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Journal of Electrical Engineering and Informatics (JEEI)
Published by SEKOLAH TINGGI TEKNOLOGI NUSANTARA LAMPUNG
ISSN : 31640591     EISSN : 31250165     DOI : -
Core Subject :
The Journal of Electrical Engineering and Informatics (JEEI) is a peer-reviewed, open-access scientific journal dedicated to publishing high-quality original research and review papers that advance knowledge in electrical engineering and informatics. Serving as a scholarly platform for researchers, academics, practitioners, and industry professionals, the journal aims to foster interdisciplinary collaboration, exchange scientific ideas, and promote innovative research. By focusing on both theoretical foundations and applied research, JEEI seeks to publish rigorous and impactful works that address contemporary challenges and support digital transformation, intelligent systems, and sustainable technological development at national and international levels. In line with its objectives, JEEI accepts a wide range of manuscripts covering a comprehensive scope of contemporary engineering and information technologies. The journal welcomes contributions in core and emerging areas, including electrical power systems, smart grids, renewable energy technologies, electronics, instrumentation, and control systems. Furthermore, it strongly encourages submissions focused on modern digital and computing advancements, such as artificial intelligence, machine learning, the Internet of Things (IoT), embedded systems, robotics, signal and image processing, information systems, and other smart engineering applications.
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Articles 10 Documents
 1 
Analysis of Transformer Oil Maintenance Management in Electrical Power Distribution Systems Agus Salim Wardana
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 1 (2026): FEBRUARY (I)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Abstract

Maintenance management is a critical technical and managerial approach to ensuring the reliability and sustainability of electrical power distribution systems, particularly through effective transformer oil maintenance. Transformer oil performs dual functions as an electrical insulating medium and a cooling agent; therefore, its degradation can significantly reduce transformer performance and operational reliability. This study analyzes the implementation of transformer oil maintenance management in distribution transformers within an electrical power distribution system using a field research approach with a descriptive–evaluative design. Data were collected through structured interviews, direct field observations, and a review of technical maintenance documents. The analysis focuses on the physical, chemical, and electrical characteristics of transformer oil, as well as the maintenance strategies applied, including preventive maintenance, corrective actions, and scheduled oil replacement. The results indicate that a structured transformer oil maintenance management framework contributes to maintaining transformer reliability, supporting continuous system operation, minimizing operational risks, and extending the economic service life of distribution transformers. These findings underscore the importance of condition-oriented maintenance management as an integral component of asset management in electrical power distribution systems.
Effect of Capacitor Capacitance on the Power Factor Performance of Single-Phase Induction Motors Elka Pranita
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 1 (2026): FEBRUARY (I)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Abstract

This study experimentally investigates the effect of capacitor capacitance variation on the power factor and operating characteristics of a single-phase induction motor under no-load conditions. Single-phase induction motors are widely used in residential and light industrial applications; however, their inductive nature often results in a low power factor and increased current consumption. To address this issue, capacitive compensation was applied by installing capacitors with different capacitance values (4 µF, 6 µF, 12 µF, and 18 µF) in the auxiliary winding circuit. The experimental setup was implemented in a controlled laboratory environment, and key parameters including power factor, stator current, electrical power consumption, starting voltage, and rotational speed were measured for each capacitance value. The results show that capacitor capacitance significantly influences motor performance. A capacitance of 4 µF provides the most favorable operating condition, yielding the highest power factor while maintaining relatively low current and power consumption. Increasing the capacitance beyond this value leads to excessive leading compensation, which is associated with higher current draw, increased power consumption, and reduced rotational speed. These findings indicate that appropriate capacitor selection is critical for achieving effective power factor improvement without compromising energy efficiency. The outcomes of this study provide practical guidance for capacitor sizing in single-phase induction motor applications and contribute to a better understanding of reactive power compensation under no-load operating conditions.
Design and Implementation of an Arduino-Based Food Delivery Robot with Bluetooth Control Fransiskus Xaverius Prasetyo Satriatama; Isnan Mulia; Anton Sukamto
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 1 (2026): FEBRUARY (I)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Abstract

The development of service robots has gained increasing attention as a solution to improve efficiency and consistency in food delivery services, particularly in environments with limited space and dynamic conditions. This study aims to design and implement an Arduino-based food delivery robot with Bluetooth control operated via a smartphone. The research employs a prototype-based development method, encompassing system requirement analysis, mechanical and electronic design, prototype construction, and functional testing. The robot is equipped with an Arduino Uno R3 microcontroller, DC gearbox motors, a Bluetooth HC-06 module for wireless communication, and an audio system to support basic human–robot interaction. System testing focuses on evaluating the robot’s ability to respond to movement commands and audio activation instructions transmitted through a smartphone application. The results demonstrate that the developed prototype is capable of executing directional movement commands and activating the audio system consistently within the defined operational range. These findings indicate that low-cost embedded platforms can be effectively utilized to develop functional food delivery robot prototypes suitable for controlled environments such as parties or small-scale service settings. This study contributes to the validation of a simple and replicable service robot design that may serve as a foundation for further development toward more autonomous and intelligent robotic service systems.
IoT-Based Monitoring of Evaporator Icing and Electrical Current in Precision Air Conditioning Sekar Kinasih; Ajeng Ameliana R
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 1 (2026): FEBRUARY (I)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Abstract

Precision Air Conditioning (PAC) systems play a critical role in maintaining thermal stability in telecommunication data centers that operate continuously. One common operational issue in PAC systems is evaporator icing, which can degrade heat transfer performance and compromise system reliability, while electrical current instability may indicate abnormal compressor operation. This study proposes an Internet of Things (IoT)-based monitoring system for real-time observation of evaporator temperature and electrical current as an early detection mechanism for potential faults in precision air conditioning systems. The system was developed using an ESP8266 microcontroller, a DS18B20 temperature sensor, and a PZEM-004T current sensor, with Telegram employed as a remote monitoring interface. Experimental testing was conducted on a precision air conditioning unit under normal operating conditions. The results demonstrate that the DS18B20 sensor provides accurate and consistent temperature measurements, with evaporator temperatures recorded within the normal operating range. Electrical current measurements obtained from the PZEM-004T sensor show close agreement with reference multimeter readings, indicating reliable current monitoring performance. The developed system successfully transmits temperature and current data in real time with stable communication performance. By integrating thermal and electrical parameters within a single IoT-based monitoring framework, the proposed system supports early fault detection and preventive maintenance in PAC systems. This approach enhances operational reliability and provides a practical, low-cost monitoring solution for telecommunication data center cooling applications.
An IoT-Enabled Monitoring System for Real-Time Water Quality Management in Catfish Aquaculture Bayu Arif Ramadhan; Muhammad Wahyu Fauzi; Sekar Kinasih
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 1 (2026): FEBRUARY (I)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Abstract

Water quality management is a critical factor in determining the productivity and sustainability of catfish (Clarias sp.) aquaculture. Conventional monitoring practices are generally manual, periodic, and inefficient, limiting farmers’ ability to respond promptly to environmental changes. This study presents the design and implementation of an Internet of Things (IoT)–based water quality monitoring system for catfish ponds using an ESP8266 microcontroller, a type-K thermocouple temperature sensor, and an SEN0161 pH sensor. The proposed system enables real-time monitoring of water temperature and pH, wireless data transmission to an online database, and continuous visualization through a web-based interface. System performance was evaluated in three pond environments indoor, semi-outdoor, and outdoor during a six-hour observation period, yielding a total of 1,012 measurement data points. The results indicate that the recorded water temperature and pH values generally fall within the recommended ranges for catfish aquaculture, demonstrating stable monitoring performance across different environmental conditions. Linear regression analysis further confirms the consistency of temperature and pH trends during the monitoring period. The findings show that the developed system is capable of providing reliable real-time water quality information and can support data-driven pond management while reducing dependence on manual measurements. Despite its effectiveness, the system’s performance is influenced by internet connectivity and is currently limited to temperature and pH parameters. Future work may focus on extending monitoring duration, improving communication reliability, and integrating additional water quality indicators to enhance system comprehensiveness.
Design of YNd11 Power Transformer Differential Protection Based on Percentage Differential Relay and MATLAB/Simulink Roy Hanum; Nambi Anasta
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 2 (2026): JUNE (II)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Abstract

Power transformers require fast and selective protection to prevent equipment damage, power interruption, and safety risks caused by internal faults. This study designs and evaluates a differential protection model for a 10 MVA, 150/20 kV, YNd11 power transformer using MATLAB/Simulink. The proposed model integrates current transformer compensation, differential current calculation, percentage differential relay characteristics, and second harmonic blocking to distinguish magnetizing inrush current from internal faults. The relay settings include a minimum operate current of 0.20 pu, two-slope characteristics of 20% and 80%, a restraint breakpoint of 1.0 pu, and a second harmonic blocking threshold of 15%. Four simulation scenarios were tested: normal operation, transformer energization inrush, phase-to-phase internal fault, and phase-to-ground internal fault on the 20 kV side. The simulation results show that the transformer model achieved a steady-state validation error below 0.25%. During normal operation, the residual differential current remained between 0.021 and 0.024 pu. During inrush, the relay was blocked because the second harmonic component reached 21.4%. For internal faults, the relay operated within 16.5 ms for phase-to-phase fault and 18.5 ms for phase-to-ground fault. These results indicate that the proposed model provides accurate, selective, and fast transformer differential protection.
Ground-Fault Protection Analysis for Electrical Installations in a Multi-Story Office Building Muhammad Solehan; Novia Utami Putri
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 2 (2026): JUNE (II)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Abstract

Reliable ground-fault protection is essential in multi-story building electrical installations because insulation failure, leakage current, and inadequate grounding may increase the risk of electric shock, fire, and equipment damage. This study evaluates the ground-fault protection system of the One-Stop Service Office Building in Bandar Lampung, Indonesia. Field measurements were conducted at 15 grounding points using the three-point fall-of-potential method with a Kyoritsu 4105A earth resistance tester. The installed residual current devices, locally configured as Earth Leakage Circuit Breakers (ELCBs), were assessed against PUIL 2020, IEC 60364, IEC 60479-1, and IEC 61008-1 requirements. Protection coordination and single line-to-ground fault behavior were further analyzed using ETAP 19.0. The measured grounding resistance ranged from 0.93 Ω to 6.21 Ω, with an average value of 2.56 Ω. Twelve grounding points, or 80% of the measured locations, complied with the 5 Ω maximum limit, whereas G-12, G-14, and G-15 exceeded the standard with values of 5.28 Ω, 5.85 Ω, and 6.21 Ω, respectively. The installed 30 mA ELCBs were suitable for personnel protection; however, type AC devices were still used on circuits supplying non-linear loads such as UPS and variable-frequency drives. Several toilet socket circuits were also found without residual current protection. The ELCB–MCB coordination was selective for low-level leakage currents, although simultaneous operation may occur around 160 A. Corrective actions include installing parallel rod electrodes, applying bentonite-based soil treatment, replacing type AC RCDs with type A devices on non-linear circuits, and implementing six-month preventive testing.
Comparative Analysis of KNN and Naive Bayes for Adolescent Mental Health Detection Mahesa Darma Satria; Yogi Saputra
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 2 (2026): JUNE (II)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Abstract

This study is motivated by the rapid rise of social media use among adolescents, which may adversely affect mental health by increasing the risk of stress, anxiety, and depression; thus, early detection is essential to prevent more severe outcomes. It aims to compare the performance of K-Nearest Neighbor (KNN) and Naive Bayes in detecting adolescent mental health risks and to evaluate the impact of data balancing using the Synthetic Minority Over-sampling Technique (SMOTE). A quantitative experimental design was applied, including data preprocessing, model implementation, and evaluation using 10-fold cross-validation with accuracy, precision, recall, F1-score, and AUC as performance metrics. The results show that Naive Bayes provides more stable performance with higher accuracy and precision, while KNN combined with SMOTE significantly improves recall, particularly for minority classes, indicating a trade-off between precision and recall in model selection. This study contributes a comprehensive analysis of the role of data balancing in classification performance within mental health contexts. Future work should explore ensemble and deep learning approaches and utilize larger, more diverse datasets to enhance generalizability.
Modern Phishing URL Detection Using Feature Selection and Comparative Classification Models M Teguh Prastyo; Febri Vahlevie
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 2 (2026): JUNE (II)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Abstract

Phishing URLs remain a critical cybersecurity threat because attackers increasingly exploit domain similarity, webpage imitation, and structural manipulation to deceive users and bypass conventional blacklist-based detection. This study proposes an engineering-oriented phishing URL detection pipeline using feature selection and comparative classification models implemented in RapidMiner. The PhiUSIIL Phishing URL Dataset was used, and after preprocessing, 234,903 URL records were retained, consisting of 134,834 legitimate URLs and 100,069 phishing URLs. Non-predictive attributes were removed, invalid target labels were filtered, missing predictor values were handled, and the target label was transformed into a binominal class, where phishing was treated as the positive class. Information Gain was applied to identify the most discriminative attributes, and the top-20 features were used for model comparison. Five classification models were evaluated using stratified 10-fold cross-validation: Decision Tree, Random Forest, Naive Bayes, Logistic Regression, and Gradient Boosted Trees. The results show that all models achieved accuracy above 99.95%, indicating strong class separability within the selected-feature scenario. Random Forest produced the most balanced performance, achieving 100.00% accuracy, 100.00% precision, 100.00% recall, 100.00% F1-score, and AUC of 1.000, with only three phishing URLs misclassified as legitimate. The findings demonstrate that selected URL similarity and webpage structural features can support efficient and interpretable phishing detection. However, the near-perfect performance should be interpreted as strong internal validation, and future work should include external dataset validation and ablation testing of dominant features.
Predicting Employee Burnout Risk Using Machine Learning and Workplace Well-Being Indicators Feti Arman; Lidia Olga; Anton Sukamto
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 2 (2026): JUNE (II)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Abstract

Employee burnout has become a critical concern in human resource management, particularly in flexible and work-from-home environments where workload intensity, digital exposure, and work-life boundaries are increasingly difficult to monitor. This study aims to develop a machine learning-based model for predicting employee burnout risk using daily work-from-home behavioral patterns and workplace well-being indicators. The dataset consisted of 1,800 daily records collected from 180 users, including work hours, screen time, meeting frequency, breaks, after-hours work, sleep duration, task completion rate, burnout score, and burnout risk category. To prevent target leakage, user identity and burnout score were excluded from the predictor set. The original burnout risk label was transformed into a binary classification target by grouping Medium and High categories into an At-Risk class, while Low was retained as Low Risk. Several supervised machine learning algorithms were evaluated using 10-fold stratified cross-validation, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosted Trees, Support Vector Machine, Naïve Bayes, and k-Nearest Neighbors. The results showed that Random Forest achieved the best overall performance, with 97.11% accuracy, 94.37% macro-F1, 94.24% balanced accuracy, and 90.11% recall for the At-Risk class. Feature importance analysis indicated that task completion rate was the most influential predictor, followed by work hours, sleep hours, and screen time. These findings demonstrate that machine learning can support HR analytics-based early warning systems for employee burnout risk detection in remote work settings.

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