Journal of Engineering, Electrical and Informatics
The journal publishes original papers in the field of Engineering, Electrical and Informatics which covers the following scope: Power Engineering Electric Power Generation, Transmission and Distribution, Power Electronics, Power Quality, Power Economic, FACTS, Renewable Energy, Smart Grid, Electric Traction, Electric Vehicles, Electromagnetic Compatibility, Electrical Engineering Materials(Conductors, Superconducors, Dielectrics and Magnetics), High Voltage Insulation Technologies, High Voltage Apparatuses, Lightning Detection and Protection, Power System Analysis, Power System Protection, SCADA, Electrical Measurements Telecommunication Engineering Antenna and Wave Propagation, Modulation and Signal Processing for Telecommunication, Wireless and Mobile Communications, Information Theory and Coding, Communication Electronics and Microwave, Radar Imaging, Distributed Platform, Communication Network and Systems, Telematics Services, Security Network, and Radio Communication. Computer Engineering Computer Architecture, Parallel and Distributed Computer, Pervasive Computing, Computer Network, Embedded System, Human—Computer Interaction, Virtual/Augmented Reality, Computer Security, VLSI Design-Network Traffic Modeling, Performance Modeling, Dependable Computing, High Performance Computing, Computer Security Control and Computer Systems Optimal, Robust and Adaptive Controls, Non Linear and Stochastic Controls, Modeling and Identification, Robotics, Image Based Control, Hybrid and Switching Control, Process Optimization and Scheduling, Control and Intelligent Systems, Artificial Intelligent and Expert System, Fuzzy Logic and Neural Network, Complex Adaptive Systems. Electronics To Study Microelectronic System, Electronic Materials (semiconductors and optics), Design and Implementation of Application Specific Integrated Circuits (ASIC), System-on-a-Chip (SoC) and Electronic Instrumentation Using CAD Tools, Sensors Information technology Digital Signal Processing, Human-Machine Interface, Stochastic Systems, Information Theory, Intelligent Systems, IT Governance, Networking Technology, Optical Communication Technology, Next Generation Media, Robotic Instrumentation Informatics Information Search Engine, Multimedia Security, Computer Vision, Information Retrieval, Intelligent System, Distributed Computing System, Mobile Processing, Next Network Generation, Computer Network Security, Natural Language Processing, Business Process, Cognitive Systems. Data and Software engineering Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods), Programming (Programming Methodology and Paradigm), Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data) Biomedical Engineering Biomedical Physics, Biomedical Transducers and instrumentation, Biomedical System Design and Projects, Medical Imaging Equipment and Techniques, Telemedicine System, Biomedical Imaging and Image Processing, Biomedical Informatics and Telemedicine, Biomechanics and Rehabilitation Engineering, Biomaterials and Drug Delivery Systems.
Articles
115 Documents
<![CDATA[Integrated Techno-Economic Evaluation, Monte Carlo Probabilistic Reliability, Unit Commitment Optimization, and Frequency Stability of Small Modular Reactor (SMR) Integration in Isolated Systems of 300–1000 MW ]]>
<![CDATA[Gunawan T. Hadiyanto]]>;
<![CDATA[Intan Kumala Sari]]>
<![CDATA[ Journal of Engineering, Electrical and Informatics Vol. 6 No. 1 (2026): Februari: Journal of Engineering, Electrical and Informatics ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.55606/jeei.v6i1.6966
<![CDATA[Electric power systems in island regions often operate as isolated grids, heavily dependent on diesel generators, leading to high fuel costs and vulnerability to fluctuations in global energy prices. This study presents an integrated evaluation framework for analyzing the integration of Small Modular Reactors (SMRs) into these isolated power systems. The research combines techno-economic analysis using Levelized Cost of Electricity (LCOE), probabilistic reliability simulation through Sequential Monte Carlo Simulation, operational optimization with the Unit Commitment model, and system frequency stability analysis. A case study was conducted on an island grid system with a peak load of 650 MW. The simulation results show that integrating SMRs can reduce Expected Energy Not Served (EENS) by over 60% and yield a median LCOE of approximately 77 USD/MWh. These results suggest that SMRs offer a reliable, cost-effective generation solution for isolated grids, improving both energy security and economic efficiency by reducing reliance on diesel and enhancing the sustainability of energy systems in remote areas.]]>
<![CDATA[Implementation of the Naive Bayes Algorithm and Support Vector Machine for Public Sentiment Analysis towards the Ratification of the Job Creation Bill on Twitter ]]>
<![CDATA[Untung Surapati]]>;
<![CDATA[Sopan Adrianto]]>;
<![CDATA[Erno Sumantri]]>;
<![CDATA[Melinius Nopianto]]>
<![CDATA[ Journal of Engineering, Electrical and Informatics Vol. 2 No. 1 (2022): Februari : Journal of Engineering, Electrical and Informatics ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.55606/jeei.v2i1.202
<![CDATA[The test design of the Public Sentiment Analysis on the Ratification of the Job Creation Bill with the RapidMiner Studio application. The initial stage is to collect data in the form of tweets of Twitter users and then put it into a CSV file, the data obtained will be divided into training data and test data. Furthermore, the training data will be labeled consisting of 2 types of labels, namely Positive and Negative labels, then the data will be cleaned from unneeded words such as Mention or Hastag, then the data will go through several stages in the Preprocessing stage to convert raw data into data that is ready to be processed. Furthermore, each word will be weighted with the TF-IDF method. The final result of the comparison with these two test methods, namely the prediction of Public Sentiment Towards the Issue of Determining the Job Creation Bill based on data obtained from Twitter and implemented by the SVM (Support Vector Machine) method, showed an accuracy value of 96.52%. Of the 605 test data, 492 data were predicted as Negative Sentiment and 112 data as Positive Sentiment and the Naive Bayes Method showed an accuracy value of 49.67%. Of the 605 test data, 492 data were predicted as Negative Sentiment and 112 data as Positive Sentiment.]]>
<![CDATA[Public Sentiment Analysis on the Issue of Stopping Tax Payments on Twitter Using the Naive Bayes Method and Support Vector Machine ]]>
<![CDATA[Mesra Betty Yel]]>;
<![CDATA[Yuma Akbar]]>;
<![CDATA[Sugiyono Sugiyono]]>;
<![CDATA[Nova Mahendra]]>
<![CDATA[ Journal of Engineering, Electrical and Informatics Vol. 2 No. 1 (2022): Februari : Journal of Engineering, Electrical and Informatics ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.55606/jeei.v2i1.203
<![CDATA[This research was conducted to find out public opinion on the Stop Paying Tax Issue on Twitter social media. In this study the author aims to use the Naïve Bayes Algorithm and Support Vector Machine in analyzing positive and negative sentiment labels and knowing the results of the accuracy of the Naïve Bayes algorithm and Support Vector Machine in posts by Twitter social media users related to Stop Paying Taxes. The data collection process in this study will using public data sets. The public data set is obtained from 2000 tweets. The final result of this comparison with the two test methods uses the naïve byes algorithm and Support Vector and Machine, namely the prediction results of Public Sentiment on Stop Paying Tax Issues based on data obtained from Twitter and implemented with the SVM (Support Vector Machine) method showing an accuracy value of 84.77 % Of the test data, it is predicted that 1,192 data are Negative Sentiment and 174 data are Positive Sentiment. Of the 1367 test data, 883 data were predicted as Negative Sentiment and 483 data as Positive Sentiment For the prediction results from Negative Sentiment, there were 1367 data predicted Negative and 1 data predicted Positive.]]>
<![CDATA[Design of an IoT-Based Server Room Temperature Security Monitoring System Using a Microcontroller and Fuzzy Logic Method ]]>
<![CDATA[Yuma Akbar]]>;
<![CDATA[Tri Wahyudi]]>;
<![CDATA[Sugiyono Sugiyono]]>;
<![CDATA[Ghofurur Nawangsah]]>
<![CDATA[ Journal of Engineering, Electrical and Informatics Vol. 2 No. 2 (2022): Juni: Journal of Engineering, Electrical and Informatics: ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.55606/jeei.v2i2.204
<![CDATA[PT. Sridatta Prastama Telecommunications (PRASTATEL) as a company in the field of telecommunications service providers must provide non-stop cell phone / VoIP servi-ces. Devices that work 24 hours non-stop by minimizing the damage that occurs, must be supported by monitoring to ensure the system is running properly. If there is a sig-nificant increase in temperature, it can affect system performance or cause damage to the hardware side. The cooler in the server room is felt to be not optimal because the cooler is often constrained by frequent power outages or the cooler turns off and avoids suspicious activities / activities that occur in the server room because the server room administrator is not always on site. From the problems described above, a solution is needed to monitor the system remotely. So that the system is able to know changes in room temperature (Monitoring) in real time and monitor whether there is activity oc-curring in the server room. By using Internet of Things (IoT) technology, the NO-DEMCU ESP-8266 device and the fuzzy logic method which basically maps an input space into an output space that is applied to the server room temperature sensor. This monitoring system uses the Telegram application to receive notifications in the form of text or images. So that it can monitor temperature changes and activities that occur in the server room in real time and accurately. Therefore the server room administrator does not have to be on the site.]]>
<![CDATA[Effectiveness of Face Recognition-Based Security System on CCTV with Raspberry Pi and Esp32-Cam Using Face Recognition Method ]]>
<![CDATA[Frencis Matheos Sarimole]]>;
<![CDATA[Satria Wira Yudha]]>;
<![CDATA[Sutisna Sutisna]]>;
<![CDATA[Ahas Eko Septianto]]>
<![CDATA[ Journal of Engineering, Electrical and Informatics Vol. 2 No. 2 (2022): Juni: Journal of Engineering, Electrical and Informatics: ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.55606/jeei.v2i2.205
<![CDATA[Current technological advances, such as the internet of things (IOT), have a very broad scope. Especially in the security sector. The fact is that there are many robots that have been made by humans to do jobs that can help humans beyond their abilities. CCTV is very important to protect the house from various types of threats, such as burglary and other hazards. However, a security system that only uses CCTV cameras is no longer secure enough because someone is needed to monitor activities in the CCTV area for 24 hours. As for CCTV that provides facial recognition features, the price is arguably quite expensive. Therefore, we need home security with a more modern, affordable, and effective version of CCTV that utilizes the technology that has been developed to date. In this context, I propose a prototype of a sophisticated, low-cost, Raspberry-PI-based home security system that is integrated with a mobile real-time application. This intelligent robot can monitor the surrounding area by detecting people who are within the range of the camera. notification if a stranger enters the area and is not recognized by the robot to a mobile application that can be installed. The author uses Raspberry Pi hardware as the main control center, OpenCV to perform motion detection and facial recognition, a webserver to make it easier for users to access data and control the system remotely, mobile applications as notification recipients, and real-time monitoring of CCTV. In the tests carried out, the developed IOT-based security system has succeeded in detecting motion and facial recognition with good accuracy and is able to send notifications to smart phones in a short time when suspicious events occur in the house. Thus, this IOT-based home security system can help improve security and comfort by integrating technology and providing more effective and efficient solutions for protecting homes and buildings from various types of threats]]>
<![CDATA[Implementation of Naive Bayes Algorithm and Support Vector Machine for Public Sentiment Analysis towards Imported Clothing Ban ]]>
<![CDATA[Veri Arinal]]>;
<![CDATA[Frencis Matheos Sarimole]]>;
<![CDATA[Kiki Setiawan]]>;
<![CDATA[Ahmad Ramdani]]>
<![CDATA[ Journal of Engineering, Electrical and Informatics Vol. 2 No. 3 (2022): Oktober: Journal of Engineering, Electrical and Informatics: ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.55606/jeei.v2i3.313
<![CDATA[This research was conducted to find out the public's opinion on the Issue of Imported Clothing on Twitter social media. One of the algorithms that can be used to carry out sentiment analysis is Naïve Bayes and Support VectorMachine. In this research the author aims to use the Naïve Bayes Algorithm and Support Vector Machine in analyzing positive and negative sentiment labels. The final result of the comparison with these two test methods, namely the prediction of public sentiment on the issue of imported clothing based on data obtained from Twitter and implemented using the SVM (Support Vector Machine) method, shows an accuracy value of 87.89%. Of the 603 test data, it is predicted that 194 data are Positive Sentiment and 409 data are Negative Sentiment. For prediction results from Negative Sentiment, there are 603 data predicted Negative and 2 data predicted Positive. and the Naive Bayes method shows an accuracy value of 97.01%. Of the 603 test data, it is predicted that 409 data are Negative Sentiment and 194 data are Positive Sentiment.]]>
<![CDATA[Classification of Favorite Book Borrowing Data at the STIKOM CKI Library Using the Decision Tree Algorithm ]]>
<![CDATA[Yuma Akbar]]>;
<![CDATA[Untung Surapati]]>;
<![CDATA[Sutisna Sutisna]]>;
<![CDATA[Yansen Yansen]]>
<![CDATA[ Journal of Engineering, Electrical and Informatics Vol. 2 No. 1 (2022): Februari : Journal of Engineering, Electrical and Informatics ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.55606/jeei.v2i1.3663
<![CDATA[The library on the STIKOM CKI campus as a means of providing information and has a complete collection of learning media books, but the data processing system for borrowing and returning favorite books in the library is currently still manual, that is, all data collection processes are written on book cards, although it is quite good but the process is rather slow and requires quite a long time because in the process of searching the data must be checked per page one by one so that the data processing is less effective and efficient. To overcome this, it is necessary to develop an application using the decision tree algorithm method which can make it easier to collect borrowing data and return favorite books that are more effective and efficient and display integrated output of student reports that have not returned so that data processing is more accurate and can speed up officer performance. library. Submitting a favorite book lending classification application can make it easier to access loans and returns anywhere and anytime. So that data processing is more accurate and can speed up librarian performance.]]>
<![CDATA[Application of TF-IDF and Xgboost Methods for Public Sentiment Analysis Towards Ozzaskin Skincare Brand on Social Media ]]>
<![CDATA[Mesra Betty Yel]]>;
<![CDATA[Elviwani Elviwani]]>;
<![CDATA[Nova Dahliyanti]]>;
<![CDATA[Ahmad Syahran Zidane]]>
<![CDATA[ Journal of Engineering, Electrical and Informatics Vol. 5 No. 1 (2025): Journal of Engineering, Electrical and Informatics ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.55606/jeei.v5i1.3676
<![CDATA[Ozzaskin is a local skincare brand founded by Ustadzah Oki Setiana Dewi that targets Muslim women and focuses on reducing dark spots and acne scars. Over time, this domestic brand has attracted considerable public attention on social media—particularly among mothers—garnering both praise for its product efficacy and criticism regarding price and texture. This study aims to analyze public sentiment toward the Ozzaskin brand by performing web scraping on Instagram and TikTok data, employing TF-IDF for textual feature extraction and XGBoost as the classification algorithm. The findings are expected to provide a comprehensive overview of consumer perceptions of Ozzaskin and to assist the marketing team and product developers in formulating communication strategies and improving product formulas that more effectively address user needs. The novelty of this research lies in the comprehensive application of the TF-IDF + XGBoost framework for brand-related sentiment analysis on Indonesian-language social media.]]>
<![CDATA[Application of Voice Processing Technology With A Natural Language Processing Approach for Pronunciation Correction of Selected Vocabulary In English ]]>
<![CDATA[Dadang Iskandar Mulyana]]>;
<![CDATA[Rizki Ananda Pratama]]>;
<![CDATA[Sugiyono Sugiyono]]>;
<![CDATA[Agiah Sofia]]>
<![CDATA[ Journal of Engineering, Electrical and Informatics Vol. 5 No. 1 (2025): Journal of Engineering, Electrical and Informatics ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.55606/jeei.v5i1.3691
<![CDATA[English vocabulary pronunciation is one of the important aspects that must be mastered by English learners. However, many people in Indonesia face difficulties in mastering basic vocabulary pronunciation, which can hinder their progress in the early stages of learning speaking and listening. Mistakes in pronunciation often cause ineffective communication, making the message difficult for the other person to understand. Even at the level of learners who have memorized many vocabularies and are able to have simple conversations, mispronunciation remains a significant obstacle and often hinders smooth communication. To address this challenge, this study aims to develop an application based on speech processing technology and Natural Language Processing (NLP) that is specifically designed to provide pronunciation correction for selected vocabulary. This application focuses on mastering the pronunciation of 500 basic vocabulary as a companion for learning speaking and listening at an early stage. This application does not only aim to memorize vocabulary, but also helps users learn to pronounce each word correctly. With this approach, users can get real-time corrective feedback for each vocabulary spoken, allowing them to correct mistakes directly and gradually improve their speaking ability. In addition, the application provides pronunciation analysis supported by speech processing technology to recognize and analyze user pronunciation errors, while NLP is used to provide relevant assessments and improvement suggestions automatically. The results of the study show that this application is effective in helping learners improve their pronunciation of basic vocabulary. By focusing on frequently used basic vocabulary, this application helps users improve their speaking skills more easily. This application is also a tool that supports independent learning for users, thereby increasing their confidence in speaking English. This study is expected to be a solution that supports more effective and affordable English learning, especially for beginners in Indonesia who want to start mastering speaking and listening with a stronger foundation.]]>
<![CDATA[Application of Data Mining for Talent Performance Analysis Using The C.45 Method In A Case Study of The Human Resource Department of PT. Xyz ]]>
<![CDATA[Sutisna Sutisna]]>;
<![CDATA[Tri Wahyudi]]>;
<![CDATA[Dedi Gunawan]]>;
<![CDATA[Ivan Pradana]]>
<![CDATA[ Journal of Engineering, Electrical and Informatics Vol. 2 No. 3 (2022): Oktober: Journal of Engineering, Electrical and Informatics: ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.55606/jeei.v2i3.3710
<![CDATA[Human resource management plays a critical role in supporting organizational performance, particularly in identifying employees with high competency and leadership potential. The process of evaluating employee performance is often conducted manually, which may lead to subjectivity and inconsistencies in decision-making. This study aims to implement the C4.5 decision tree algorithm for talent performance analysis within the Human Resource Department of PT. XYZ. The research utilized employee performance data collected during the 2022–2023 period, including variables such as attendance, achievement, assessment results, service period, and other competency-related indicators. The study adopted the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. The C4.5 algorithm was employed to classify employee competencies and generate decision rules based on entropy and information gain calculations. The results indicate that the algorithm successfully identified the most influential attributes affecting employee performance classification, with achievement, assessment, and service period emerging as key determinants. The resulting decision tree provides a systematic and interpretable classification model that supports objective employee evaluation and talent identification. The study demonstrates that the application of data mining techniques can assist organizations in improving the effectiveness of employee performance assessment and human resource decision-making processes.]]>
