Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT)
Journal of Electrical, Electronic, Information and Communication Technology (JEEICT) is a peer-reviewed open-access journal in English published twice a year by the Department of Electrical Engineering, Sebelas Maret University, Indonesia. The JEEICT aims to provide a leading-edge medium for researchers, industry professionals, engineers, educators, students to disseminate research work and studies in the fields of electrical, electronics, information and communication technology. The journal publishes work from power systems, electronics, instrumentation, and biomedical engineering, renewable energy and its application, control systems, information technology, and communication and vehicular technology disciplinary, in theoretical and experimental perspectives.
Articles
89 Documents
<![CDATA[Experimental Study of Lithium-ion Battery Performance Based on Mini-channel Cooling Plate ]]>
<![CDATA[Ihsan Pratama Rushadiawan]]>;
<![CDATA[Dominicus Danardono Dwi Prija Tjahjana]]>;
<![CDATA[Muhammad Nizam]]>;
<![CDATA[Julian Fikri Arifwardana]]>;
<![CDATA[Mufti Reza Aulia Putra]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 6, No 2 (2024): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.6.2.92488
<![CDATA[Making efficient batteries is important nowadays. One potential problem that can hinder this is the thermal runaway that occurs in battery cells. There are various causes of thermal runaway, one of the most common is an increase in temperature that exceeds the optimal allowable limit. Additional cooling will be required in vehicles that use batteries. Battery Thermal Management System (BTMS) with mini-channel cooling plate is one of the methods often used to maintain battery performance. In this study, the performance of Lithium-ion batteries is affected by fluid flow velocity. The experimental process was carried out by charging and discharging with a C-rate of 1C. Cooling is done with ethylene glycol fluid with fluid velocity variations of 0.21 L/min; 0.42 L/min and 0.63 L/min. The results show that fluid flow velocity affects the final battery temperature and battery performance. The optimal fluid velocity is shown at 4.2 L/min. At this speed it can reduce the battery temperature by 6.7°C.]]>
<![CDATA[Glucose Detection Based on Light Reflection in Modulated Optical Fibers for Continuous Diabetes Monitoring ]]>
<![CDATA[Mahmudah Salwa Gianti]]>;
<![CDATA[Ahmad Fauzi]]>;
<![CDATA[Muhammad Rizalul Wahid]]>;
<![CDATA[Taufik Ridwan]]>;
<![CDATA[Tri Seda Mulya]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 6, No 1 (2024): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.6.1.86301
<![CDATA[Diabetes mellitus is a chronic disease characterized by elevated blood glucose levels. Approximately 463 million adults worldwide have diabetes, and nearly half of them remain undiagnosed. Early diagnosis and treatment of diabetes are crucial to prevent serious complications. Continuous glucose monitoring (CGM) offers numerous advantages over traditional fingertip blood glucose measurements. CGM allows patients to track their glucose levels in real time, identify patterns, and adjust their diet and medication appropriately. This research presents a novel optical fiber-based glucose sensor utilizing the macrobending modulation technique. This method offers several advantages, including ease of use, low cost, and strong signal generation. The sensor exploits the refractive index difference between the glucose solution and the optical fiber. Variations in glucose concentration induce changes in the refractive index, which are converted into voltage signals. The sensor exhibits a sensitivity of 337 mV/decade and demonstrates a linear relationship between the voltage signal and glucose concentration within the range of 0-10 mM. The macro bending-modulated optical fiber sensor shows potential as a simple, cost-effective, and efficient CGM tool. Further research is necessary to enhance the sensor's sensitivity and stability and to evaluate its performance in biological samples. ]]>
<![CDATA[Design and Implementation of an Ultrasonic Sensor-Based Blind Spot Monitoring Prototype for Vehicle Safety ]]>
<![CDATA[Fikri Arif Wicaksana]]>;
<![CDATA[Trisiani Dewi Hendrawati]]>;
<![CDATA[Panji Narputro]]>;
<![CDATA[Muhammad Raihan Usman]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 2 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.7.2.108076
<![CDATA[Blind spots are one of the main contributing factors to traffic accidents in four-wheeled vehicles due to the driver’s limited visibility in certain areas surrounding the vehicle. This research presents the design and implementation of a blind spot monitoring prototype using ultrasonic sensors to detect objects in blind spot areas and provide real-time warnings to the driver. The system consists of four ultrasonic sensors (HC-SR04 for the front and JSN-SR04T for the sides and rear), an Arduino Mega 2560 microcontroller, a 20×4 LCD for distance display, and a buzzer for audible alerts. Sensor calibration was conducted to ensure measurement accuracy, achieving an average error rate of 0.77%. The prototype was tested under various simulated scenarios, including static and moving objects in different blind spot zones. The results show that the system successfully detected vehicles, motorcycles, and pedestrians in almost all testing conditions, with an average response time ranging from 0.20 to 0.35 seconds. However, the system faced limitations in detecting objects moving at speeds above 30 km/h, which is inherent to ultrasonic sensor technology. Despite this limitation, the proposed system offers a cost-effective alternative to radar- or camera-based blind spot detection systems, making it more accessible for a wider range of vehicles. The findings indicate that the developed prototype can effectively improve driving safety and has the potential for further enhancement through integration with IoT and advanced sensor fusion technologies.]]>
<![CDATA[Development of Sign Language Interpreter Glove for Speech-Impaired Deaf Individuals with Levenshtein Algorithm as an Autotext Correction System. ]]>
<![CDATA[Dea Muthia Febry]]>;
<![CDATA[Ananda Putra Kanieza]]>;
<![CDATA[Gilang Fajar Ramadhan]]>;
<![CDATA[Velisa Nur Aini]]>;
<![CDATA[Faisal Rahutomo]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 6, No 1 (2024): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.6.1.79652
<![CDATA[Assistive technology is technology developed in the form of aids, adaptive tools, and rehabilitation for individuals with disabilities to compensate for their lacking abilities. With assistive technology available to people with special needs, it can help them enhance their independent living skills and reduce their dependence on others, including in communication activities. This research aims to assess the feasibility of developing assistive technology for the speech-impaired, specifically a glove that can translate the SIBI letter sequence into words displayed on a 6 x 12 LCD screen and also produce sound output from the sign language hand movements of speech-impaired individuals when using the glove. The research method used in this study is Research and Development (R&D). The calibration test phase indicated that the prototype was in good condition, as evidenced by an accuracy rate above 90% for voltage at 0◦ and 90◦ angles produced by each finger. Consequently, the next calibration phase, which involves translating sensor readings into SIBI letters through digital data values, can be carried out by taking the ADC values of each finger. Subsequently, the glove was tested to read 7 out of 20 alphabets and achieved a success rate of ≥ 90% for 5 alphabets. The lowest success rate was 70% for the letter E. The average success rate for the 7 alphabet experiments was 91.4%. In the field test phase, the glove was tested on a deaf-mute student to form several words, and the output text displayed on the LCD and audio output matched the readings corrected by the auto-text correction system.]]>
<![CDATA[Application of LSTM Algorithm to Assist Diagnosis of Epilepsy Based on Electroencephalogram (EEG) Signals ]]>
<![CDATA[Sutrisno Ibrahim]]>;
<![CDATA[Kaleb Nathan Zebua]]>;
<![CDATA[Faisal Rahutomo]]>;
<![CDATA[Muhammad Alif Rizky Naufal]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 1 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.7.1.100360
<![CDATA[Epilepsy is a common disease that affects the brain's ability and has the potential to destroy the quality of life of sufferers. Diagnosis of epilepsy can be done by clinical testing and by using the electroencephalography (EEG) method. This research aims to apply artificial intelligence to improve the effectiveness and accuracy of EEG signal analysis. Epilepsy diagnosis is done automatically based on trained EEG signal files. This application can be done by applying the Long-Short Term Memory (LSTM) machine learning algorithm for recognizing patterns from brain signals that lead to epilepsy. The development was carried out using the EEG signal dataset from the University of Bonn which consists of 5 data sets. The detection process consists of the stages of data loading, augmentation, filtering, training, and classification. The developed system will be loaded into a GUI to facilitate users. The result of this research is a machine learning model with Long Short-Term Memory (LSTM) algorithm that has an accuracy rate of 91%, validation accuracy of 94% and loss of 0.2. Compared to other machine learning approaches such as SVM, KNN, and ANN, the proposed method achieves higher accuracy without the need for explicit feature extraction, highlighting its effectiveness in time-series signal classification. The model evaluation results show that this research is successful in assisting the detection of epilepsy using EEG signals with a high level of accuracy and efficiency.]]>
<![CDATA[Development of Solar Power Plant to Support Smart Farming 4.0 at Hubbul Khoir Islamic Boarding School Indonesia ]]>
<![CDATA[Sutrisno W. Ibrahim]]>;
<![CDATA[Muhammad Nizam]]>;
<![CDATA[Agus Ramelan]]>;
<![CDATA[Adriel Satrio Nugroho]]>;
<![CDATA[Salsabila Putri]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 5, No 2 (2023): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.5.2.77435
<![CDATA[Electricity is a very crucial thing in human life today such as for communications, education, offices, household appliances, and transportation. Unfortunately, most of the electricity in Indonesia is still generated by fossil fuels such as coal. Indonesia which is on the equator has an advantage in solar power generation. Every area in Indonesia gets sunlight for a full year. In this paper, we develop a 1.62 kWp solar power plant at the Hubbul Khoir Islamic Boarding School Indonesia. Energy generated from solar panels is used to support the hydroponic farming system owned by the Islamic boarding school. Economic analysis is used to find out whether the installed generating system is profitable or not. Power quality analysis is used to determine whether the load can be supplied with good power quality. The results obtained by the monitoring system can monitor the PV output power and the power to the load with a display on the LCD screen and on the web with overall accuracy above 96%, economic analysis results show that the system will return on investment after 6.8 years with a profit in the 25th year of Rp20,871,282, and quality the power in this system has good power quality from the PLN side and the inverter side.]]>
<![CDATA[Dress Code Selection Recommender System Based on Smartphone ]]>
<![CDATA[Venus Lidzikri Adhitya]]>;
<![CDATA[Muhamad Irsan]]>;
<![CDATA[Muhammad Faris Fathoni]]>;
<![CDATA[Diky Zakaria]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 6, No 1 (2024): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.6.1.82934
<![CDATA[In the era of rapidly developing information technology, the existence of smartphones has become an integral part of everyday life. Appearance and choice of dress code play a crucial role in a person's self-image. Therefore, this research aims to design a smartphone-based dress code selection recommendation system. This system will use clothing usage data, user preferences, and event context to provide relevant dress code recommendations. With this solution, it is hoped that users can easily and efficiently choose the appropriate dress code, increase self-confidence, and create a pleasant dressing experience. This research contributes to the development of smartphone-based applications to support users' lifestyle and personal appearance. This application not only provides dress code inspiration, but also makes it easier for users to make decisions regarding clothing choices. Model testing using Machine Learning with the K-Nearest Neighbor (KNN) algorithm shows satisfactory accuracy, precision and recall, namely 83.67%, 83.82% and 99.34%. This application has the potential to be a useful tool helping users live an informed fashion lifestyle and according to personal preferences, and also minimize the waste of time that would occur when choosing clothes.]]>
<![CDATA[Scalable Microservices Architecture for Face Recognition-Based Employee Attendance Systems ]]>
<![CDATA[Ridwan Setiawan]]>;
<![CDATA[Wawan Hermawan]]>;
<![CDATA[Asep Trisna Setiawan]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 2 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.7.2.108430
<![CDATA[We present a face-recognition-based employee attendance system built on a microservices architecture and integrated with the external Worker AI (LSKK) inference API. The design separates camera I/O, verification, persistence, and presentation into independently deployable services, enabling targeted scaling and resilient operation through asynchronous queues. The system was developed using Rapid Application Development (RAD) and evaluated via black-box testing that covered authentication, camera and AI data views, filtering and pagination, reporting, and employee CRUD. The results show conformance to specifications: the interface renders the expected outputs, and the cooldown policy effectively prevents duplicate entries, while the separation of history (raw) and history_ai (verified) supports traceability and clean reporting. These findings indicate that combining microservices with API-based face recognition offers a practical and maintainable alternative to RFID-based workflows with fewer operational frictions. Limitations include the use of an external inference API (model configuration and thresholds are outside our control) and testing within a single organizational setting. Future work will focus on operational measurements of the deployed pipeline, particularly end-to-end latency under load spikes and queue formation, as well as monitoring misread/error rates to inform model improvements.]]>
<![CDATA[IoT with Firebase: Smart Ring Android App Using MAX30100 for Fatigue Detection ]]>
<![CDATA[Liptia Venica]]>;
<![CDATA[Elysa Nensy Irawan]]>;
<![CDATA[Dewi Indriati Hadi Putri]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 6, No 1 (2024): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.6.1.81312
<![CDATA[IoT in healthcare enables real-time health monitoring & data evaluation of patient conditions. One of the benefits of IoT wearable devices is protecting a person from getting exhausted. Body fatigue is an indicator of the emergence of several problems such as sudden attacks of dangerous diseases, accidents, and so on. The large number of deaths from various diseases and accidents that are triggered by body fatigue makes monitoring the level of body fatigue important to minimize this. Through this research, we proposed the Smart Ring; a tool for monitoring and evaluating the level of body fatigue using a wearable sensor and based on the Internet of Things (IoT). In this article, we focus on developing the software component (Android application) and database management system of the Smart Ring IoT system. Age, heart rate, SpO2, and body temperature are used as indicators to determine user’s body condition categories. These data are collected through sensors on hardware part of Smart Ring System. The proposed database management system is able to store collected data inside NoSQL database in the form of documents. Smart Ring Android-based application is capable to monitor three user’s body condition and evaluate them to predict the user’s condition with classification accuracy of 100% within the defined categorization rules. It offers real-time user monitoring for exhaustion signs and triggering timely alerts with sub-2-second data processing under ideal conditions. The proposed Smart Ring system is expected to become an easy to develop, economical and portable medical device which help improve the welfare of society 5.0 in the health sector.]]>
<![CDATA[Battery Capacity Estimation with Kalman Filter for Battery Management System of Public Street Lighting ]]>
<![CDATA[Agus Ramelan]]>;
<![CDATA[Insan Fadhil Maulana]]>;
<![CDATA[Fakhri Muhammad Azzam]]>;
<![CDATA[Muhammad Shiddiq]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 1 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.7.1.94556
<![CDATA[This work aim to evaluate effectiveness of Kalman Filter in increase State of Charge (SoC) estimation on the system management battery For Street Lighting based on ESP32 microcontroller. SoC is a crucial parameter in determine level filling battery on time certain . In study Here , we use random data from the INA219 voltage and current sensor to simulate various condition operational . We do analysis to use of Kalman Filter on this data use evaluate improvement accuracy estimate SoC. Studies literature show that the Kalman Filter has succeed applied in various application For repair estimate system based on measurement data that is not perfect . Through modeling and simulation , we compare SoC estimation obtained from Kalman Filter with estimate direct from sensor data. Experimental results show that the Kalman Filter is significant reduce variation and increase accuracy battery SoC estimation , with average error not enough from 1.5%. Findings This support that use of Kalman Filter in system management PJU batteries have potential big For increased reliability operational and efficiency use energy . Research This give donation important in development monitoring and management technology more battery sophisticated and accurate For applications in the field.]]>
