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Emitor: Jurnal Teknik Elektro
Published by Universitas Muhammadiyah Surakarta
ISSN : 14118890     EISSN : 25414518     DOI : https://doi.org/10.23917/emitor
Core Subject : Engineering,
Engineering
Emitor: Jurnal Teknik Elektro merupakan jurnal ilmiah yang diterbitkan oleh Jurusan Teknik Elektro Fakultas Teknik Universitas Muhammadiyah Surakarta dengan tujuan sebagai media publikasi ilmiah di bidang ke-teknik elektro-an yang meliputi bidang Sistem Tenaga Listrik (STL), Sistem Isyarat dan Elektronika (SIE) yang meliputi Elektronika, Telekomunikasi, Komputasi, Kontrol, Instrumentasi, Elektronika Medis (biomedika) dan Sistem Komputer dan Informatika (SKI).
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 101 Documents
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Design and Implementation of a Microcontroller-Based Air Filter System with Android Application Interface Muhammad, Kusban; Ramadhan Putra, Rafigo; Purnomo, Eko
Emitor: Jurnal Teknik Elektro Vol 26, No 1: March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i1.16143

Abstract

Indoor air quality is a crucial factor influencing occupants’ health and comfort. Various issues, including dust particles, excessive humidity, hazardous gases, and inadequate lighting conditions, can significantly reduce indoor air quality. However, most existing air filter systems still lack real-time monitoring and remote control capabilities. Therefore, this study aims to develop a microcontroller-based air filter system using ESP32 integrated with an Android application via Wi-Fi connectivity. The proposed system utilizes a DHT22 sensor to measure temperature and humidity, a GP2Y1010AU0F sensor to detect dust particles, an MQ-135 sensor to detect harmful gases and unpleasant odors, and a BH1750 sensor to measure light intensity. All sensor data are processed by the ESP32 microcontroller and displayed through an Android application developed using Android Studio, allowing users to monitor indoor air quality conditions in real time. System testing was conducted under several indoor air quality scenarios, including variations in humidity and dust concentration levels. The results demonstrate that the system operates accurately, responsively, and in real time in monitoring indoor air quality. Furthermore, the system can be remotely controlled through the Android application. This system is expected to improve indoor air quality while providing positive impacts on environmental health and comfort.
Automatic Portal Design Using Ultra High Frequency - RFID Fashiha Ilman, Abdillah; Jauhari, Moh.; Sohibul Hajah, M.; Nur, Mohammad; Basya Shahrys Tsany, Rahmat; Dzulkiflih, Dzulkiflih
Emitor: Jurnal Teknik Elektro Vol 26, No 1: March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i1.16274

Abstract

The development of science and technology has experienced a very high acceleration. Along with this, the need for and demand for fast and accurate information is also getting higher. Based on this, this research aims to create an automatic portal model using UHF RFID. In addition, this model will have digital data documenting entry and exit of vehicles, so that it is easier in the process of monitoring data on students, lecturers and guests in carrying out activities within the Madura State Polytechnic (POLTERA) campus. To help security officers, and prevent transmission of Covid-19, an automatic entry and exit control system is needed. The purpose of this research is to design and build an automatic portal with RFID that has Ultra High Frequency (UHF) technology and additional driver body temperature detection technology. The temperature value and the RFID code are read by the RFID Reader and then the code is used as a condition for access to open and close the portal. After that, the recorded data in the form of RFID card data, temperature data, and driver images will be stored in a microSD data logger.
Smart Irrigation System Prototype for Chili Plants with Voice Control Using Wit.ai Based on NodeMCU 8266 Muhammad Haekal; Raden Supriyanto
Emitor: Jurnal Teknik Elektro Vol 26, No 2: July 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i2.17100

Abstract

Abstract— Traditional irrigation practices for chili cultivation often lead to inefficient water usage and inconsistent scheduling, adversely affecting crop yields and sustainability. To address these limitations, this study proposes a smart irrigation system prototype utilizing the NodeMCU ESP8266 microcontroller integrated with both environmental sensing and dual-mode voice control. The system operates in two modes: (1) automatic, based on real-time sensor inputs from soil moisture, rainfall, and water level detectors; and (2) manual, through voice commands processed via the Wit.ai API or offline triggers using the KY-037 high-sensitivity sound sensor. The ESP8266 serves as the core controller, executing irrigation logic and relay-based pump activation programmed through the Arduino IDE. Experimental testing demonstrated a 95% accuracy rate in voice command recognition and consistent sensor performance aligned with predefined irrigation thresholds. This dual-control approach ensures operational flexibility under varying connectivity conditions, making it well-suited for small to medium-scale agriculture, particularly in rural environments with intermittent internet access. The system represents an original contribution to the integration of IoT and natural language processing in precision agriculture, with potential for scalable implementation.
Viability Using Multimodal Integration of Bioimpedance Spectroscopy and Spectrophotometry Afrida Firdausi; Wahyu Sugianto; Argawi Kandito
Emitor: Jurnal Teknik Elektro Vol 26, No 2: July 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i2.17220

Abstract

Bacterial viability monitoring is generally carried out using the Total Plate Count (TPC) method, but this method requires an incubation time of 24–48 hours, making it less efficient for rapid detection. This study aims to develop a method for estimating the number of viable bacteria based on the integration of bioimpedance and absorbance. Cell lysis was induced using varying doses of alcohol, bioimpedance measurements were performed using the BioSMET system at a frequency range of 20 Hz–100 kHz and absorbance measurements at a wavelength of 260 nm. The analysis results showed that a frequency of 70 kHz had the highest correlation with bacterial viability and was selected as the optimal frequency. Integration of both parameters using the Multiple Linear Regression (MLR) model increased the prediction accuracy with a value and RMSE of 0.4156. These results indicate that the multimodal approach is able to provide more accurate and faster estimates compared to the conventional TPC method.
Analysis of SVD-Based Image Compression for Efficient Deep Learning in Face Mask Classification Eric Sean Kesuma; Ali Zainal Abidin; Bhakti Yudho Suprapto; Suci Dwijayanti; Baginda Oloan Siregar
Emitor: Jurnal Teknik Elektro Vol 26, No 2: July 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i2.17629

Abstract

This study investigates the use of Singular Value Decomposition (SVD) as an image compression technique to improve the efficiency of deep learning models for face mask detection. The proposed approach applies SVD-based compression with different values of k (k = 10, 30, 50) prior to training a MobileNetV2 model. Experimental results show that SVD-based compression can significantly reduce computational cost while maintaining high classification performance. The model trained with k = 50 achieves the highest accuracy of 99.43%, slightly outperforming the model trained on the original dataset. In addition, compressed datasets require less training time, with the fastest configuration (k = 30) achieving a substantial reduction in training duration. The results also indicate that moderate compression levels provide an optimal balance between efficiency and accuracy, while excessive compression leads to performance degradation due to loss of important image features. Furthermore, the training process demonstrates faster convergence for compressed datasets, indicating improved learning efficiency. Overall, this study confirms that SVD-based image compression is an effective preprocessing technique for improving deep learning efficiency without significantly compromising classification accuracy.
Stator Design Analysis of Induction Motors to Improve Efficiency and Reduce Torque Ripple Using the Taguchi Method Ferdyanto; Muhammad Raihan Fadhlurrahman; Wildan Hakim; R. Danuprawira Harris; Mario Roal
Emitor: Jurnal Teknik Elektro Vol 26, No 2: July 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i2.17718

Abstract

Three-phase induction motors dominate industrial applications due to their robustness, simple construction, and low maintenance requirements; however, torque ripple and efficiency degradation remain critical issues affecting vibration, acoustic noise, mechanical stress, and overall energy consumption. Excessive torque ripple may reduce operational stability and shorten motor lifespan, while low efficiency increases electricity costs in continuous industrial processes. Conventional optimization approaches often address torque ripple or efficiency separately and usually require extensive computational effort with a large number of simulation iterations. This study proposes an integrated optimization framework combining Finite Element Analysis (FEA) and the Taguchi method to simultaneously reduce torque ripple and improve efficiency through systematic stator design modification. Six control factors are evaluated, including slot geometry parameters (hs0, hs2, bs1, bs2), stator core material, and air-gap length, arranged using an L25 orthogonal array to minimize simulation runs while maintaining statistical reliability. Electromagnetic simulations are performed using ANSYS Maxwell to obtain torque characteristics, magnetic behavior, power loss, and efficiency under rated operating conditions. Statistical evaluation through Analysis of Means (ANOM) and Analysis of Variance (ANOVA) identifies dominant parameters and determines the optimal design combination. Results indicate that stator core material contributes most significantly, accounting for 77.89% of torque ripple variation and 69.24% of efficiency variation, followed by slot width parameters and air-gap length. The optimized design reduces torque ripple from 17.27% to 17.13% and increases efficiency from 86.58% to 88.52%, while reducing total power losses by 2.25 kW. In addition, the optimized design provides smoother torque response and more stable operation during startup and steady-state conditions. The proposed approach demonstrates a computationally efficient and statistically robust method for industrial induction motor design, offering practical applicability for improving energy efficiency, reliability, and operational stability.
Implementation of Centralized IoT Safety Latching for Class-D Amplifiers: QoS Analysis and OTA Integration Gaguk Firasanto; Andriani Andriani; Donie Agus Ardianto; Zainin Widadi; Donal Karyano
Emitor: Jurnal Teknik Elektro Vol 26, No 2: July 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i2.17831

Abstract

Class-D audio power amplifiers deliver high operational efficiency but remain exceptionally vulnerable to catastrophic failures induced by thermal runaway and output overcurrent. While traditional passive protections and localized active systems isolate faults, they often lack centralized remote monitoring capabilities and seamless software maintainability. This research proposes a Centralized Internet of Things (IoT) protection system using an ESP32 microcontroller as a telemetry node, integrated with INA219 and LM35 precision sensors. Unlike decentralized systems, the core Safety Latching algorithm (OR logic) is processed centrally on a Python-based Graphical User Interface (GUI) server via the MQTT protocol. Furthermore, an Over-The-Air (OTA) firmware update mechanism is integrated for wireless software maintenance. Experimental results demonstrate high measurement accuracy, yielding average errors of less than 1% for temperature and 2% for current. The centralized safety latching algorithm operated robustly, successfully disconnecting the amplifier load at critical thresholds (75.1°C and 3.48 A) and effectively preventing destructive power oscillation by enforcing a manual reset state. The integration of OTA updates proved successful for remote firmware deployment without physical hardware intervention. In conclusion, the proposed centralized IoT framework significantly enhances the diagnostic reliability, safety, and long-term maintainability of high-power audio systems.
Simulation and Modeling CUK H-Bridge Current-Controlled Inverter Using a Proportional-Integral Algorithm Fahrul Indra; Eka Nuryanto Budisusila
Emitor: Jurnal Teknik Elektro Vol 26, No 2: July 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i2.17937

Abstract

The growing demand for renewable energy drives the need for efficient and reliable power conversion systems. This paper presents the simulation and modeling of a CUK H-Bridge Inverter with current control using a Proportional Integral (PI) algorithm implemented in Power Simulator (PSIM) software. The proposed topology combines an H-Bridge inverter with a CUK AC–AC converter, enabling bidirectional power flow, buck-boost voltage operation, and low Total Harmonic Distortion (THD) in the output current. The current control loop uses a PI controller tuned using the empirical Ziegler–Nichols (Z-N) method, followed by fine-tuning, resulting in optimal parameters of Kp = 0.6, Ti = 0.0167, and Ki = 35. Simulation results show that the actual output current (Iact) accurately follows the reference current (Iref) with minimal steady-state error. The topology operates in buck mode (Vo < Vin), steady-state mode (Vo ≈ Vin), and boost mode (Vo > Vin) depending on the given reference current value. A maximum output power of 2.25 kW is achieved at a reference current of 15 A with a maximum output voltage of 150 V. The measured output current THD is 3.6%, meeting the IEEE 519 standard. These results confirm the effectiveness of the PI-controlled CUK H-Bridge Inverter topology for renewable energy applications.
Hybrid Bayesian Optimization and Deep Reinforcement Learning for Enhanced MPPT in PV Systems Under Dynamic Conditions Firas Maulana Lasidi; Jangkung Raharjo; Basuki Rahmat; Andriani Andriani
Emitor: Jurnal Teknik Elektro Vol 26, No 2: July 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i2.18469

Abstract

Abstract − Conventional Maximum Power Point Tracking (MPPT) methods in photovoltaic (PV) systems frequently suffer from significant efficiency degradation when subjected to dynamic weather and Partial Shading Conditions (PSC). To address this issue, this study proposes a novel hybrid control algorithm integrating Bayesian Optimization (BO) and Deep Reinforcement Learning (DRL). The primary contribution of this research is the development of an adaptive MPPT system architecture that leverages the global exploration capabilities of BO alongside the high-precision local tuning of DRL to maximize solar energy extraction. The methodology evaluates the proposed BO-DRL agent through an ablation study within a Python simulation environment across four distinctive environmental profiles: uniform irradiance, light partial shading, heavy partial shading, and extreme dynamic conditions. In this framework, the BO component executes a probabilistic global search via Gaussian Processes to prevent the system from getting trapped in local maxima, while the DRL agent performs continuous duty cycle adjustments to minimize steady-state oscillations. Simulation results demonstrate that the hybrid approach significantly outperforms the conventional Perturb and Observe (P&O) method. Under heavy partial shading, the hybrid algorithm achieves a tracking efficiency of 96.08%, whereas the P&O method drops to 62.24% due to local peak entrapment. Under extreme dynamic scenarios, the hybrid efficiency remains robust at 93.22%, while the P&O performance drastically degrades to 39.07%. Furthermore, the ablation validation proves that standalone DRL agents fail to initialize optimally without the global search assistance from the BO unit. In conclusion, the synergistic integration of BO-DRL yields a highly robust, efficient, and adaptive MPPT control solution capable of optimizing PV energy harvesting in highly volatile environments.
IoT-Integrated Monitoring System for Environmental Parameters in a Rice Seedling Greenhouse Using ESP32 Jauharotul Maknunah; Rahajeng Kurnianingtyas
Emitor: Jurnal Teknik Elektro Vol 26, No 2: July 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i2.18490

Abstract

The application of Internet of Things (IoT) technology in modern agriculture has become increasingly important for improving efficiency, productivity, and environmental monitoring. This study presents the design and implementation of an ESP32-based IoT monitoring system for a rice seedling greenhouse. The proposed system was developed to monitor multiple environmental parameters simultaneously, including temperature, soil moisture, pH, and Total Dissolved Solids (TDS) of the nutrient solution. The research employed a Research and Development (R&D) approach involving system requirement analysis, hardware and software design, implementation, IoT integration, and performance evaluation. The hardware architecture consists of an ESP32 microcontroller, DS18B20 temperature sensor, soil moisture sensor, pH sensor, TDS sensor, and an I2C LCD for local data visualization. Sensor data are transmitted via WiFi using the HTTP protocol and displayed in real time through a web-based monitoring platform. Experimental results demonstrated that the developed system successfully acquired, processed, and transmitted environmental data continuously to the monitoring server. Analysis of the collected data showed soil moisture values ranging from 47.8% to 59.1%, temperature values between 28.13°C and 31.69°C, TDS values from 118.22 ppm to 138.02 ppm, and pH values between 3.27 and 13.78. The results also revealed an inverse relationship between temperature and soil moisture, indicating the influence of environmental temperature on water evaporation within the growing medium. Furthermore, the monitoring platform enabled real-time remote supervision and historical data storage, supporting data-driven decision-making for greenhouse management. Overall, the proposed system demonstrates the effectiveness of integrating electronic sensing devices and IoT technology to support smart agriculture applications in rice seedling cultivation.

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