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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknik Terapan (J-TETA)
Nurazaq, Warit Abi
Unknown Affiliation
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Other

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Design and Development of an Off-Grid Solar Power Monitoring System on A 160 Wp PV System at SMPN 04 Tempurejo, Jember Regency Ramadhan, Fila; Rachmanita, Risse Entikaria; Mulyono, Novangga Adi; Nurazaq, Warit Abi; Hasbiyati, Haning
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 22, No 1 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v22i1.32975

Abstract

Solar energy can be converted into electrical power through photovoltaic (PV) technology, commonly referred to as solar panels.  As a result, the implementation of a monitoring system is crucial to mitigate battery degradation caused by excessive discharging. This study employs a research and development (R&D) approach. The off-grid solar power monitoring system installed at SMPN 04 Tempurejo is designed to facilitate maintenance and enhance system reliability. By integrating Internet of Things (IoT) technology, the monitoring system allows for real-time tracking of voltage, current, and power output from the PV system via smartphone access, irrespective of location. Consequently, the IoT-based monitoring system significantly improves the management and oversight of the off-grid solar power system at SMPN 04 Tempurejo.
Artificial Neural Network Approach for Estimating Biochar Yield from Biomass Composition and Pyrolysis Temperature Nurazaq, Warit Abi; Pambudi, Suluh; Mulyono, Novangga Adi
Jurnal Teknik Terapan Vol. 4 No. 1 (2025): April
Publisher : P3M Politeknik Negeri Jember

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Abstract

Biochar yield from biomass pyrolysis is influenced by complex interactions among feedstock properties and pyrolysis conditions. This study proposes the generation of an artificial neural network (ANN) model to predict biochar yield using input variables including volatile matter, fixed carbon, ash content, elemental composition (C, H, O, N), and temperature on pyrolysis process. A multilayer perceptron (MLP) network was trained using experimental data collected from various biomass sources. The model achieved high performance, with correlation coefficients (R2) of 0.98812 for training, 0.96529 for validation, and 0.94148 for testing. Mean squared error (MSE) analysis showed optimal validation performance at epoch 31, while the error histogram and regression plots confirmed strong predictive accuracy across all datasets. These results demonstrate that ANN is a powerful tool for modeling biochar production, offering a reliable and efficient alternative to labor-intensive experimental methods.
Co-Authors Hasbiyati, Haning Mulyono, Novangga Adi Pambudi, Suluh Rachmanita, Risse Entikaria Ramadhan, Fila