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Jurnal EECCIS
Published by Universitas Brawijaya
ISSN : 19783345     EISSN : 24608122     DOI : -
Core Subject : Engineering,
Engineering
EECCIS is a scientific journal published every six month by electrical Department faculty of Engineering Brawijaya University. The Journal itself is specialized, i.e. the topics of articles cover electrical power, electronics, control, telecommunication, informatics and system engineering. The languages used in this journal are Bahasa Indonesia and English.
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Articles 9 Documents
 1 
Search results for , issue "Vol. 20 No. 1 (2026)" : 9 Documents clear
Biogas Digester Monitoring System Using Machine Learning Classification Junus, Mochammad; Nuraini Putri Utami, Muslimah; Bin Abdullah, Mohd Noor
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1832

Abstract

Abstract— The problem faced in the biogas fermentation process is the challenge of continuously monitoring environmental conditions such as temperature, humidity, methane gas (CH?) concentration, and pressure, which have a major effect on gas production efficiency. This research aims to design a biogas fermentation monitoring system that uses Internet of Things (IoT) technology so that it can automatically classify fermentation conditions with the help of the K-Means Clustering algorithm. The system utilizes ESP32 microcontroller connected with DHT22 and MQ-4 sensors to measure temperature, humidity, and CH? parameters, and sends the data directly to Blynk platform via WiFi connection. The data collection process was carried out every five hours for 15 days after the initial fermentation lasted for three weeks. The resulting data was then analyzed using the K-Means algorithm to classify fermentation conditions into three categories: early, transitional, and active. Evaluation results using the Elbow and Silhouette Score methods indicated that the ideal number of clusters was three (K=3), with most of the data belonging to the active cluster. The 3D representation and scatter diagram confirmed that each cluster had significantly different sensor characteristics. The system successfully facilitated the monitoring of the fermentation process and provided important classification information to support decision-making. This research shows that combining IoT and machine learning can improve the efficiency of biogas fermentation management.
Metamaterial Layer Loaded on Log Periodic Antenna for Energy Harvesting Levy Olivia Nur; Quzwain, Kamelia; Wahid Nur Hamid; Aisyah Novfitri; Mudrik Alaydrus
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1840

Abstract

This paper investigates metamaterial layer loaded on a log periodic antenna for Radio Frequency (RF) energy harvesting. The proposed antenna was designed at a frequency of 5.8 GHz using Rogers RT5880 substrate with a thickness of 0.787 mm. The log periodic antenna covered with a metamaterial layer was simulated and analyzed. The computational results show a good agreement in which the proposed antenna is able to produce -13.5 dB return loss and 8.3 dB gain with a compact size of 40 x 50. It implies that the proposed concept in this paper can be implemented to increase the antenna performance.
Design and Development of a Motorized Electronic Valve as a Flow Control Actuator Based on the ESP32 Microcontroller Nurkamila, Hasna Azkiya; Ridwan; Nur Jamiludin Ramadhan
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1882

Abstract

Electric actuators have an important role in automatic control systems by replacing manual mechanisms, therefore increasing efficiency through automation. Among the most expensive components in control systems are control valves. In this study, an electronically motorized valve was designed to respond to three types of industrial analog signals. The valve was developed using an ESP32 microcontroller to optimize the performance of the DC motor. The researched methodology followed the VDI 2206 model, which provides a systematic and structured approach to product development through modeling, analysis, and verification. The system is designed to integrate with PLC control using voltage and current signals as inputs. Validation results show that the system achieves multi-input capability with highly linear and accurate position control (R² > 0.99; MAPE 5.96%). Although the functional response to flow rate is also consistent (R² > 0.96), its accuracy is lower (MAPE 24.54%) due to systematic errors primarily caused by mechanical factors such as valve friction and pump pressure.
Optimizing the Initial Current Control of a Single-Phase Induction Motor Using the Arduino Nano Soft Start Module Nisa', Silvi Nur Rakhman; Ibrohim; Rijanto, Tri; Haryudo, Subuh Isnur
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1884

Abstract

Single-phase induction motors are widely used in domestic and industrial applications, but they have the main disadvantage of significant initial current surges, which have the potential to damage components and degrade power quality. This research proposes a solution through the design and implementation of an Arduino Nano-based soft start automation system to overcome these surges. The developed system uses Arduino Nano as the main microcontroller integrated with the PZEM-004T sensor for real-time monitoring of electrical parameters, as well as relays as an efficient switching mechanism. The method applied is the manipulation of the value of the resistor to limit the initial current, with the aim of measuring the effectiveness of the system in reducing the current surge. Focusing on testing a fully functioning system, the study is expected to prove a drastic decrease in initial current spikes. The results of this study can show that the Arduino Nano-based soft start automation system is effective in improving the performance and efficiency of induction motors, as well as being a practical guide for similar applications in the industry.
The Hybrid PV–Micro-Hydro Power System with Automatic ECS in Semi Off-Grid Scheme Hidayat, Saepul Hidayat; Sucita, Tasma; Washimudin Surya Saputra
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1888

Abstract

This study investigated the design and implementation of a hybrid photovoltaic (PV)–micro-hydro power system equipped with an automatic Energy Control System (ECS) in a semi off-grid configuration. The research addressed the challenge of providing reliable and continuous electricity in rural and remote areas where access to the national grid was limited or unreliable. The proposed system combined two 550 Wp monocrystalline PV panels and thirty-two pipeline micro-hydro generators with a total output of approximately +550 VA, supported by a 25.6 V 200 Ah LiFePO? battery bank and a 2000 W pure sine wave inverter. The ECS was designed to manage seamless switching between power sources, regulate charging processes, and ensure uninterrupted electricity supply to household loads. The results showed that the hybrid system generated an average of 12.48 kWh per day, meeting the typical household demand in Indonesia. Economic analysis indicated that monthly electricity costs were reduced by more than 85% compared to full reliance on the national grid. Furthermore, the system maintained 24-hour electricity availability by utilizing solar energy during the day, micro-hydro power during water usage peaks, and battery storage during off-peak periods. Overall, the hybrid PV–micro-hydro system with ECS proved to be a cost-effective, reliable, and sustainable solution for household-scale energy supply in semi off-grid areas.
Design and Development of a Bearing Fault Detection System Using CNN Karimul Afdlol, Ilham; Muhammad Aswin; Raden Arief Setyawan
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1890

Abstract

Bearings are a crucial mechanical component in machinery systems, they function to support and reduce friction between moving parts. Damage to bearings can lead to decreased machine efficiency and further damage to other components. This study aims to develop a bearing fault detection system using a Deep Learning approach. The dataset used was obtained from acoustic signal recordings of induction motor bearings under three different conditions: good condition, slightly damaged condition, and severely damaged condition. The signals are processed using the Short Time Fourier Transformation method to represent them as two-dimensional time-frequency-based spectrograms. The bearing conditions are classified by a Convolutional Neural Network model based on these spectrograms. The architecture used in this study consists of several convolutional and pooling layers for feature extraction and classification. The model training uses a dataset that has been split between training data and validation data. The training results show that the model can achieve a validation accuracy of up to 99%, with stable performance and no indication of overfitting or underfitting. The accuracy value reaches 0.99, with a precision of 1.00, recall of 1.00, and an F1-score of 1.00. The macro and weighted averages, each valued at 0.99, indicate that the model performs excellently across all classes. This study proves that the STFT and CNN methods are effective in detecting and classifying bearing faults using acoustic signals. This system has the potential to be implemented in industry as an efficient tool for preventive maintenance compared to conventional methods that rely on human hearing.
Implementation of YOLOv5s for Automatic Waste Category Classification in Digital Waste Bank Systems Rinanto, Noorman; Mat Syai’in; Agus Khumaidi; Muhammad Khoirul Hasin; Lilik Subiyanto; Vivin Setiani; Firstama Yusuf Noor; Harun Ismail
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1894

Abstract

The increasing volume of organic waste in campuses or households demands innovative solutions for waste management and classification. This study proposes an automated classification system based on deep learning using the YOLOv5s algorithm to detect 14 categories of inorganic waste in real-time. The dataset consists of over 3.500 labeled images, annotated via Makesense.ai and augmented using Roboflow. The model was trained on Google Collaboratory for 100 epochs using the YOLOv5s architecture and evaluated based on precision, recall, F1-score, and mean Average Precision (mAP). Training result show mAP@0.5 approaching 100% and mAP@0.5:0.95 around 85%, with an average confidence score of 88.30% during real-time testing using a webcam. These findings demonstrate that YOLOv5s can accurately and efficiently classify waste objects, offering strong potential for integration into digital waste bank systems to enhance the efficiency and transparency of waste management processes.
Design of a Decoupling Control System for pH and TDS in Hydroponics Putri, Ina Rahmawati; Siswojo, Bambang; Rusli, Mochammad
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1899

Abstract

Hydroponics was a sustainable cultivation method that optimized land and water use efficiency. The stability of the nutrient solution in this system was strongly influenced by pH and Total Dissolved Solids (TDS) parameters, which were closely interrelated. Changes in pH values could affect TDS concentration, and vice versa, creating coupled dynamics that complicated precise control. This study applied a decoupling control strategy that separated interaction effects so that pH and TDS could be controlled more independently. The system was developed using a pH sensor, a TDS sensor, an Arduino Uno microcontroller, and three peristaltic pumps for adding pH up, pH down, and nutrient solutions. The results showed that the decoupling control system in simulation-maintained pH stability with a rise time of 6.04 s, a settling time of 31.04 s, an overshoot of 9.59%, and a steady-state error of 0, while TDS achieved a rise time of 84.79 s, a settling time of 161.45 s, and a steady-state error of 0.0005%. The hardware implementation demonstrated similar performance with a pH rise time of 42.62 s, a settling time of 46 s, an overshoot of 10%, a steady-state error of 0.68%, and TDS with a rise time of 62.35 s, a settling time of 91 s, an overshoot of 2.40%, and a steady-state error of 0.72%. This study proved that the decoupling method provided more optimal nutrient control performance in hydroponic systems.
Metaheuristic-Driven Stabilization: Multi-Objective Optimization for UAV Camera Gimbal Systems Putro, Nur Achmad Sulistyo; Priyambodo, Tri Kuntoro; Dharmawan, Andi; Perwira, Zandy Yudha
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1898

Abstract

Stabilizing a camera mounted on an unmanned aerial vehicle (UAV) is critical for obtaining high-quality aerial imagery, particularly under dynamic disturbances and rapid maneuvers. Conventional proportional-integral-derivative (PID) controllers are widely employed in gimbal systems due to their simplicity, yet their performance strongly depends on precise parameter tuning. Classical methods, such as Ziegler–Nichols, often result in excessive overshoot and slow settling time, which are unsuitable for high-precision applications. This study introduces a metaheuristic-driven approach based on Ant Colony Optimization (ACO) integrated with a multi-objective cost function to tune PID parameters for a three-axis UAV camera gimbal system. The cost function simultaneously considers rise time, settling time, overshoot, and steady-state error, providing a balanced performance across all dynamic response metrics. A dynamic model of the gimbal actuators was developed, and simulations were performed in MATLAB using identical initial conditions derived from Ziegler–Nichols tuning. Comparative experiments demonstrate that the proposed method significantly outperforms classical tuning, reducing overshoot from 47.6% to 0% and improving settling time from 4.54 s to 1.28 s on the roll axis, with similar improvements on pitch and yaw. These results highlight the effectiveness of multi-objective ACO for high-precision stabilization, offering a promising direction for real-time UAV control systems.

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