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Journal of Electrical Engineering and Computer (JEECOM)
Published by Universitas Nurul Jadid
ISSN : 27150410     EISSN : 27156427     DOI : -
Core Subject : Science, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy
Journal of Electrical Engineering and Computer (JEECOM) is published by Engineering Faculty of Nurul Jadid University, Probolinggo, East Java, Indonesia. This journal encompasses research articles, original research report, : 1) Power Systems, 2) Signal, System, and Electronics, 3) Communication Systems, 4) Information Technology, etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 265 Documents
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Predicting Daily Goride Orders From Online Hours, Day Type, And Weather Purnomo Hadi Susilo; Mochammad Sihabudin Firdaus Rifani; Affan Bachri; Mustain Mustain; Mochammad Sholikhin
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.16937

Abstract

Daily order fluctuations make it difficult for motorcycle ride-hailing drivers to plan online working time. This study develops an interpretable multiple linear regression model to predict the number of daily GoRide orders from online hours, day type, and weather. The dataset contains 100 driver-day observations collected from eight GoRide driver-partners operating in central Tulungagung. To preserve observation order, the first 70 records were used for training and the last 30 for testing. Day type and weather were expressed as binary indicators, yielding the operational equation ŷ = −1.9887 + 1.0036X₁ + 1.4187Dweekend + 2.3968Dclear. The model obtained R² = 0.8440 and MAPE = 15.62% on training data. On the held-out test set, it achieved R² = 0.8341, MAPE = 13.48%, MAE = 0.989 orders, and RMSE = 1.322 orders. Twenty of 30 predictions (66.7%) were within one order of the actual value, and 26 (86.7%) were within two orders. However, predictions were positively biased by 0.784 orders on average, with 23 of 30 observations overpredicted and a maximum absolute error of 4.458 orders. The model was integrated into a Flask–MySQL web application for data management and interactive prediction. The results show that a compact linear model can provide useful and explainable driver-level estimates, but its reliability remains conditional on the small, locally collected sample. Rolling validation, driver-aware evaluation, richer temporal variables, and count-regression benchmarks are required before operational deployment at scale.
Physics-Informed Deep Sequential Learning and Non-Parametric Adaptive Thresholding for Integrated Microgrid Fault Detection Muhammad Akbar Barrinaya; Herminarto Akbar Nugroho
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.17123

Abstract

The rapid proliferation of clean energy microgrids—integrating solar photovoltaic (PV) generation, bidirectional power converters, and battery energy storage systems (BESS)—requires robust fault detection and isolation (FDI) to guarantee continuous operational stability. However, non-Gaussian residual profiles caused by severe irradiance intermittency and dynamic switching transients compromise traditional fixed-threshold strategies, resulting in elevated false alarm and missed detection rates. To address this challenge, this study presents a hybrid FDI framework combining physics-guided deep sequential forecasting with non-parametric adaptive thresholding. A Temporal Convolutional Network integrated with Long Short-Term Memory (TCN-LSTM), constrained by equivalent circuit and DC bus power balance equations, is developed to forecast multi-modal nominal trajectories and extract reliable diagnostic residuals. Raw electrical streams sampled at 10 kHz are down sampled via moving-window averaging to 10-second intervals to accommodate prognostic forecasting horizons. Non-parametric Kernel Density Estimation (KDE) is subsequently implemented to dynamically compute adaptive threshold boundaries from empirical non-Gaussian residual distributions. Simulation experiments under stochastic irradiance profiles and dynamic load cycles confirm that the proposed TCN-LSTM architecture achieves nominal forecasting RMSE values of 0.007 V for cell voltage and 0.306 °C for temperature, reducing the missed detection rate of PV shading mismatches and critical sensor biases compared to static thresholding. Furthermore, integration with a Local Outlier Factor (LOF) anomaly detector provides early warning margins of 83.5 minutes for micro-short circuit voltage dips and 24.6 minutes for thermal runaway precursors prior to hard-limit BMS alarms.
Quantifying Overhaul Effectiveness for a 6 kV Primary Air Fan Motor: A Multi-Parameter Diagnostic Assessment against International Standards Elih Mulyana; Pambudi Nur Hafizh; Muhamad - Habibi
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.17186

Abstract

Reliability of 6 kV auxiliary motors is critical to combustion stability in coal-fired steam power plants, yet the quantified effectiveness of overhaul interventions on such motors is seldom reported. This study evaluates a 6 kV Primary Air Fan (PA Fan) induction motor through multi-parameter diagnostics performed during a scheduled overhaul, covering insulation resistance, winding-resistance unbalance, bearing vibration, and mechanical clearance, referenced to IEEE Std 43-2013, ISO 20816-3, IEC 60034-1, NEMA MG 1, and the manufacturer design tolerances. Vibration under load decreased by 49.1% in the vertical direction at the non-drive-end (NDE) bearing, and the mean of the six load-condition readings fell by 20.5% (2.13 to 1.69 mm/s), correlating with the equalization of the NDE bearing top clearance (0.19/0.32 to 0.23/0.22 mm), which indicates that the dominant benefit of the overhaul for this motor was mechanical. Winding-resistance unbalance was 0.279%, far below the 2% acceptance limit. The post-cleaning drop in absolute insulation resistance is consistent with measurement on a warm winding after the running test; as winding temperature was not recorded, insulation integrity is confirmed by the ratio-based Dielectric Absorption Ratio (DAR, 1.62) and Polarization Index (PI, 2.39) rather than by absolute comparison. All measured parameters comply with their applicable acceptance criteria, and the motor is deemed fit for continued operation. The study quantifies overhaul effectiveness and illustrates, on a field case, the consequence of omitting the temperature normalization required by IEEE Std 43-2013: a legitimate maintenance action can be misread as dielectric degradation.
IoT-Enabled Smart Water Level Monitoring and Automatic Floodgate Control Using Arduino and Cloud Integration Sumardi Sadi; Sri Mulyati
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.17102

Abstract

Flooding in Tangerang requires real-time and reliable water level monitoring and automatic floodgate control. This research designs an IoT-based intelligent system using Arduino Mega 2560 for data acquisition, HC-SR04 ultrasonic sensor for non-contact measurement, SIM800L for SMS gateway redundancy, ESP8266 for ThingSpeak cloud connectivity, and Omron CPM1A PLC for floodgate motor actuation. The system adopts four-layer architecture: perception, control, network, and application. Threshold logic refers to Indonesian flood alert standards: Siaga 3 (40-60 cm), Siaga 2 (60-80 cm, gate 50%), and Siaga 1 (>80 cm, gate 100%). Validation was conducted in laboratory tank and UMT drainage channel for 30 days. Results show sensor accuracy 98.2% with R²=0.9987, average response time 2.3 seconds from threshold detection to full actuation, SMS delivery success 97.5% within 5 seconds (average 4.2 s), ThingSpeak uplink success 98.9% with 1.8 s latency, and uptime 99.1% for 72 hours. Power consumption is 8.5 W suitable for solar. Hybrid Arduino-PLC architecture provides cost-effectiveness Rp 4.5M vs commercial Rp 25-40M and industrial reliability with fail-safe interlock. The system contributes to smart city infrastructure and urban flood mitigation.
Feature-Normalized k-Nearest Neighbor Classification for Real-Time Photovoltaic Maintenance Monitoring Novan Akhiriyanto; Arie Susilo; Wasis Waskito Adi
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.16749

Abstract

Photovoltaic (PV) module performance is significantly affected by environmental factors, particularly solar irradiation, panel surface temperature, and soiling, requiring a monitoring and maintenance system responsive to varying PV operating conditions. This study proposes a maintenance condition classification system integrating real time sensor measurement with a feature-normalized k-Nearest Neighbor (k-NN) algorithm to identify four panel conditions (Neglected, PV Normal, Dusty PV, and Hot PV) requiring specific maintenance intervention. The methodological contribution comprises three aspects: an explicit labeling procedure with thresholds derived from empirical analysis of 250 field samples, avoiding circularity between label determination and classification features; a Min-Max normalization scheme ensuring each feature contributes proportionally to the Euclidean distance calculation in k-NN; and stratified 10-fold cross validation providing an unbiased estimate of generalization performance while revealing overfitting risks otherwise hidden in a simple train-test split. Experimental results show that the k=3 configuration achieves 95.2% ± 2.99% accuracy with superior consistency, balancing high accuracy and stability for deployment on power constrained microcontrollers such as the ESP32. The system integrates a Nextion graphical interface for real-time monitoring and condition notification, providing a platform for condition based maintenance decision making in community scale and industrial photovoltaic installations.

Page 27 of 27 | Total Record : 265

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