Jurnal ELTIKOM
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Time Segment Analysis of Heart Rate Variability to Evaluate Daily Stress using Wearable Device Technology
Full text linkPresent studies have successfully evaluated psychological properties such as mental health and stress by using physiological data from the cardiovascular system. Most studies established specific interventions and ambiguous heart rate properties according to homeostatic conditions. We proposed a study evaluating mental stress based on daily activities dataset. Twenty-two healthy men were observed in this study. We employed two approaches based on the time segments, while extracting the HRV parameters. We discovered that there was no significant difference between the parameters corresponding to the daily stress score groups (low- and high-stress) when we used whole-day recording in one segment HRV parameter measurement (p > 0.05). However, by extracting the HRV parameters based on multi time segments (phases 1, 2, and 3), we found parameters that were able to properly distinguish the two groups (low- and high-stress). The frequency domain parameters are the most sensitive features, especially the LF and HF (p < 0.01), followed by the total power (p < 0.05). In the time domain measurement, the RMSSD, StdHR, SD1, and SD2 are able to differentiate the participants based on the daily stress scores (p < 0.05). As a result, this study proposed that by continually monitoring biological signals based on time segment and employing the given parameters, it is possible to appropriately and meaningfully measure the daily stress condition for future classification studies
Wind Power Frequency Control in Doubly FED Induction Generator Using CFMPC-FOPID Controller Scheme
Full text linkBecause the majority of wind turbines operate in maximum output power tracking mode, power system frequency cannot be supported. However, if the penetration rate of wind power increases, the system inertia related to frequency modulation may decrease. In addition, frequency stability will be severely affected in the event of significant disturbances to the system load. Due to the high penetration of wind power in isolated power systems, this study suggests a coordinated frequency management approach for emergency frequency regulation. In order to prevent the phenomenon of load frequency control in doubly fed induction generators (DFIGs), a unique efficient control scheme is developed. The Cascaded Fractional Model Predictive Controller coupled with Fractional-Order PID controller (CFMPC-FOPID) is developed to provide the DFIG system with an efficient reaction to changes in load and system parameters. The proposed controller must have a robust tendency to respond quickly in terms of minimum settling time, undershoot, and overshoot. Nonlinear feedback controllers are designed using frequency deviations and power imbalances to achieve the reserve power distribution between generators and DFIGs in a variety of wind speed conditions. It makes upgrading quick and easy. In Matlab/Simulink, a simulation model is built to test the viability of the suggested approach
Analysis of Power Generation and Distribution of Hybrid Energy for Electricity Loads in Batakan Village
Full text linkThe need for electrical energy continues to increase over time. However, in Indonesia power plants are still dominated by fossil fuel power plants, and there are still many areas without access to electricity. The use of renewable energy is needed to replace fossil fuels considering that fossil fuels can run out one day. The coastal area of Batakan Village in Tanah Laut Regency, South Kalimantan Province, was chosen as the focus location for conducting hybrid power plant simulations because this village is located in a coastal area where wind and solar energy sources are abundant. Batakan Village is approximately 40 km from Pelaihari City. Medium-voltage network transmission system (JTM) is supplied from Pelaihari City, and it is almost certain that this village experiences large power losses over long distance. This power loss will be detrimental if an effort is not made to reduce it. The purpose of this research is first to determine the optimal hybrid power plant configuration design to reduce power loss in the electricity system in Batakan Village. Second, it will analyze the power loss of the hybrid power plant system in Batakan Village, and finally, this research is going to analyze the investment feasibility of the hybrid power plant system in Batakan Village. In this study, the design of renewable energy plants, such as solar power plants (PLTS) with a total capacity of 406.1 kW and wind power plants (PLTB) with a total capacity of 125 kW, and the electricity network (grid system) are used together in a hybrid power generation system. The ETAP software was used to analyze the power losses of the hybrid power generation system, while the HOMER software was used to determine the net present value (NPV) and cost of energy (COE) of the hybrid power generation system. The results show that the configuration of the solar, wind, and grid systems is the most optimal. It is obtained from the results of ETAP simulations that have been carried out during average load and peak load conditions that by including the Solar Power Plant and Wind Power Plant power losses in the electricity system in Batakan Village can be reduced from the previous one using the system configuration only connected to the PLN power grid (grid system only). The total power losses incurred was 269.1 kW of active power and 1613.5 kvar of reactive power at average load reduced to 266.9 kW of active power and 1568.9 kvar of reactive power. At peak load the total power losses were 423.4 kW of active power and 2573.0 kvar of reactive power and they deceased to 41.,5 kW of active power and 2510.5 kvar of reactive power. In terms of investment, the COE value decreased by IDR 111, and the NPC decreased by IDR 6,600,000,000 at the average load. At the peak load COE decreased by IDR 88, while NPC by IDR 7,000,000,000. The return of investment (ROI) value is 13.2%, which indicates that the investment is still in the profitable stage
Model Analysis of Gated Recurrent Unit for Multivariate Rice Price Forecasting
Full text linkFood security, especially in the agricultural sector in the form of food price stability of rice as a national food ingredient is a strategic issue for Indonesia. Rice price forecasting is needed to mitigate rising rice food prices. Rice price fluctuations can be caused by internal factors such as bad weather or external factors such as the low selling price of rice, resulting in losses for farmers. This study aims to carry out multivariate rice price forecasting in DKI Jakarta by involving rice prices, weather, economic, and health factors using the Gated Recurrent Unit (GRU) algorithm where the accuracy test is based on the MAPE value between forecasting results and actual data. As a result of the GRU algorithm for multivariate rice price forecasting, the MAPE for training and testing is 0.964% and 2.628%, indicating that all models in the measurement category are very well represented
The Hybrid Cryptographic Algorithms for Secure RFID Data Protection in The Internet of Things
Full text linkRFID is often used by companies to identify employees and company assets, as well as in supermarkets to identify goods when shopping. In this increasingly sophisticated era, IoT technology has wide applications. The use of RFID technology in IoT networks may pose vulnerabilities to security and privacy because it contains sensitive information, and RFID data transmitted over communication channels is vulnerable to attacks. IoT technology has characteristics such as high autonomous data capture rate, network connectivity, and interoperability for services and applications. Therefore, this research aims to improve the security of RFID data by taking into account the characteristics of IoT. The method used is hybrid cryptography by combining AES (Advanced Encryption Standard) and ECDH (Elliptic-curve Diffie-Hellman) keys. AES, as a commonly used symmetric cryptography, is chosen to protect the data, while ECDH, as the latest asymmetric cryptography, is used for a faster and more efficient process compared to previous asymmetric methods. This study utilizes the Python programming language on Jupyter Notebook. The initial step of the study involved scanning the RFID data to be secured and configuring the key on ECDH. The subsequent process included encryption and decryption of the data. The study successfully tested the success of encryption and decryption on RFID UIDs. The test data includes the result display of the hybrid encryption, the encryption and decryption processing time, and the file size of the encryption (ciphertext) and decryption (decodetext). These results show an excellent level of security for RFID UIDs. Only those with a specific key can know the contents of the cipher. It should be noted that this study was only conducted at the program level and was not implemented on hardware. Therefore, the results can be a valuable reference for future research
Optimization of 4G LTE Network Bad Spot Area using Automatic Cell Planning Method
Full text linkThe 4G LTE technology is widespread in almost all regions in Indonesia, including Solok City in West Sumatra, where this study was conducted. Based on the results of the 1800 MHz frequency drive test, the RSRP value was in the good category (81.44%), but the SINR value was in the poor category (56.96%). Furthermore, the results of the 2100 MHz frequency drive test showed that the RSRP value was in the poor category (58.11%), and the SINR value was also in the poor category (54.62%). These results indicate that the area had poor network quality. Therefore, this study aimed to optimize the Bad Spot Area in Solok City using the Automatic Cell Planning (ACP) method. The ACP method optimization results show that at the 1800 MHz frequency, the value of the ten cells is in the interval -127.84 ≤ RSRP ≤ -99.34. Meanwhile, at the 2100 MHz frequency, the value of the seven cells is in the poor category, which is in the interval -136.39 ≤ RSRP ≤ -111.03. In the 2100 MHz frequency, there is a decrease in RSRP value in the poor cate-gory from 43.27% to 41.85%. SINR parameter optimization results of 2100 MHz frequency, the percentage of a very good category is higher with a value of 51.40% than 44.16% at 1800 MHz frequency
Automatic Tube Counter Monitoring System using Infrared Sensor based on NODEMCU ESP8266
Full text linkTechnology is needed in every industry because it can simplify the production process, improve production quality, and enhance the company\u27s reputation in the sight of consumers. The cylinder counting activity at PT Batuah Energi Indonesia is still done manually, involving time and standardized estimation of LPG cylinder loads, which faces inaccuracy issues. In fact, PT Batuah Energi Indonesia has facilities that handle many LPG cylinders from various users and providers of LPG cylinders. While accurate cylinder counts are beneficial to the industry, companies need technology that can automatically calculate the number of filled LPG cylinders. Therefore, this study was carried out to demonstrate to students how to develop automatic tube counters using an infrared E18-D80NK as a tube detector, NodeMCU microcontrollers ESP8266 as controllers, Arduino IDE for developing programs, and IoT for remote monitoring. The developed device approach, specifically using the E18-D80NK infrared proximity sensor based on the NodeMCU ESP8266, can be coded using the Arduino IDE compiler. For the detection of the number of tubes, the E18-EN80K infrared sensor is used and data transmission wirelessly utilizes the Blynk application. The results of the automatic tube counter monitoring tool were successfully tested with a 100% accuracy rate, utilizing the E18-D80NK infrared sensor and NodeMCU microcontroller ESP8266, and can be monitored remotely using Blynk
English
No full textAquaculture of Litopenaeus Vannamei shrimp is one of Indonesia\u27s most crucial commodity export shrimp. Aquaculture feed management and environmental management are essential factors in determining shrimp sustainability. To maximize shrimp farming results, proper feeding, water quality control, and con-tinuous monitoring of three critical parameters: temperature, power of hydrogen (pH), and salinity levels in ponds are required. This study aims to feed the shrimp automatically at predetermined times (07.00, 11.00, 16.00 and 20.00). At the same time, it will monitor pond water quality parameters. Temperature, pH and salinity are all factors monitored. Every 10 minutes, monitored data is stored in ThingSpeak using IoT technology. The design goal has a specific threshold to avoid future problems. A Telegram notification is sent every 10 seconds when the water condition exceeds the threshold. The overall accuracy rate of 98.81%, pH of 96.6%, and salinity of 99.17% demonstrate that the system works correctly
Open Artificial Intelligence Analysis using ChatGPT Integrated with Telegram Bot
Full text linkChatbot technology uses natural language processing with artificial intelligence that can interact quickly in answering a question and producing relevant answer. ChatGPT is the latest chatbot platform developed by Open AI which allows users to interact with text-based engines. This platform uses the GPT-3 (Generative Pre-trained Transformer) algorithm to help understand the response humans want and generate relevant responses. Using the platform, users can find answers to their questions quickly and relevantly. The method used for OpenAI\u27s research on ChatGPT integrated through Telegram chatbot is using a waterfall method which utilizes open API tokens from Telegram. In this research we develop OpenAI application connected with telegram bot. This application can help provide a wide range of information, especially information related to the Semarang State Polytechnic. By using Telegram chatbot in the program, users can find it easy to ask because it is integrated with OpenAI using the API. Telegram chatbot, which has a chat feature, allows easy communication between users and chatbots. Thus, it may reduce system errors on the bot
The Utilization of Deep Learning in Forecasting The Inflation Rate of Education Costs in Malang
Full text linkThe public needs information about the predicted inflation rate for education costs to manage family finances and prepare education funds. This information is also beneficial for the government to create policies in education. Malang is one of the educational cities in Indonesia, but research on the prediction of the inflation rate of education costs in the city still needs to be made available. Besides, the researchers have yet to find previous studies on forecasting that used the specific inflation rate for education costs in Indonesia by applying the Deep Learning method, especially those using the Consumer Price Index (CPI) data for the Education Expenditure Group. This research aims to develop a model to forecast the inflation of education costs in Malang using the Deep Learning Method. This research was conducted using Consumer Price Index (CPI) data for the Education Expenditure Group in Malang during 1996-2021s taken from the Central Bureau of Statistics (BPS) Malang. The forecasting method used is the Long and Short-Term Memory (LSTM) method, which is a development of the Recurrent Neural Network (RNN). The results showed that it achieved the best accuracy by a model with one hidden layer and four hidden nodes, namely MAPE=2.8765% and RMSE=8.37