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Pelatihan Sistem Keamanan IoT berbasis Model PBL untuk Warga Ridwansyah; Saharuddin; Nurfauziah; Darlan Sidik; Ahmad Risal
Vokatek : Jurnal Pengabdian Masyarakat Volume 3: Issue 3 (Oktober 2025)
Publisher : Sakura Digital Nusantara

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

Abstract

Program Pengabdian kepada Masyarakat (PKM) ini bertujuan meningkatkan kapasitas warga dalam mengelola sistem keamanan perumahan berbasis Internet of Things (IoT) di kawasan padat penduduk. Pelatihan yang dilaksanakan di Kelurahan Mannuruki, Kecamatan Tamalate, Kota Makassar, mencakup pengenalan perangkat IoT, penggunaan aplikasi pemantauan real-time, pemeliharaan, dan troubleshooting dasar. Model Problem Based Learning (PBL) digunakan untuk mendorong partisipasi aktif melalui praktik perakitan sensor, konfigurasi mikrokontroler, serta pengujian notifikasi otomatis. Peserta juga dibekali materi keamanan siber untuk meminimalkan risiko akses ilegal dan kerentanan sistem. Hasil kegiatan menunjukkan peningkatan kemampuan warga dalam mengoperasikan sistem IoT secara mandiri, mendeteksi aktivitas mencurigakan, dan merespons insiden lebih cepat. Dukungan peserta dan aparat setempat menegaskan relevansi program ini sebagai langkah menuju sistem keamanan berbasis komunitas yang terintegrasi.
Adaptive Pulse-Width Modulation Algorithm for Energy-Efficient Control of Robotic Arm Actuators Sutarsi Suhaeb; Ahmad Risal
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10318

Abstract

The development of energy-efficient robotic systems has become increasingly important, particularly in servo-driven robotic arms where continuous PWM signals lead to unnecessary energy loss and thermal stress. This study proposes an adaptive pulse-width modulation (PWM) algorithm designed to activate servo signals only during motion phases and automatically deactivate them once the target position is reached. A four-degree-of-freedom robotic arm prototype was developed using an ESP32 microcontroller, MG90S servos, and ACS712-based current monitoring to evaluate power efficiency under conventional continuous PWM and the proposed adaptive control. Experimental results demonstrate a 28–33% reduction in average power consumption, a decrease of 6–8 °C in servo operating temperature, and the preservation of positional accuracy within ±5%. These findings confirm that significant energy savings and thermal improvements can be achieved without modifying hardware components. The proposed algorithm offers a practical, lightweight, and software-based optimization approach suitable for educational, research, and low-power robotic applications. This work introduces a distinct adaptive activation strategy that fully disables PWM in steady-state conditions, representing a low-cost and effective contribution to sustainable servo control.