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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 32 Documents
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Search results for , issue "vol 8, no 2 (2026)" : 32 Documents clear
Price-Incentive Distribution Grid Load Scheduling Using Hybrid Grey Wolf Optimization Biobele Alexander Wokoma; Kinba Queen Blue-Jack
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.v8i2.16815

Abstract

Distribution feeders increasingly require load schedules that respond to time-varying prices while preserving operational limits. This paper presents a Hybrid Grey Wolf Optimizer (HGWO) for 24-hour distribution grid load scheduling underprice incentives. The method augments the leader-guided position update of the conventional Grey Wolf Optimizer with a momentum-based directional term and evaluates candidate schedules through a penalty-aware objective that combines energy price, incentive reward, and constraint violations. The model was implemented in MATLAB and assessed using hourly price, base-load, and incentive data obtained for a representative 11 kV feeder of the Port Harcourt Electricity Distribution Company. Thirty independent runs were conducted and compared with classical GWO. HGWO attained a best objective value of 38,910 ¢, approximately 2.3% below the GWO result, and reached a stable solution in 58 iterations compared with 82 iterations. Its standard deviation decreased from 760 ¢ to 412 ¢, indicating more consistent search performance. Sensitivity tests on penalty coefficient and population size further showed lower and flatter objective responses. The results demonstrate that HGWO can improve convergence speed, robustness, and price-responsive peak-load redistribution for day-ahead feeder scheduling.
Performance Evaluation of YOLOv8-Pose for Vision-Based Fatigue Detection via Multi-Feature Behavioral Analysis Dinda Ayu Permatasari; Rifki Noviandra Lestari; Dimas Rossiawan Hendra Putra; Brahma Ratih Rahayu Fakhrunnia; Wahyu Tri Wahono; Mohammad Muallif
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.v8i2.16982

Abstract

Driver fatigue is a significant contributor to road traffic accidents, motivating the development of non-invasive vision-based monitoring systems. This study evaluates a single-stage YOLOv8n-Pose approach for fatigue detection based on multiple behavioral indicators, including eye closure, a hand-at-mouth gesture as a yawning-related cue, and head tilt. Unlike conventional multi-stage approaches that require separate face detection and facial landmark extraction, the proposed approach jointly detects the facial region and estimates eight task-specific keypoints within a single inference process. The model was trained on 2,625 images representing variations in lighting conditions, subjects, accessories, and camera distance. Performance was evaluated using bounding-box and keypoint detection metrics, followed by live testing under different head orientations and camera distances. The model achieved a bounding-box mAP50 of 0.967 and a keypoint mAP50 of 0.817, with F1-scores of 0.90 for Eyes Open, 0.94 for Eyes Closed, and 0.97 for Hand-at-Mouth detection. Head-angle testing showed a monotonic decrease in ear-to-shoulder keypoint distance as the head tilted from the upright position, supporting its use as a head-tilt indicator within the tested conditions. Live testing identified an effective camera distance of 60–100 cm and an average processing rate of approximately 3.3–3.5 FPS. Error analysis identified backlighting as a source of incorrect eye-state predictions and hand occlusion as a cause of missed Hand-at-Mouth detections. These results demonstrate the potential of a single-stage pose-based approach for multi-indicator vision-based fatigue detection while highlighting the need for improved lighting robustness, diverse hand-pose training data, and higher inference efficiency.
Design of a Solar Charging Regulator Based on a Buck Converter that Integrates P&O PI MPPT Control and SOC Estimation Based on Coulomb Counting Wildan Surya Wijaya; Aditya Ilham; Dwi Hastuti; Maharani Wisudawati Swista; Alfiandi Aulia Rahmadani
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.v8i2.17162

Abstract

Changes in irradiation conditions cause photovoltaic (PV) power to be fluctuating, so the operating point of the PV is not always at the maximum power point. On the other hand, the battery charging process needs to be controlled so that it can be stopped when the battery reaches a full condition. This condition indicates the need for a control system that not only tracks the maximum PV power but also adjusts the charging process based on the battery's condition. This research develops a PV-based battery charging system using a buck converter that integrates Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT), Proportional-Integral (PI) controller, and Coulomb Counting-based State of Charge (SOC) estimation. The P&O algorithm is used to determine the PV voltage reference, then the PI Controller adjusts the duty cycle of the buck converter so that the operating point follows this reference. Meanwhile, Coulomb Counting is used to monitor SOC and control the cut-off and restart charging mechanisms. The simulation results show that the system is capable of adjusting the PV operating point to changes in source conditions and maintaining the charging process under normal conditions. The battery SOC reaches 100% at around 3.45 s, followed by a decrease in duty cycle from about 0.5 to 0 and charging current to 0 A. These results show that the integration of MPPT, buck converter, and SOC-based supervision is capable of controlling the charging process according to PV operating conditions and the battery's SOC limits.
Enhancing User Experience in a Land Transportation Information Application: A Design Thinking Approach to the GOBIS Application Lisana Lisana; Melissa Angga; Eugenia Caitlyn Cristabella
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.v8i2.16994

Abstract

GOBIS is a digital transportation application developed by the Surabaya City Government to facilitate public access to public transportation services. As a land transportation information system, GOBIS plays an important role in providing route, bus stop, and fleet information for urban mobility. However, user reviews and usability test results indicate that the current version of the application still has several shortcomings, particularly in interface design and user experience. The main issues include a difficult-to-interpret integrated map, the absence of an automatic route-search feature, and unclear information about bus stops and fleets. This study developed a redesigned GOBIS prototype through the five-stage Design Thinking process and evaluated the existing application and the redesigned prototype using the same task scenarios. Effectiveness was measured by task completion rate, efficiency by time-based efficiency, and perceived user experience by the User Experience Questionnaire (UEQ). The redesigned prototype increased task completion from 52% to 92%, representing a 40-percentage-point improvement, and increased time-based efficiency from 0.008 to 0.104 goals/sec. User satisfaction also improved after the redesign. Furthermore, assessment with the User Experience Questionnaire (UEQ) showed that all dimensions fell within the “excellent” category. These findings indicate that the redesigned interface successfully improves the overall quality of user interaction and enhances the user experience of GOBIS as a land transportation information system.
Explainable Entropy-TOPSIS for Digital Infrastructure Prioritization Across Indonesian Island Groups Elta Sonalitha; Bondhan Rio Prambanan; Rahman Arifuddin; Fajar Maulid Zidane
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.v8i2.16803

Abstract

Indonesia’s national internet penetration has continued to increase, reaching 80.66% in 2025, equivalent to 229,428,417 connected people from a total population of 284,438,900. However, this national achievement does not fully represent regional equality in digital infrastructure readiness. This study proposes an Explainable Entropy-TOPSIS framework to develop a Regional Digital Infrastructure Priority Index (RDIPI) across six Indonesian island groups using APJII 2025 indicators. The model transforms internet penetration, regional contribution, and ISP distribution into three priority-oriented criteria: access gap, ISP supply gap, and demand-supply shortage. The entropy weighting results show that demand-supply shortage is the dominant criterion with a weight of 0.8192, followed by ISP supply gap = 0.1292 and access gap = 0.0517. The TOPSIS results indicate that Sumatra has the highest infrastructure priority with an RDIPI score of 0.9840, followed by Maluku-Papua = 0.5226, Sulawesi = 0.3699, Kalimantan = 0.2422, Bali-Nusa Tenggara = 0.2013, and Java = 0.0000. These findings suggest that digital infrastructure priority should not be determined only by low penetration, but also by the imbalance between internet-user contribution and ISP representation. The proposed framework provides an explainable decision-support approach for APJII, ISPs, and policymakers in designing targeted broadband expansion and digital inclusion strategies.
Development and Field Evaluation of a Solar-Powered LoRa-Based Air Quality Monitoring and Early-Warning System for a Sand-Mining Area Alamsyah -; Aidynal Mustari; Moh. Ikro Fajar Rahman; Mohammed Ikhlayel
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.v8i2.17080

Abstract

Air pollution generated by sand-mining activities can degrade environmental quality and increase public-health risks, particularly in locations lacking continuous air-quality monitoring infrastructure. This study presents the development and field evaluation of a solar-powered LoRa-based air-quality monitoring and early-warning system for the Watusampu sand-mining area in Palu City, Indonesia. The main contribution of the proposed system is the integration of autonomous solar power, long-range wireless communication, multi-parameter sensing, real-time visualization, local backup storage, and ISPU-based warning functions into a single deployable platform. The transmitter node combines ZH03B, MiCS-5524, and DHT22 sensors with an ESP32 and RFM95W LoRa module to measure PM₁, PM₂.₅, PM₁₀, carbon monoxide, temperature, and relative humidity. At the receiver node, the acquired data are processed, stored on a microSD card, uploaded to a web database, and displayed through an LCD and real-time dashboard. Air-quality status is classified using Indonesia’s Air Pollutant Standard Index (ISPU). Sensor performance was evaluated by comparison with a reference air-quality detector, while LoRa communication was tested under Line-of-Sight (LoS) and Non-Line-of-Sight (Non-LoS) conditions. The ZH03B sensor produced average errors of 6.61%, 10.58%, and 8.67% for PM₁, PM₂.₅, and PM₁₀, respectively, while the average errors for CO, temperature, and relative humidity were 18.48%, 0.87%, and 0.61%. LoRa communication achieved 0% packet loss up to 1.2 km under LoS conditions and up to 20 m under Non-LoS conditions. These findings indicate that the proposed platform is feasible for autonomous, real-time air-quality monitoring and early warning in sand-mining areas with limited infrastructure.
Sesame Oil with Butylated Hydroxyanisole as a Candidate Natural Ester for Transformer Insulation Yenni Afrida; Carla Maretha; Fitriono Fitriono
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.v8i2.17036

Abstract

This study evaluates sesame oil modified with butylated hydroxyanisole (BHA) as a candidate eco-friendly transformer insulating liquid. Ten formulations were prepared using 500 mL sesame oil with 1-10 g BHA. Viscosity was measured using a rotational viscotester, and the laboratory-reported kinematic values were treated as estimates derived from the direct viscosity readings. Alternating-current breakdown voltage (BDV) was measured five times for each formulation, and differences among BHA loadings were evaluated using one-way analysis of variance. The estimated kinematic viscosity ranged from 33.33 to 42.59 cSt and generally decreased as BHA loading increased. The highest mean BDV was obtained at 3 g BHA/500 mL (30.24 +/- 7.94 kV), whereas the 10 g formulation showed the lowest estimated viscosity (33.33 cSt) and the second-highest BDV (25.58 +/- 3.38 kV). BDV exhibited a non-monotonic response to BHA loading. One-way ANOVA indicated significant differences among formulations (F(9,40)=7.98, p<0.001). The results identify 3 g BHA/500 mL as the most promising formulation in the current screening because it combined the highest dielectric performance with moderate viscosity. Further validation with BHA-free sesame oil, mineral-oil controls, independent formulation batches, measured density, moisture, acidity, and direct kinematic-viscosity testing is required before transformer application.
Multi-Source Data Integration for Photovoltaic Power Forecasting in Tropical Indonesia Rahma Fitria Ariani; Machmud Effendy; Yepy Komaril Sofi&#039;i
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.v8i2.17149

Abstract

The variability of photovoltaic (PV) power under tropical weather conditions complicates one-hour-ahead forecasting and operational energy planning. This study evaluates deep learning-based PV power forecasting by integrating historical PV output from a university in Malang, Indonesia with NASA POWER, Solcast, BMKG, and time features. Five data scenarios were assessed using Persistence, LSTM, and GRU, while hybrid architectures and source- and architecture-level ablation tests were evaluated on the complete multi-source configuration. Timestamp continuity was explicitly audited, and 178 holdout sequences that crossed temporal gaps were removed. GRU with the complete configuration achieved the lowest baseline RMSE of 0.218839 kW, with MAE of 0.110686 kW, nRMSE of 6.230669%, and R² of 0.955550. Relative to Persistence, RMSE and MAE decreased by 49.60% and 54.36%, respectively. LSTM with the Solcast scenario produced a nearly identical RMSE of 0.218992 kW, indicating that the benefit of additional data sources was architecture-dependent and non-monotonic. CNN-LSTM was the best hybrid model but remained 3.26% worse in RMSE than GRU, while the tested attention mechanism increased RMSE by 10.02%. Four-fold limited walk-forward validation supported GRU with the lowest mean RMSE of 0.232213 ± 0.019500 kW. The final forecasts were also translated into conceptual Energy Management System decision-support recommendations. Because same-day BMKG daily summaries were used in the main configuration, the findings are primarily interpreted as a retrospective evaluation.
Energy Efficiency and Performance of PMSM Drives in Electric Vehicles under Variable Loads I Ketut Wiryajati; I Made Mara; I Chatur Adhi Wirya Aryadi; Ida Ayu Sri Adnyani
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.v8i2.16859

Abstract

This study evaluates the performance and energy efficiency of an electric vehicle powertrain based on a Permanent Magnet Synchronous Motor under varying speed and load conditions using a real-time monitoring system. Experiments were conducted at speeds of 10–40 km/h and loads of 50–200 kg, measuring voltage, current, power, efficiency, and specific energy consumption. Results show that efficiency increases significantly from approximately 66% at low speeds to over 90% at higher speeds, while specific energy consumption decreases from 118 Wh/km to around 60 Wh/km, indicating improved energy utilization. Load variation has a relatively minor effect on efficiency but contributes to higher current and power demand. Efficiency gradient analysis reveals a reduced rate of improvement at higher speeds, suggesting saturation effects and the presence of an optimal operating region. Measurement validation confirms low error levels (<5%), ensuring data reliability. Overall, the system demonstrates optimal performance at medium to high speeds with significantly enhanced energy efficiency.
Neuro-Fuzzy Green-Time Allocation for Oversaturated Four-Way Signalized Intersections Ifan Wiranto; Yuliyanti Kadir
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.v8i2.17057

Abstract

Fixed-time traffic signals allocate a constant green duration regardless of how vehicle demand fluctuates across approaches. That rigidity becomes critical once an approach enters oversaturation, that is, once its degree of saturation x, the ratio of the arrival rate per cycle to the capacity the signal makes available per cycle, reaches or exceeds unity, so that a residual queue is carried over from one cycle to the next and successive residuals accumulate. This study designs and simulates an adaptive green-time allocation controller based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) with two inputs, aggregate phase queue length (Q) and maximum approach waiting time (W), for a four-way intersection operated from undersaturated to oversaturated conditions. The training data are not arbitrary: they are generated from a queue-actuated green-time formula whose three terms, queue discharge time, startup lost time and an anti-starvation priority proportional to normalized waiting time, are individually grounded in signal-timing theory, and whose parameters are reported in full. The model, using three Gaussian membership functions per input, nine first-order Sugeno rules and hybrid learning, was trained and validated on an 80:20 split of 180 data pairs and reached a testing RMSE of 2.39 s and an MAE of 1.87 s, that is, 2.1% of the 115-second output range. Performance was then evaluated over a 100-cycle discrete-event simulation with Poisson arrivals against fixed-time control at a constant 30 s green, using an identical queue-update mechanism. Under the baseline plan the effective capacity is 7 vehicles per approach per cycle, and the three scenarios in which at least one approach exceeds it (x = 1.14) are exactly the scenarios in which the fixed-time queue grows linearly with a strictly positive drift and in which the proposed controller yields improvements of 68.6% to 87.1%. In the four scenarios in which no approach exceeds capacity the improvement is 0.8% to 10.0%. The benefit of neuro-fuzzy green-time allocation is therefore concentrated in, and structurally explained by, the oversaturated regime.

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