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JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA)
Published by Universitas Muhammadiyah Sidoarjo
ISSN : 25408658     EISSN : 25408658     DOI : https://doi.org/10.21070/jeeeu
Core Subject : Engineering,
Electrical & Electronics Engineering
Aim: to facilitate scholar, researchers, and teachers for publishing the original articles of review articles. Scope: Electrical, Electronica, Telecomunication, Medical Electronica, Digital system, Control system.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 5 Documents
 1 
Search results for , issue "Vol. 10 No. 1 (2026): April" : 5 Documents clear
Design and Construction of an Automatic Body Weighing Scale for Classification of Pencak Silat Athlete Classes Using the Decision Tree Method Saputro, adi; faswiaf, monika; romanjavaters; rahmawati, diana; ibadillah, fiqhi; hardiwansyah, muttaqin
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol. 10 No. 1 (2026): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v10i1.1707

Abstract

General Background: Automated athlete measurement systems are increasingly required in combat sports to support accurate classification, efficient data management, and competition validation processes. Specific Background: Conventional weighing procedures in pencak silat competitions still rely on manual measurements and independent weighing devices without integrated classification or web-based recording systems, creating risks of athlete misclassification and administrative difficulties. Knowledge Gap: Previous studies primarily focused on nutritional assessment systems using rule-based or z-score methods, while limited research has integrated automatic athlete classification, Body Mass Index (BMI) analysis, website integration, and decision tree algorithms in pencak silat competitions. Aims: This study aims to design and develop an automatic body weighing system for pencak silat athlete class classification using the decision tree method and integrated website monitoring. Results: The system utilized load cell sensors for body weight measurement and Time of Flight (ToF) sensors for height detection, while the ESP32 microcontroller processed classification and BMI calculations. Experimental results demonstrated an average error rate of 0.81% and success rate of 99.19% for body weight measurements, while height measurements achieved an average error rate of 1.52% and success rate of 98.48%. The decision tree classification results were consistent with manual calculations across athlete categories from pre-teen to adult levels. Novelty: The study integrates automatic athlete classification, BMI evaluation, sensor-based measurements, and website-based monitoring within a single decision tree framework. Implications: The proposed system supports accurate athlete verification, digital sports data management, and automated classification processes for pencak silat competitions.
Rancang Bangun Estimasi Daya Output Pada Photovoltaic Menggunakan Metode Fuzzy Anfis Sugeno riny sulistyowati; Sujono, Hari Agus; H., Gatot Basuki; Saputra, Ariyanto Dwi
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol. 10 No. 1 (2026): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v10i1.1733

Abstract

General Background: The increasing demand for electrical energy has accelerated the development of renewable energy systems, particularly photovoltaic (PV) technology, which requires reliable power estimation under varying environmental conditions. Specific Background: PV output power is strongly affected by environmental parameters such as light intensity, voltage, current, and temperature, making prediction difficult when nonlinear relationships occur among variables. Knowledge Gap: Conventional multi-regression approaches have limitations in modeling nonlinear PV characteristics, while comparative evaluations of Adaptive Neuro-Fuzzy Inference System (ANFIS) configurations for PV power estimation remain limited. Aims: This study aims to develop and evaluate an ANFIS Sugeno-based model for estimating PV output power and compare its performance with multi-regression methods using real-time environmental data collected through an Arduino-based data logger. Results: The developed data logger successfully recorded stable real-time data, while the ANFIS model demonstrated substantially lower prediction errors than multi-regression. The best-performing configuration, Gauss555, achieved Mean Absolute Percentage Error (MAPE) values of 2.03% for training data and 2.13% for testing data, whereas multi-regression produced errors of 54.63% and 79.19%, respectively. Gaussian membership functions consistently generated lower and more stable Absolute Percentage Error (APE) values than triangular and trapezoidal functions. Novelty: The study integrates real-time PV environmental monitoring with comparative ANFIS membership function configurations to identify the most suitable nonlinear prediction model for PV output estimation. Implications: The findings demonstrate that ANFIS provides a robust and accurate approach for photovoltaic power estimation, supporting reliable renewable energy management and future intelligent PV monitoring systems.
Genetic Algorithm-Based SVC Capacity Optimization for Voltage Stability in 500 kV Systems Winarno, Istiyo; Iradiratu Diah P K; Belly Yan Dewantara
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol. 10 No. 1 (2026): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v10i1.1737

Abstract

General Background: Voltage stability and reactive power management remain critical operational challenges in large-scale 500 kV transmission systems operating under high loading conditions. Specific Background: Static Var Compensators (SVCs) are widely applied to support voltage regulation; however, determining appropriate SVC capacity remains complex because inadequate sizing may reduce system performance and increase transmission losses. Knowledge Gap: Previous studies have frequently emphasized voltage improvement in simplified benchmark systems without comprehensively integrating realistic power flow simulation and transmission loss evaluation in large-scale networks. Aims: This study aims to determine the optimal SVC capacity using a Genetic Algorithm (GA) to improve voltage stability and reduce transmission losses in a 500 kV transmission system. Results: A quantitative simulation-based framework integrating ETAP load flow analysis and MATLAB-based GA optimization was implemented under steady-state operating conditions. The base-case scenario recorded a minimum voltage of 455.009 kV and total transmission losses of 607.32 MW. After applying optimized SVC capacities of 404.80 MVAr, 288.43 MVAr, and 175.94 MVAr at critical buses, voltage magnitudes increased by 0.98%–7.89%, while transmission losses decreased to 594.30 MW, representing a 13.02 MW reduction or 2.14% improvement. The convergence analysis confirmed stable optimization behavior within 100 generations. Novelty: The study introduces an integrated simulation–optimization framework combining ETAP and GA for practical SVC capacity determination in a realistic 500 kV transmission network. Implications: The findings provide a reproducible and practical reference for reactive power planning, voltage stability improvement, and transmission efficiency optimization in large-scale power systems.
Hybrid Fuzzy-PID for Temperature and Water Quality Control in Freshwater Lobster Farming Arifuddin, Rahman; Setiawan, Aries Boedi; Sumarahinsih, Andrijani; Sonalitha, Elta; Kartika Sari, Resi Dwi Jayanti; Iman Mufti, Akbarsyah Nur
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol. 10 No. 1 (2026): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v10i1.1742

Abstract

General Background: Freshwater lobster aquaculture requires stable temperature and water quality conditions to support optimal growth and survival. Specific Background: Conventional and semi-automatic aquaculture control systems often experience limitations in responding to dynamic environmental changes, particularly in maintaining stable temperature, pH, and dissolved oxygen (DO) conditions in freshwater lobster cultivation. Knowledge Gap: Existing aquaculture studies predominantly focus on sensor monitoring and data transmission, while limited research has developed adaptive automatic control systems integrating fuzzy logic and PID control for simultaneous temperature and water quality stabilization in freshwater lobster farming. Aims: This study aims to design, implement, and evaluate a hybrid fuzzy-PID control system for regulating temperature and water quality in freshwater lobster (Cherax quadricarinatus) cultivation media. Results: The system utilized temperature, pH, and DO sensors, a microcontroller-based processing unit, and actuators consisting of a heater, aerator, and circulation pump. At a temperature setpoint of 28°C, the hybrid fuzzy-PID achieved a rise time of 8.7 minutes, settling time of 14.2 minutes, overshoot of 0.36%, and steady-state error of 0.03°C, outperforming conventional PID and uncontrolled systems. The hybrid approach also produced lower MAE and RMSE values of 0.393 and 0.660, respectively. Water quality evaluation showed more stable pH and DO conditions, with pH reaching 7.39 and DO reaching 6.47 mg/L. Novelty: This study integrates adaptive fuzzy inference and PID parameter adjustment within a closed-loop aquaculture control framework for simultaneous temperature and water quality management. Implications: The proposed system supports adaptive aquaculture automation and stable freshwater lobster cultivation environments for modern aquaculture applications.
A Novel Fuzzy-COVID Optimization Algorithm for Enhancing Energy Efficiency and Stability in Renewable Smart Grids Abidin, Zainal; Amal, Andi Syaiful
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol. 10 No. 1 (2026): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v10i1.1745

Abstract

Latar Belakang Umum Integrasi berbagai sumber energi terbarukan ke dalam jaringan cerdas membutuhkan strategi kontrol canggih untuk mempertahankan operasi yang efisien dan stabil dalam kondisi dinamis. Latar Belakang Spesifik Jaringan cerdas hibrida yang menggabungkan sistem fotovoltaik, turbin angin, sel bahan bakar, dan mikrohidro dengan penyimpanan baterai menghadirkan tantangan dalam pengaturan frekuensi dan tegangan karena variabilitas beban dan ketidakpastian sumber daya. Kesenjangan Pengetahuan Pendekatan optimasi konvensional seperti algoritma genetik dan optimasi swarm partikel menghadapi keterbatasan dalam mencapai kontrol adaptif dan penyetelan parameter global secara bersamaan. Tujuan Studi ini mengusulkan pendekatan kontrol hibrida baru menggunakan Pengontrol Logika Fuzzy yang terintegrasi dengan Algoritma Optimasi COVID untuk mengatasi tantangan ini. Hasil Hasil simulasi di MATLAB/Simulink menunjukkan peningkatan 15–20% dalam stabilitas sistem dan 12% dalam efisiensi penyimpanan energi, bersamaan dengan pengurangan deviasi frekuensi (±0,18 Hz), fluktuasi tegangan yang diminimalkan (±0,03 pu), dan peningkatan manajemen status pengisian baterai dibandingkan dengan metode konvensional. Kebaruan Integrasi kontrol logika fuzzy dengan Algoritma Optimasi COVID memperkenalkan kerangka kerja optimasi baru untuk penyetelan parameter adaptif dalam sistem energi terbarukan hibrida. Implikasi Metode yang diusulkan mendukung pengembangan sistem jaringan cerdas yang andal dan fleksibel, berkontribusi pada manajemen energi berkelanjutan dan peningkatan kinerja operasional dalam jaringan listrik berbasis energi terbarukan.

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