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All Journal Mechatronics, Electrical Power, and Vehicular Technology JFA (Jurnal Fisika dan Aplikasinya) JOURNAL OF MECHANICAL ENGINEERING, MANUFACTURES, MATERIALS AND ENERGY Journal of Physics: Theories and Applications Syntax Literate: Jurnal Ilmiah Indonesia Jurnal ULTIMA Computing Journal of Renewable Energy and Mechanics Journal of Educational Chemistry (JEC) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) International Journal of Renewable Energy Development Journal of Mechatronics and Artificial Intelligence
Elysa Nensy Irawan
Universitas Pendidikan Indonesia
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Other

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Journal : Mechatronics, Electrical Power, and Vehicular Technology

 1 
Analyzing the growth and trends of vertical axis wind turbine research: Insight from a bibliometric study Elysa Nensy Irawan; Nuur Wachid Abdul Majid; Liptia Venica; Fahrur Aslami; Goro Fujita
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 1 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.55-61

Abstract

Bibliometric analysis research has been done for vertical axis wind turbine (VAWT). This study aims to determine the growth of VAWT research, the number of VAWT studies in various countries and the most influential authors to find opportunities for research collaboration, and the challenges of future VAWT research. Research data was taken from Scopus in 1801 articles from 1970-2021. The software used for data interpretation was VosViewer 1.6.19 and Tableau Public 2022.2. Based on the analysis, VAWT research has tended to increase from 1970-2021, although there was a decrease from 1987-2006. The country that has conducted the most VAWT research is China, while the author with the highest number of citations is from Italy. The most dominant research topic related to VAWT research is computational fluid dynamics (CFD), which is 50.14 % of the total. A future challenge related to VAWT research is finding a suitable turbulence model for each type of VAWT or finding an airfoil optimization method so that a model with betterperformance is obtained. Opportunities for research collaboration can be carried out with China or an author with the highest number of citations who has expertise in the field of CFD.
Smart watering of ornamental plants: exploring the potential of decision trees in precision agriculture based on IoT Pratama, Hafiyyan Putra; Hadi Putri, Dewi Indriati; Putri, Hafiziani Eka; Irawan, Elysa Nensy; Kautsar, Makna A’raaf
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.963

Abstract

Ornamental plant farmers face various challenges due to climate change and environmental stress that significantly affect plant health and growth. This research overcomes these challenges by developing an intelligent watering system that uses internet of things (IoT) technology and decision trees (DTs) algorithms to optimize the use of planting land by ensuring plants grow in the most optimal conditions, both in terms of water and nutrients and increase land productivity. The system is built by integrating various sensors to monitor soil moisture, air humidity, temperature, and light intensity in real-time. The collected data is used to automate watering schedules and provide recommendations on suitable plant species based on the soil nutrient content of nitrogen (N), phosphorus (P), and potassium (K). The use of the DTs algorithm helps in analyzing the data from the sensors and providing recommendations on the most suitable plants for the land. The smart watering system was tested in three zones, each simulating a different watering scenario, and successfully maintained optimal conditions for plant growth in each zone. The machine learning (ML) model with the DTs algorithm can predict the right type of ornamental plants based on the existing land conditions in three watering zones, with an accuracy of 89 %, 90 %, and 91 %, respectively. Furthermore, farmers can follow these recommendations to minimize damage and death of plants so that the level of productivity on the land becomes optimal.
Co-Authors Ahmad Syahdi Al Khawarizmi Aldo Mahendra Putra Aldo Mahendra Putra Athorn Vora-Ud Dendy Satrio Diky Zakaria Dwa Desa Warnana Elyas Nur Fridayana Fahrur Aslami Fujita, Goro Galura Muhammad Suranegara Goro Fujita Hadi Putri, Dewi Indriati Hafiziani Eka Putri, Hafiziani Eka Kai Shibuya Kautsar, Makna A’raaf Kunchit Singsoog Liptia Venica Mochamad Sahal Muhammad Husni Muttaqin Muhammad Matthew Janotama Mulya, Tri Seda Muntaha, Mohammad Muthmainnah, Melania Suweni Muntini Nguyen Hong Minh Giang Nuur Wachid Abdul Majid Pratama, Hafiyyan Putra Putri, Aisyah Aira Rizky Hamdani Sakti Sakti, Rizky Hamdani Salman, Fauzie Shibuya, Kai Somporn Thaowankaew Suyatno Tosawat Seetawan Tosawat Seetawan4 Wanatchaporn Namhongsa Yamashita, Ken-Ichiro Yoyok Setyo Hadiwidodo