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ELKHA : Jurnal Teknik Elektro
Published by Universitas Tanjungpura
ISSN : 18581463     EISSN : 25806807     DOI : http://dx.doi.org/10.26418
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering
The ELKHA publishes high-quality scientific journals related to Electrical and Computer Engineering and is associated with FORTEI (Forum Pendidikan Tinggi Teknik Elektro Indonesia / Indonesian Electrical Engineering Higher Education Forum). The scope of this journal covers the theory development, design and applications on Automatic Control, Electronics, Power and Energy Systems, Telecommunication, Informatics, and Industrial Engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 12 Documents
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Search results for , issue "Vol. 17 No.1 April 2025" : 12 Documents clear
Analysis of Photovoltaic Panel Performance Integrated with the Grid in a Load-Sharing Scheme Aditya, Agellio Farras; Ulinuha, Agus
ELKHA : Jurnal Teknik Elektro Vol. 17 No.1 April 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i1.91191

Abstract

This paper examines the performance of photovoltaic panels integrated into the electrical grid in a load-sharing scheme at Darool Ehsan Muhammadiyah Senior High School, Sragen. The study is part of Universitas Muhammadiyah Surakarta's community service program focused on renewable energy development in educational environments. Unlike previous studies relying on simulated data, this research uses real-time primary data from direct measurements of photovoltaic power production and grid electricity consumption over a specific period. The study"™s innovation lies in analyzing the photovoltaic system within a tropical climate-based load-sharing scheme and comparing energy usage when connected to photovoltaic panels versus when disconnected. It also evaluates the impact of reducing solar energy consumption from the grid. The collected data reveal an estimated daily load of 49.89 kWh, with PLN-supplied energy consistently exceeding inverter-supplied energy. Integration of photovoltaic panels into the grid reduces PLN electricity consumption by up to 30% during optimal sunlight periods, achieving an average system efficiency of 15%. This research offers valuable insights into the potential and challenges of implementing photovoltaic panels in educational settings, emphasizing the importance of climate considerations in system design and optimization. Future studies will focus on techno-economic evaluations of solar power installations and harmonic distortion (THD) analysis for larger-scale implementations.
Wireless Sensor Sub-Network Based IoT System for Probiotic Dosing and Water Quality Management Using Artificial Neural Network Junfithrana, Anggy Pradiftha; Suryana, Anang; Saputri, Utamy Sukmayu
ELKHA : Jurnal Teknik Elektro Vol. 17 No.1 April 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i1.96914

Abstract

Water quality management in aquaculture plays a crucial role in maintaining fish health and optimizing growth, particularly in intensive tilapia farming. This study develops a Wireless Sensor Sub-Network (WSSN) based Internet of Things (IoT) system designed to automate probiotic dosing and monitor water quality conditions using real-time sensor feedback and Artificial Neural Network (ANN) analysis. Utilizing TCS3200 color sensors, flow sensors, and an ANN within a WSSN, the system autonomously manages probiotic delivery based on real-time water color analysis, marking a shift towards intelligent water-quality management in aquaculture. The system architecture consists of three primary sensing and control components: a flow sensor connected to an ESP32 microcontroller to measure the precise volume (in milliliters) of probiotic solution dispensed; a TCS3200 color sensor, also integrated with an ESP32 module, to detect variations in water color as an indicator of pond health; and a solenoid valve controlled through a relay-actuated ESP32 node to regulate probiotic release into the pond. The sensor network operates wirelessly to provide continuous monitoring and intelligent decision-making. The ANN employs the backpropagation algorithm to perform color-based classification, where light green indicates a healthy condition, dark green represents normal stability, light brown signals the need for probiotic dosing, and dark brown denotes a critical condition requiring water replacement. This integration of optical and flow sensing with neural network computation provides an intelligent, non-invasive, and adaptive mechanism for probiotic management in tilapia aquaculture, supporting sustainable aquaculture practices and improving operational efficiency through automation and predictive learning.

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