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All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics INDONESIAN JOURNAL OF APPLIED PHYSICS Pascal: Journal of Physics and Science Learning Journal of Applied Engineering and Technological Science (JAETS) International Journal of Engineering, Technology and Natural Sciences (IJETS) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Penelitian Pendidikan Fisika JTEIN: Jurnal Teknik Elektro Indonesia Ability : Journal of Education and Social Analysis Indonesian Journal of Engineering and Science (IJES) Andalasian International Journal of Applied Science, Engineering, and Technology Mejuajua INVENTION: Journal Research and Education Studies Jurnal Informatika Teknologi dan Sains (Jinteks) Aceh International Journal of Science and Technology Riwayat: Educational Journal of History and Humanities Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Jurnal Penelitian Pendidikan Fisika Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Indri Dayana
Universitas Medan Area
Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Machine learning for energy conversion prediction and photovoltaic-on grid protection system using IoT Habib Satria; Muhammad Fadlan Siregar; Indri Dayana; Dadan Ramdan; Hermansyah Hermansyah; Muhammad Irwanto; Syafii Syafii
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i2.pp416-425

Abstract

The advancement of photovoltaic (PV) systems in tropical regions faces significant efficiency challenges due to fluctuating panel surface temperatures. This study addresses these issues by implementing machine learning (ML) models, specifically k-nearest neighbors (KNN) and extreme gradient boosting (XGBoost), to classify and monitor panel temperatures. To enhance system resilience, an internet of things (IoT) based on-grid protection system was developed, featuring a dual-relay redundancy mechanism that triggers an automated trip when the current exceeds 1.30 A. This integration ensures the protection of both the PV infrastructure and household electrical loads. Experimental results demonstrate that the KNN model exhibits superior reliability with a testing accuracy of 93% and a baseline performance of 96.67%, successfully identifying both normal (25 °C to 35 °C) and high-temperature (36 °C to 48 °C) states. In contrast, while the XGBoost model reached a maximum validation accuracy of 94.44% during training, it only achieved a testing accuracy of 84% and showed significant limitations in detecting normal temperature patterns. Beyond classification, the IoT framework proved highly precise in real-time energy monitoring, with sensor error rates below 2%. This research offers a strategic solution for optimizing energy conversion and system reliability, providing a robust framework for sustainable clean energy management in tropical climates.
Multi-level redundancy with internet of things battery supply for fault mitigation in grid-tied photovoltaic systems Habib Satria; Muhammad Fadlan Siregar; Indri Dayana; Dadan Ramdan; Muhammad Irwanto; Syafii Syafii
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp622-633

Abstract

The development of grid-tied photovoltaic (PV) systems in tropical regions remains a strategic focus for achieving sustainable clean energy. However, energy conversion efficiency is often hampered by fluctuations in panel surface temperature and electrical faults. To ensure long-term system reliability, this study implements a multi-level redundancy architecture integrated with dynamic internet of things (IoT) monitoring for fault mitigation in grid-tied PV systems. The system employs a machine learning (ML) method using the k-nearest neighbors (KNN) algorithm for thermal classification, achieving an accuracy of 84% in identifying normal (25 °C to 35 °C) and overheating conditions. Furthermore, an electrical redundancy layer is designed with an automatic tripping mechanism that activates when the current exceeds a 1.30 A threshold, demonstrating a rapid response latency of 150 ms. To ensure monitoring resilience, the system is supported by a dedicated 18650 Li-ion battery backup. The implementation results confirm that this multi-level protection framework effectively monitors real time energy usage, prevents critical component damage, and enhances the overall safety of household-scale PV installations. This research provides a scalable and intelligent solution for fault mitigation, supporting the broader adoption of renewable energy in tropical environments.
The Effect of Tetraethil OrthoCilicate (TEOS) on Fe3O4 Nanoparticles Addition in Electrical Indri Dayana; Habib Satria; Martha Rianna
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 14, No 1 (2024): April
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v14i1.74558

Abstract

Magnetite Nanoparticles of pure (Fe3O4) and Fe3O4 with TEOS addition have been successfully synthesized from natural iron sand using the coprecipitation method. The purpose of this study is to provide information on the effect of TEOS to Fe3O4 on the electrical properties. The effect of TEOS addition to Fe3O4 indicates that the increase in true density results is 4.95 gr/cm3. The stability of nanolubricant on Fe3O4 nanoparticles with the addition of TEOS 1.2 ml was dispersed homogeneously. The value of thermal conductivity also increases due to TEOS addition on Fe3O4 nanoparticles in a volume fraction of 0.8% of 1,631 W/m.K and the heat of the type produced was 718.44 J/kg.K. The effect of TEOS addition on Fe3O4 nanoparticles produces good electrical properties of stability in the nano-lubricant.
Development of Potato-Derived Carbon Nanofibers as Sustainable Moisture-Regulating Supporting Layers for Photovoltaic Modules Indri Dayana; Habib Satria; Nidya Chitraningrum; Mega Puspita Sari; Tino Hermanto; Yopan Rahmad Aldori; Dadan Ramdan; Syofyan Anwar Syahputra; Dina Maizana; Moranain Mungkin; Junaidi Junaidi; Ade Irma Sagala; Siti Utari Rahayu
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9175

Abstract

Moisture-induced degradation remains a critical reliability issue in photovoltaic (PV) modules, particularly in humid tropical climates. While biomass-derived carbon materials have been widely investigated for electrochemical applications, their use as functional supporting layers in PV modules remains limited. This study develops potato-derived carbon nanofibers through moderate-temperature carbonization (700 °C, N₂ atmosphere) followed by electrospinning. Structural and chemical properties were evaluated using XRD, FTIR, Raman spectroscopy, and BET surface area analysis. Morphology and fiber diameter distribution were examined via SEM. Electrical conductivity was measured using a four-point probe method. Water vapor permeability (WVP) and biodegradation behavior were assessed to evaluate durability and moisture regulation capability. The synthesized material exhibits predominantly amorphous turbostratic carbon with characteristic D and G Raman bands (ID/IG ≈ 0.92), specific surface area of 186 ± 12 m²/g, and electrical conductivity of 3.8 ± 0.4 S/m. Nanofiber diameters range from 72–108 nm (mean ± SD). Controlled WVP (3200–4100 mg/day/L) and moderate biodegradability (8–12% mass loss over 14 days) indicate balanced vapor diffusion and structural integrity. The results demonstrate the feasibility of potato-derived carbon nanofibers as sustainable moisture-regulating supporting layers in photovoltaic modules.
Co-Authors Ade Irma Sagala Adi Rahwanto Aldhi, Muhammad Anwar Syahputra, Sofyan Anwar Syaputra, Sofyan Ayu Fitriani Dadan Ramdan Denny Meisandy Hutauruk Dian Inda Sari Dian Noviandri Dina Maizana Fuadaturrahmah, Fuadaturrahmah Habib Satria Habib Satria Habib Satria Habib Satria Habib Satria Habib Satria Hermansyah Hermansyah Hermansyah Hermansyah Hermansyah Hermansyah Hermansyah Hermansyah Hermansyah Hutauruk Hutauruk Ilham Maulana Indawan, Isnanda Irwan Irwan Irwan Irwan Irwanto, Muhammad Juliaster Marbun Junaidi Junaidi Jusoh, Muzzammil Mahpuja Narti Simatupang Martha Rianna Martha Rianna Martha Rianna Mega Puspita Sari Moranain Mungkin Muhammad Fadlan Siregar Muhammad Fauzi Muhammad Ikhwan Fahmi Muhammad Irwanto Muhammad Irwanto Muhammad Khahfi Zuhanda Nidya Chitraningrum Ninny A Siregar Nuril Mahda Nurwijayanti Parluhutan Panjaitan Permata Sari Siregar, Ade Puspita Sari, Mega Rana Fathinah Ananda Ridwan Arrasyid Rudi Salam Rudi Salam Sari, Mega Puspita Sijabat, Tonggo Siti Utari Rahayu Syafii . Syafriwel, Syafriwel Syah, Rahmad B. Y. Syofyan Anwar Syahputra Tino Hermanto Vina Winda Sari WINATA, SATRIA ANANDA Yenny Agnes Angela Turnip Yoga Tri Nugraha Yopan Rahmad Aldori Yuana Delvika Yudi Daeng Polewangi Zakaria, Zulkarnay Zulkifli Bahri, Zulkifli