8
Mahmoud ZadehbagheriIslamic Azad University, Tehran8
Abdul Rahim AbdullahUniversiti Teknikal Malaysia Melaka6
Mochammad FactaDiponegoro University6
Mohammad Javad KianiIslamic Azad University, Tehran5
Watra ArsadiandoAfiliasi tidak diketahui5
Mohammad Faridun Naim TajuddinUniversiti Malaysia Perlis5
Mohd Junaidi Abdul AzizAfiliasi tidak diketahui5
Imam Much Ibnu SubrotoSultan Agung Islamic University4
M. Rahimi YusoffUniversiti Teknikal Malaysia Melaka4
Muhammad AdoUniversity of Technology Malaysia4
Hendril Satrian PurnamaAfiliasi tidak diketahui4
Anggit PamungkasAfiliasi tidak diketahui4
Vahdat NazerianUniversity of Mazandaran4
Muhammad Zaid AihsanUniversiti Malaysia Perlis4
Chibuike Peter OhanuUniversity of Nigeria4
Tri WahonoAfiliasi tidak diketahui4
Rahim IldarabadiAfiliasi tidak diketahui4
Azralmukmin AzmiAfiliasi tidak diketahui4
Shahrin Md AyobAfiliasi tidak diketahui4
Siti Azura Ahmad TarusanAfiliasi tidak diketahui4
Ibrahim AlhamrouniAfiliasi tidak diketahui4
Aiman Zakwan JidinAfiliasi tidak diketahui4
Deris StiawanSriwijaya University3

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A systematic literature review of machine learning methods in predicting court decisions</dc:title>
	<dc:creator>Rosili, Nur Aqilah Khadijah</dc:creator>
	<dc:creator>Zakaria, Noor Hidayah</dc:creator>
	<dc:creator>Hassan, Rohayanti</dc:creator>
	<dc:creator>Kasim, Shahreen</dc:creator>
	<dc:creator>Rose, Farid Zamani Che</dc:creator>
	<dc:creator>Sutikno, Tole</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Judicial cases, Legal system, Machine learning, Predictive model</dc:subject>
	<dc:description xml:lang="en-US">Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21018</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i4.pp1091-1102</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 4: December 2021; 1091-1102</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21018/13342</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 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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