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All Journal JURNAL MASYARAKAT NEGERI ROKANIA JOURNAL OF ICT APLICATIONS AND SYSTEM
Elyandri Prasiwiningrum
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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A Systematic Literature Review on Optimizing Mask Detection Systems Using Convolutional Neural Networks for Public Health and Safety Savira Putri Ayu, Tengku; Annisa Nur Afidah; Yuliani; Fernanda Abi Maulana; Elyandri Prasiwiningrum
Journal of ICT Applications System Vol 3 No 2 (2024): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v3i2.391

Abstract

The COVID-19 pandemic has emphasized the critical importance of mask-wearing as a preventive measure to mitigate virus transmission. However, ensuring compliance with mask mandates in public spaces remains a challenge. This study conducts a Systematic Literature Review (SLR) to explore the application of Convolutional Neural Networks (CNNs) in developing automated mask detection systems. CNNs are widely recognized for their ability to process complex visual patterns with high accuracy, making them ideal for real-time detection in images and videos. This review evaluates various CNN architectures, datasets, and preprocessing techniques used in mask detection systems. The findings highlight significant advancements, such as achieving detection accuracies exceeding 95% under controlled conditions, while also identifying challenges like dataset diversity, model generalization, and computational requirements. Additionally, the integration of CNN-based mask detection systems with Internet of Things (IoT) technologies is explored for enhanced monitoring and enforcement of health protocols. This research aims to provide a comprehensive understanding of current approaches and future directions for optimizing mask detection systems, contributing to public health and safety
Explainable Transformer-Based Object Detection for Autonomous Systems under Adversarial and Low-Light Conditions Elyandri Prasiwiningrum; Aris Sudaryanto
Journal of ICT Applications System Vol 4 No 2 (2025): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v4i2.444

Abstract

Recent advancements in object detection have demonstrated remarkable performance in autonomous systems; however, most deep learning models still suffer significant accuracy degradation under low-light or adversarial conditions. This study proposes an Explainable Transformer-Based Object Detection (ETOD) framework that integrates Vision Transformer (ViT) architecture with Explainable Artificial Intelligence (XAI) mechanisms to achieve robust and interpretable object detection in adverse environments. The proposed ETOD model employs a dual-branch structure: (i) a low-light enhancement module that uses contrastive illumination normalization to recover critical features, and (ii) a transformer-based detection head optimized for global contextual reasoning. To ensure explainability, Grad-CAM and attention visualization maps are incorporated to highlight the model’s focus regions, providing interpretive insights for human operators and safety auditors. Experimental evaluation was conducted using benchmark datasets (ExDark, BDD100K-Night, and COCO-Adversarial) with simulated adversarial perturbations and low-illumination conditions. The proposed ETOD achieved a 12.8% improvement in mAP over standard DETR and 17.5% higher robustness against adversarial attacks while maintaining real- time inference on edge GPUs. Qualitative analysis demonstrates that the explainability module provides clear visual cues that correlate strongly with detected object boundaries. The findings suggest that integrating transformer- based detection with explainable reasoning mechanisms offers a promising pathway for trustworthy and safety-critical perception systems in autonomous vehicles and drones
Pelatihan Internet of Things (IoT) Berbasis Mikrokontroler untuk Meningkatkan Literasi Teknologi dan Keterampilan Praktik Mahasiswa Fakultas Ilmu Komputer Universitas Rokania Ridwan; Detri Amelia Chandra; Muslim; Elyandri Prasiwiningrum; Sri Wahyudi; Jufri
JURNAL MASYARAKAT NEGERI ROKANIA Vol 7 No 01 (2026): JURNAL MASYARAKAT NEGERI ROKANIA
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Rokania

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jmnr.v7i01.521

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi teknologi dan keterampilan praktik mahasiswa Fakultas Ilmu Komputer melalui pelatihan penerapan Internet of Things (IoT) berbasis mikrokontroler. Kegiatan dilaksanakan dengan pendekatan edukatif, partisipatif, dan praktik berbasis proyek yang melibatkan 30 mahasiswa. Materi pelatihan meliputi konsep dasar IoT, konfigurasi mikrokontroler ESP32, integrasi sensor dan aktuator, pemrograman menggunakan Arduino IDE, komunikasi nirkabel, pemantauan data real-time, dan pengujian dashboard sederhana. Teknik pengumpulan data dilakukan melalui observasi, pre-test dan post-test, tugas praktik, respon peserta, serta dokumentasi kegiatan. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan praktik mahasiswa. Rata-rata nilai peserta meningkat dari 54,2 pada pre-test menjadi 86,1 pada post-test dengan persentase peningkatan sebesar 31,9%. Mahasiswa menjadi lebih mampu menjelaskan arsitektur IoT, merangkai rangkaian dasar, membaca data sensor, mengunggah program ke mikrokontroler, dan menampilkan hasil monitoring melalui dashboard web sederhana. Secara keseluruhan, pelatihan ini efektif dalam memperkuat literasi IoT, berpikir komputasional, dan kompetensi praktik mahasiswa. Kegiatan ini juga menegaskan bahwa praktik IoT berbasis mikrokontroler relevan digunakan sebagai strategi pembelajaran kontekstual untuk menyiapkan mahasiswa menghadapi perkembangan teknologi industri digital.
Co-Authors Annisa Nur Afidah Aris Sudaryanto Detri Amelia Chandra Fernanda Abi Maulana Jufri Muslim Ridwan Savira Putri Ayu, Tengku Sri Wahyudi YULIANI