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JASEE Journal of Application and Science on Electrical Engineering
Published by UNIVERSITAS WIDYAGAMA MALANG
ISSN : 27213625     EISSN : 2721320X     DOI : https://doi.org/10.31328/jasee.v1i02
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
Teknik Elektro Fakultas Teknik Universitas Widyagama Malang mempublikasikan JASEE Journal of Application and Science on Electrical Engineering sebagai jurnal open access yang memuat tulisan ilmiah hasil review, penelitian, aplikasi dan pengembangan di bidang Teknik Elektro. Focus and Scope : Sistem pembangkit, transmisi, distribusi dan proteksi tenaga listrik, Transformator, elektronika dan kualitas daya listrik, motor-motor listrik, Sistem kendali, pengukuran dan instrumentasi elektronik, mikrokontroler, sistem tertanam berbasis arduino dan FPGA, instrumentasi medik, Pengolahan sinyal multimedia, jaringan telekomunikasi dan sensor, jaringan komputer, elektronika komunikasi, disain antena komunikasi, radio kognitif, Robotika dan aplikasi kecerdasan buatan.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 73 Documents
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Rancang Bangun Sistem Monitoring Kondisi Bearing Motor Satu Fasa Berbasis IoT Dava Saputra Effendy; Gigih Priyandoko; Mohammad Mukhsim
JASEE Journal of Application and Science on Electrical Engineering Vol. 7 No. 1 (2026): JASEE-March
Publisher : Program Studi Teknik Elektro - Fakultas Teknik - Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jasee.v7i1.02

Abstract

Single-phase induction motors are widely used in industrial support systems such as compressors, cooling fans, and pumps. Bearing deterioration can alter motor vibration and operating current and may reduce equipment reliability. This study develops an Internet of Things-based condition-monitoring prototype for a 1/4 horsepower single-phase induction motor using an MPU6050 accelerometer, an ACS712 current sensor, an ESP32 microcontroller, a liquid crystal display, and a Firebase realtime database. Sensor data are processed by the ESP32, displayed locally, and transmitted through a wireless network for remote observation. Tests were conducted for 100 seconds under normal and abnormal bearing conditions, with three repetitions for each condition. The average Y-axis acceleration reading changed from -8 LSB in the normal condition to -372 LSB in the abnormal condition, indicating a measurable shift in the recorded vibration level for the tested configuration. The ACS712 provided current readout and the ESP32 successfully transmitted monitoring data to Firebase. The prototype therefore supports early condition monitoring, while generalized fault classification and current-based discrimination require further validation.
Analisis Deviasi Pengukuran Energi Listrik antara kWh Meter PLN dan Power Meter Digital pada Site BTS Telekomunikasi Eko Wahyu Santoso
JASEE Journal of Application and Science on Electrical Engineering Vol. 7 No. 1 (2026): JASEE-March
Publisher : Program Studi Teknik Elektro - Fakultas Teknik - Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jasee.v7i1.03

Abstract

A base transceiver station site operates continuously, so electricity cost is a major operationalexpenditure. Two energy meters normally work in parallel at such a site: the utility kilowatt-hourmeter used as the legal basis for billing and an internal digital power meter connected to the powermanagement system for operational monitoring. A discrepancy between the two readings canreduce the reliability of bill verification. This study analyses the measurement deviation betweenboth instruments at a base transceiver station site in Lamongan Regency and evaluates the voltageprofile, current profile, and three-phase load unbalance at the same site. A comparative quantitativemethod was applied by recording both meters at four observation points within ten days. Duringthe ten-day period, the utility meter registered 2216.08 kilowatt-hours, while the digital powermeter registered 2205.35 kilowatt-hours, giving a deviation of 10.73 kilowatt-hours or 0.48 percent.This value is lower than the ±1 percent class 1 benchmark used for comparison in this study. Thevoltage unbalance stayed below two percent, whereas the current unbalance reached 29 to 46percent; therefore, redistribution of the single-phase loads is recommended.
Sistem Deteksi Alat Pelindung Diri Berbasis YOLOv8n pada Jetson Nano untuk Industri Migas Amien Thohari Yudhistira; Sabar Setiawidayat; Istiadi; Diky Siswanto
JASEE Journal of Application and Science on Electrical Engineering Vol. 7 No. 1 (2026): JASEE-March
Publisher : Program Studi Teknik Elektro - Fakultas Teknik - Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jasee.v7i1.04

Abstract

Occupational safety in the oil and gas industry requires strict compliance with Personal Protective Equipment (PPE); however, manual inspection is prone to human error and lacks effectiveness. This research designs and implements a PPE Smart Station based on the YOLOv8n algorithm on an NVIDIA Jetson Nano A02 embedded system to automatically and in real-time detect the completeness of three main PPE types—helmet, coverall, and gloves—within the industrial environment of PT Husky-CNOOC Madura Limited. The system employs a state machine with PASS/DENIED output, a real-time 2D visualization of missing PPE on the worker’s anatomical regions, and Indonesian-language text alerts with automatic audio warnings. Results are reported at two levels: (i) model evaluation on a limited test batch yielded 99.0% precision and 100% recall under near-ideal capture conditions; and (ii) field system evaluation on 30 samples at a single installation point, assessed with a confusion matrix at the PASS/DENIED decision level, yielded 93.3% accuracy, 96.0% precision, 96.0% recall, 96.0% F1-measure, and a 6.7% error rate. The results indicate the system’s initial feasibility as an AI-based occupational-safety monitoring solution under the evaluated conditions, while larger-scale, multi-site validation remains necessary.

Page 8 of 8 | Total Record : 73

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