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Prototype Phase Failure Detection Berbasis Internet Of Things (IoT) Arif Dwi Wahyudi; Charis Fathul Hadi; Ratna Mustika Yasi
Journal of Educational Engineering and Environment Vol. 4 No. 2 (2025): Journal of Educational Engineering and Environment
Publisher : Fakultas Teknik Universitas PGRI Banyuwangi

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Abstract

The three-phase system is applied to the electricity network supplied by PLN, starting from the generator to the low voltage network (JTR) in front of people's homes. The PLN network uses three-phase lines (R, S, T) and neutral (N), or often called ground. According to the term three-phase power, it consists of three live cables and one neutral cable. Usually three-phase power supplies have a voltage of 380 volts and are widely used in industry and factories. Previous research to detect Phase failure still used conventional methods so it was difficult to control. This once happened at a hospital where the researcher worked, which experienced damage to a 3 Phase electric motor and a 3 Phase submersible water pump due to the failure of one of the Phases of the electrical network without being recognized and controlled by the Phase failure system. Currently, much of the technology used in equipment is operated manually, so the efficiency in terms of time, energy and accuracy is not optimal. This research carried out the design and development of a prototype Phase Failure Detection system which, when applied, can function as a safety measure, can monitor or monitor in real time, and can also remotely control the function of a 3 Phase electrical network using an Android system based on the Internet of Things. . The Phase Failure Detection working system is that if there is a phase imbalance (R, S, T) that exceeds the value set by the software, it will provide information via LCD, LED indicator and buzzer (alarm). NodeMCU ESP8266 in real-time. The voltage measurement value on the LCD (Contactor input voltage) is close to the voltage value measured using the AVOmeter. From the results of the research, 8 measurements were carried out in each phase, including a voltage of 220V - 196V in each phase R, S, T, and showed a maximum voltage error value for each phase of 0.50%, namely at a phase voltage of 199 volts. The voltage value shown by the AVOmeter is relatively the same as the voltage value shown by the LCD, so it can be concluded that the research results in the form of a prototype of this tool have a high level of accuracy because the percentage value of the allowable deviation coefficient between phases (10% of the nominal voltage of 220 volts) not exceeded [1], and when there is a deviationin the voltage value between Phases that exceeds 10%, the device automatically cuts off the flow of each Phase
Design and Implementation of Ultraviolet Light Control on Corn Fodder Using Fuzzy Logic Method: Lighting Control Design, Mamdani Fuzzy Logic, Corn Fodder susilo; Adi Mulyadi; Ratna Mustika Yasi
Journal of Educational Engineering and Environment Vol. 4 No. 2 (2025): Journal of Educational Engineering and Environment
Publisher : Fakultas Teknik Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/jeee.v4i2.7835

Abstract

This paper discusses the design and implementation of ultraviolet light control in corn fodder. The problem with corn fodder is that temperature and humidity and inappropriate lighting can trigger the growth of mold. The ultraviolet light illumination control system method uses a fuzzy logic method to control ultraviolet light illumination. So it affects temperature and humidity, as well as growth of corn fodder. The results of the comparison of the Mamdani fuzzy control system using Matlab and Arduino applications to control corn fodder illumination obtained defuzification values (0.5, 2.5, 6.5). The implementation of the Mamdani fuzzy logic control system affects the light, temperature and humidity, as well as the height and weight of the corn fodder with a definition error of 0.5%. Lighting 5 hours/day on a full spectrum grow light (380nm-730nm) with a light intensity of 2053lux, for 14 days produces a temperature of 27˚C and humidity of 93%, fodder height reaches 28cm with a total weight of 124 grams. Meanwhile, the grow light spectrum (450nm-460nm) with a light intensity of 1462.5 lux produces a temperature of 28˚C and humidity of 85%, the height of the fodder reaches 26.5cm with a total weight of 120 grams. Optimum lighting results were obtained in 5 hours of light with a full spectrum grow light lamp with a light intensity of 2053lux, for 14 days resulting in a temperature of 27˚C and humidity of 93%, the height of the fodder reached 28cm with a total weight of 124 grams.
Electricity Consumption Forecasting Analysis using Linear Regression Method, DKL 3.2 Method and BaU Scenario Muhammad Zainal Roisul Amin; Ratna Mustika Yasi; Wahyu Setyo Aji; Bambang Sri Kaloko
Journal of Educational Engineering and Environment Vol. 5 No. 1 (2026): Journal of Educational Engineering and Environment
Publisher : Fakultas Teknik Universitas PGRI Banyuwangi

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Abstract

Accurate long-term electricity demand forecasting is essential for ensuring reliable power system planning and sustainable energy development. Previous studies have generally focused on the application of a single forecasting approach, such as linear regression, or a limited comparison between two methods, resulting in insufficient evaluation of forecasting performance across multiple sectors and forecasting models. This research addresses this gap by conducting a comparative analysis of three forecasting approaches Multiple Linear Regression, Electricity Demand List Method 3.2 (DKL 3.2), and Business as Usual (BaU) for projecting electricity consumption in the ULP Kencong service area during the 2025–2029 period. The novelty of this study lies in the integration and comparison of these three forecasting methods within a single framework, combined with the utilization of customer growth, connected power capacity, and Land and Building Tax (PBB) indicators as forecasting variables. Forecasting simulations were performed using LEAP software and Microsoft Excel, while forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that all sectors are expected to experience continuous growth in electricity consumption, with the industrial sector showing the highest increase. Among the evaluated methods, Multiple Linear Regression demonstrated the best forecasting performance, achieving the lowest MAPE values in three of the four analyzed sectors, namely residential (3.49%), business (6.68%), and social (4.77%) sectors. In contrast, DKL 3.2 produced the highest forecasting errors, particularly in the industrial sector (45.98%), while BaU showed moderate and relatively stable performance. These findings support the claim that Multiple Linear Regression is the most suitable and accurate method for long-term electricity consumption forecasting in the ULP Kencong region, providing a reliable basis for future electricity supply planning and infrastructure development.