cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Suyahman
Contact Email
suyahman.com@gmail.com
Phone
+6285155377406
Journal Mail Official
sahpublisher@gmail.com
Editorial Address
Jl. Pedan Karangdowo, Munggung, Karangdowo, Klaten, Central Java, Indonesia
Location
Kab. klaten,
Jawa tengah
INDONESIA
Journal of Artificial Intelligence and Legal Technology
Published by PT Solusi Administrasi Hukum
ISSN : -     EISSN : 3123786X     DOI : -
Core Subject :
The Journal of Artificial Intelligence and Legal Technology (JAILT) is an international, peer-reviewed journal dedicated to advancing interdisciplinary research in artificial intelligence (AI) and its applications in the legal domain. JAILT serves as a platform for academics, practitioners, and policymakers to discuss cutting-edge developments in AI-driven legal reasoning, regulatory compliance, and intelligent legal systems. The journal publishes high-quality research articles, reviews, and research notes that address both theoretical and applied aspects of AI in law, fostering dialogue on legal, ethical, and societal challenges.
Arjuna Subject : -
Articles 12 Documents
 1   2 
Cluster Analysis of Open Unemployment Rates Across Indonesian Provinces Using the K-Means Algorithm Egi Dio Bagus Sudewo; Fadila Khairani; Irawati; Niken Ayu; Andri Syahfikri
Journal of Artificial Intelligence and Legal Technology Vol. 2 No. 2 (2026): August 2026
Publisher : Sah Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengangguran tetap menjadi tantangan yang terus-menerus dalam pembangunan ekonomi regional Indonesia, yang tercermin dalam variasi Tingkat Pengangguran Terbuka (Open Unemployment Rate/OUR) di berbagai provinsi. Studi ini bertujuan untuk mengidentifikasi dan mengklasifikasikan pola pengangguran regional menggunakan pendekatan pengelompokan berbasis data. Desain kuantitatif dengan kerangka kerja pembelajaran tanpa pengawasan (unsupervised learning) digunakan, memanfaatkan data sekunder tentang Tingkat Pengangguran Terbuka provinsi untuk tahun 2025 yang diperoleh dari Badan Pusat Statistik (BPS). Algoritma pengelompokan K-Means diterapkan setelah pra-pemrosesan dan normalisasi data, sedangkan jumlah cluster optimal ditentukan menggunakan Metode Elbow. Hasil menunjukkan bahwa tiga cluster mewakili struktur pengelompokan yang paling tepat, mengkategorikan provinsi ke dalam cluster pengangguran rendah (3,03%), sedang (4,41%), dan tinggi (6,32%). Temuan ini mengungkapkan heterogenitas regional yang signifikan dalam kondisi pasar tenaga kerja dan menunjukkan bahwa kesenjangan pengangguran secara struktural berbeda di berbagai provinsi. Studi ini menegaskan efektivitas K-Means dalam mengungkap pola regional laten tanpa bergantung pada asumsi kausal dan memberikan landasan empiris untuk mengembangkan kebijakan ketenagakerjaan yang tepat sasaran dan peka terhadap wilayah di Indonesia.
Explainable XGBoost for Indonesian Hoax Detection under the Electronic Transactions Law Rizka Nadialif; Landung Sudarmana; Selvi Dwi Hartiyani; Sapriani Gustina; Ardy Wicaksono; Agatha Pricillia Sekar Tamtomo
Journal of Artificial Intelligence and Legal Technology Vol. 2 No. 2 (2026): August 2026
Publisher : Sah Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid circulation of misleading information in digital spaces creates a need for screening tools that are accurate, transparent, and suitable for human review. This study develops a reproducible Indonesian hoax-detection pipeline and examines whether model explanations can support cautious legal review. The experiment uses a political-hoax text corpus with fixed training, validation, and test splits. The primary classifier combines word- and character-level TF-IDF features with XGBoost, while a frozen-encoder IndoBERT-Lite model is evaluated as a CPU pilot. TreeSHAP summarizes global and local feature contributions, and LIME is used to inspect borderline predictions. On the held-out test set, XGBoost achieved 0.9725 accuracy, 0.9724 macro-F1, 0.9957 ROC-AUC, and a 0.0235 Brier score; the constrained IndoBERT pilot reached a validation macro-F1 of 0.3343 and is not treated as a final benchmark. The most influential features included source and article-genre markers such as “baca juga,” “referensi,” “Kompas,” “Facebook,” and “foto hoaks,” indicating that the model may learn publisher or writing-style shortcuts in addition to claim-related signals. The audit also identified normalized duplicate overlap across the training-validation and training-test splits. The resulting system should therefore support triage, explanation, and documentation by trained reviewers, not serve as a standalone basis for determining the truth of a claim or establishing an Electronic Information and Transactions Law violation.

Page 2 of 2 | Total Record : 12

 1   2 

Filter by Year

2025 2026


Reset
Filter By Issues
All Issue Vol. 2 No. 2 (2026): August 2026 Vol. 2 No. 1 (2026): February 2026 Vol. 1 No. 1 (2025): August 2025