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All Journal Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal of Electrical Engineering Research
Syahputra, Mhd Rizki
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Social Sciences

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Kinerja Helm Pelindung Diri pada Pekerja Industri Menggunakan Sensor Light Dependent Resistor untuk Minimalisir Kecelakaan di Desa Klambir Lima Tarigan, Adi Sastra Pengalaman; Syahputra, Mhd Rizki; Ajagar, Syah
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 4 No 1 (2025): Juli 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v4i1.556

Abstract

Keselamatan kerja merupakan aspek penting dalam industri untuk melindungi pekerja dari risiko kecelakaan. Salah satu alat pelindung diri yang wajib digunakan adalah helm pengaman. Namun, kepatuhan pekerja dalam menggunakan helm masih menjadi tantangan, sehingga berpotensi meningkatkan risiko kecelakaan kerja. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kesadaran dan keselamatan kerja melalui pemanfaatan teknologi sensor Light Dependent Resistor (LDR) yang terintegrasi pada helm pelindung diri. Sensor LDR berfungsi mendeteksi penggunaan helm secara real-time dan memberikan peringatan apabila helm tidak digunakan sebagaimana mestinya. Implementasi dilakukan pada pekerja industri di [lokasi sasaran], dengan tahapan sosialisasi, pemasangan perangkat, serta pelatihan penggunaan. Hasil kegiatan menunjukkan bahwa penggunaan helm dengan sensor LDR dapat meminimalisir potensi kecelakaan akibat kelalaian pemakaian helm, serta meningkatkan kepatuhan pekerja terhadap prosedur keselamatan.
Analysis of Power Quality in Technology Based Power Systems Tharo, Zuraidah; Wibowo, Pristisal; Syahputra, Mhd Rizki
Journal of Electrical Engineering Research Vol. 2 No. 1 (2026): Januari 2026
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/joeer.v2i1.23

Abstract

The rapid advancement of power system technologies has significantly transformed the way electrical power quality (PQ) is monitored, managed, and improved. The increasing penetration of non-linear loads, automation systems, and renewable energy sources has intensified power quality challenges, including harmonic distortion, voltage sags, voltage fluctuations, and transient disturbances. This study aims to analyze the impact of modern technological approaches on power quality enhancement in contemporary power systems. A descriptive-analytical research methodology was employed through a systematic review of recent scientific literature, international standards, and technical reports related to power quality management. The collected data were analyzed using qualitative content analysis and comparative evaluation to assess the effectiveness of automation, machine learning, Internet of Things (IoT)-based monitoring systems, and power electronic mitigation devices. The results indicate that integrated technological solutions significantly improve power quality performance compared to conventional approaches. Machine learning techniques, particularly Artificial Neural Networks, demonstrate high accuracy in disturbance classification and adaptive control, while IoT-based systems enable real-time monitoring and rapid response to power quality deviations. Furthermore, power electronic devices such as Active Power Filters, Static Var Compensators, and Dynamic Voltage Restorers effectively mitigate harmonics and stabilize voltage. However, challenges related to implementation cost, system complexity, cybersecurity, and regulatory inconsistency remain. The study concludes that an integrated, intelligent, and data-driven framework is essential for sustainable power quality management in modern power systems.
Co-Authors Ajagar, Syah Tarigan, Adi Sastra Pengalaman Wibowo, Pristisal Zuraidah Tharo