Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal Science and Theory of law
Achmad Chotib
Universitas Sains Indonesia
Computer Science & IT Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pelatihan Penggunaan Sistem Informasi Distribusi Bansos Berbasis Web Terintegrasi Sensor Biometric dan Foto Timestamp Miri Ardiansyah; Ali Muhammad; Angge Firizkiansah; Imron Rizki Maulana; Damara Astiningtyas; Dita Setiawan; Muhamad Aria Armada Djojosugito; Siti Herawati Fransiska Dewi; Achmad Chotib; Hasan Abdullah Muhammad
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 5 No 1 (2026): Juli 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v5i1.988

Abstract

Permasalahan dalam penyaluran bantuan sosial (bansos) masih sering ditemui, antara lain ketidaktepatan sasaran, duplikasi penerima, lemahnya validasi identitas, serta kurangnya transparansi dan akuntabilitas distribusi. Kondisi ini menimbulkan ketidakpercayaan masyarakat dan berpotensi menghambat efektivitas program bantuan. Kegiatan pengabdian kepada masyarakat ini bertujuan memberikan pelatihan dan pendampingan penerapan Sistem Informasi Distribusi Bansos Berbasis Web yang terintegrasi dengan sensor biometrik sidik jari dan fitur foto berbasis timestamp sebagai mekanisme verifikasi penerima bantuan. Metode pelaksanaan pengabdian meliputi tahap analisis kebutuhan mitra, perancangan sistem, pelatihan penggunaan aplikasi, serta pendampingan implementasi di lapangan. Sistem yang dikembangkan memungkinkan pencatatan data penerima secara terpusat, verifikasi identitas penerima menggunakan sidik jari, serta dokumentasi penyaluran bantuan melalui foto yang dilengkapi informasi waktu dan lokasi (timestamp). Pelatihan diberikan kepada aparat desa dan petugas distribusi bansos dengan pendekatan praktik langsung agar peserta mampu mengoperasikan sistem secara mandiri. Hasil kegiatan menunjukkan bahwa peserta pelatihan memiliki peningkatan pemahaman dan keterampilan dalam memanfaatkan teknologi informasi untuk mendukung proses distribusi bansos. Penerapan sistem ini membantu meminimalkan kesalahan data, mencegah penerimaan ganda, serta meningkatkan transparansi dan akuntabilitas penyaluran bantuan. Dengan demikian, kegiatan pengabdian ini diharapkan dapat menjadi solusi teknologi yang berkelanjutan dalam mendukung tata kelola distribusi bansos yang lebih efektif, tepat sasaran, dan terpercaya di tingkat masyarakat.
Tinjauan Sistematis Penerapan Artificial Intelligence dan Big Data Untuk Klasifikasi Identifikasi Kasus dan Prediksi Putusan Hakim Ali Muhammad; La Ode Mbunai; Muhammad Hasan Abdullah; Achmad Chotib; Suminah
JUSTLAW : Journal Science and Theory of law Vol. 3 No. 1 (2026): JUSTLAW : Journal Science and Theory of Law
Publisher : Universitas Sains Indonesia Publishing

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

Abstract

This study aims to systematically analyze the application of Artificial Intelligence and Big Data in legal case classification and judicial decision prediction. The study employs a Systematic Literature Review by examining relevant publications on the use of artificial intelligence technologies in the legal domain. The review focuses on the methods employed, dataset types and characteristics, preprocessing techniques, feature extraction methods, and factors affecting model performance. The findings show that methods used for case classification and judicial decision prediction include Machine Learning, Natural Language Processing, Support Vector Machine, Deep Learning, BERT, LSTM-CNN, Contrastive Learning, Knowledge Graph, Graph Contrastive Learning, Large Language Model, Multi-Task Learning, AdaBoost, and Explainable Deep Learning. The datasets are predominantly composed of legal documents, case texts, judgment texts, case facts, appeal cases, and structured judicial data. Data representation is performed through NLP, BERT, Deep Learning models, Fact Elements, Knowledge Graphs, and graph-based approaches. Preprocessing techniques are not reported consistently across the reviewed literature. Model performance is influenced by dataset quality and characteristics, data representation, algorithm and model architecture selection, completeness of case facts, relationships among legal elements, and the ability of models to capture legal context. The findings indicate a methodological development from Machine Learning and NLP toward Deep Learning, large language models, and graph-based approaches for supporting case analysis and judicial decision prediction. The review also emphasizes interpretability for responsible legal analysis and judicial decision support systems.
Co-Authors Ali Muhammad Angge Firizkiansah Ardiansyah, Miri Damara Astiningtyas Dita Setiawan Hasan Abdullah Muhammad Imron Rizki Maulana La Ode Mbunai Muhamad Aria Armada Djojosugito Muhammad Hasan Abdullah Siti Herawati Fransiska Dewi Suminah