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All Journal Literasi: Jurnal Pengabdian Masyarakat dan Inovasi SmartComp Applied Information Technology and Computer Science (AICOMS) Journal of Law, Human Rights, Immigration, and Corrections
David Bani Adam
Universitas Harkat Negeri
Humanities Computer Science & IT Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Implementation Challenges of the New Criminal Code for Victim Protection: A Restorative Justice Perspective Raden Azhari Setiadi; David Bani Adam; Nur Baiti Rahman; Muhammad Ruslan Afandi
Journal of Law, Human Rights, Immigration, and Corrections Vol. 1 No. 2 (2026): Journal of Law, Human Rights, Immigration, and Corrections
Publisher : Yayasan Cerdas Pedia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65101/lawric.v1i2.162

Abstract

The implementation of the new Criminal Code (KUHP) in Indonesia presents major challenges in protecting crime victims, especially due to regulatory inconsistencies, disharmony among law enforcement agencies, and limited understanding of Restorative Justice principles. This study analyzes both normative and empirical aspects while offering more humanistic, restorative justice-based solutions. The research method is normative juridical, using statutory and case approaches. Findings indicate that regulatory harmonization, active involvement of victims and the community, strengthening judges’ roles, and improving education and capacity building for relevant institutions are key steps for optimizing victim protection. Additionally, developing technical guidelines for Restorative Justice implementation and increasing synergy law enforcement main recommendations. Empowering victims with information access and psychological support is also essential for optimal recovery. The conclusion emphasizes need for comprehensive, cross-sectoral collaboration and ongoing evaluation to ensure effective, fair, and truly victim-oriented application of Restorative Justice in the era of the new Criminal Code.
Implementasi Sistem Informasi Manajemen Inventaris Web-Based untuk Meningkatkan Efisiensi di SMK Zafirah Safar Dwi Kurniawan; Muhammad Yanuar; David Bani Adam
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 15, No 2 (2026): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i2.7868

Abstract

Penelitian ini bertujuan untuk mengimplementasikan sistem informasi manajemen inventaris berbasis web di SMK Zafirah guna meningkatkan efisiensi dalam pengelolaan inventaris sekolah. Dalam era digital saat ini, pengelolaan data yang efisien menjadi kebutuhan mendesak bagi institusi pendidikan. Sistem ini dirancang untuk menggantikan metode manual yang sering kali menyebabkan inefisiensi dan kesalahan dalam pencatatan data inventaris. Dengan memanfaatkan teknologi berbasis web, sistem ini memungkinkan pengelolaan data inventaris yang lebih terstruktur dan terotomatisasi, sehingga dapat mengurangi kesalahan pencatatan dan meningkatkan efisiensi operasional. Metode penelitian yang digunakan adalah System Development Life Cycle (SDLC) dengan model Waterfall, yang memberikan pendekatan terstruktur dalam pengembangan perangkat lunak. Hasil penelitian menunjukkan bahwa implementasi sistem ini dapat meningkatkan efisiensi operasional, mengurangi kesalahan manusia, dan meningkatkan akurasi data inventaris. Dengan demikian, sistem informasi manajemen inventaris berbasis web ini diharapkan dapat memberikan kontribusi signifikan dalam meningkatkan efisiensi dan efektivitas pengelolaan inventaris di SMK Zafirah.
Pelatihan Deteksi Lesi Periapikal Berbasis CNN–LSTM Retinex untuk Diagnosis Radiografis Dokter Gigi Safar Dwi Kurniawan; David Bani Adam
Literasi Jurnal Pengabdian Masyarakat dan Inovasi Vol 6 No 1 (2026)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang Jl. Rangga Sentap, Dalong Sukaharja, Ketapang 78813. Telp. (0534) 3030686 Kalimantan Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/x5vqxr05

Abstract

Accurate periapical lesion diagnosis represents a significant clinical challenge for dentists, particularly in primary care clinics with limited access to Computer-Aided Diagnosis (CAD) systems. This Community Service (PKM) program aims to train dental medical personnel in using an AI-based periapical lesion detection system integrating CNN–LSTM architecture with Retinex image enhancement. The program was conducted at Primary Dental Clinics in the City Region over three months (October–December 2024), involving 32 dentists and 8 radiographers. Methods included socialization, intensive workshops, clinical case simulations, and technical mentoring. Evaluation results showed an average competency score increase of 34.7% (from 61.2 to 82.4 out of 100), with 87.5% of participants successfully operating the system independently. Participant satisfaction reached 89.1% (very satisfied category). The implemented AI system achieved periapical lesion detection accuracy of 98.4% with 97% recall, far exceeding manual diagnosis sensitivity averages of 70–85%. This PKM demonstrates that AI diagnostic technology transfer to primary dental clinical practitioners is feasible and significantly impacts dental healthcare quality improvement
Arsitektur Hibrida CNN–LSTM Berbasis Retinex untuk Deteksi Lesi Periapikal pada Radiograf CBCT–Panoramik Safar Dwi Kurniawan; Tri Haryo Nugroho; David Bani Adam
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/vc18dh25

Abstract

Periapical lesion detection plays a crucial role in endodontic diagnosis; however, manual interpretation of Cone-Beam Computed Tomography (CBCT) and panoramic radiographs remains time-consuming, highly dependent on the clinician's expertise, and susceptible to diagnostic variability. This study proposes a hybrid deep learning framework that integrates a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture, combined with Retinex-based image enhancement, for the automatic detection and classification of periapical lesions. Retinex enhancement is employed as a preprocessing step to normalize illumination and improve lesion contrast. The hybrid CNN-LSTM model captures both spatial and contextual dependencies through sequential patch-based processing of panoramic and CBCT images. Using a dataset of 1,500 annotated images collected from clinical radiographic datasets and publicly available sources, the proposed model achieved an accuracy of 97.8%, precision of 96.4%, recall of 95.9%, and an F1-score of 0.96, significantly outperforming conventional CNN and U-Net models. These findings demonstrate that the integration of image enhancement and hybrid deep learning improves sensitivity to small lesions while reducing false-negative detections, offering a clinically viable AI-assisted approach for endodontic diagnosis.
Co-Authors Muhammad Ruslan Afandi Muhammad Yanuar Nur Baiti Rahman Raden Azhari Setiadi Safar Dwi Kurniawan Tri Haryo Nugroho