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Indra Ava Dianta
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INDONESIA
Hakim: Jurnal Ilmu Hukum dan Sosial
ISSN : 29876737     EISSN : 29877539     DOI : 10.51903
Core Subject : Humanities, Social,
Sub Rumpun ILMU POLITIK 1 Ilmu Politik 2 Kriminologi 3 Hubungan Internasional 4 Ilmu Administrasi (Niaga, Negara, Publik, Pembangunan, Dll) 5 Kriminologi 6 Ilmu Hukum 7 Ilmu Pemerintahan 8 Ilmu Sosial dan Politik 9 Studi Pembangunan (Perencanaan Pembangunan, Wilayah, Kota) 10 Ketahanan Nasional 11 Ilmu Kepolisian 12 Kebijakan Publik 13 Bidang Ilmu Politik Lain Yang Belum Tercantum Sub Rumpun ILMU SOSIAL 1 Ilmu Kesejahteraan Sosial 2 Sosiologi 3 Humaniora 3 4 Kajian Wilayah (Eropa, Asia, Jepang, Timur Tengah Dll) 5 Arkeologi 6 Ilmu Sosiatri 7 Kependudukan (Demografi, dan Ilmu Kependudukan Lain) 8 Sejarah (Ilmu Sejarah) 9 Kajian Budaya 10 Komunikasi Penyiaran Islam 11 Ilmu Komunikasi 12 Antropologi 13 Bidang Sosial Lain Yang Belum Tercantum
Articles 211 Documents
Analisis Prediktif Putusan Perdata Berbasis Machine learning di Indonesia Sikky, Florentino; Mengge, Valentino
Hakim: Jurnal Ilmu Hukum dan Sosial Vol. 3 No. 4 (2025): HAKIM: Jurnal Ilmu Hukum dan Sosial
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/zqn0g438

Abstract

The rapid advancement of artificial intelligence, particularly machine learning (ML), has opened new opportunities in the legal domain, especially in addressing the long-standing issue of inconsistency in civil court decisions in Indonesia. This study aims to develop and evaluate predictive models of civil case outcomes using various ML approaches, including Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and IndoBERT. A dataset of 199,000 published civil court decisions was collected, pre-processed, and annotated into three categories: granted, rejected, and partially granted. The experimental results demonstrate that IndoBERT achieved the best performance with an accuracy of 83.5% and an F1-macro score of 81.7%, outperforming classical models. Feature analysis indicated that contractual terms, evidence, and core legal reasoning were the most influential predictors. These findings highlight the potential of ML to enhance consistency, transparency, and predictability in the Indonesian judiciary, while also raising important considerations regarding ethics, bias, and interpretability. The study contributes to both the theoretical discourse on legal analytics and the practical implementation of AI in judicial reform.