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All Journal Mechatronics, Electrical Power, and Vehicular Technology Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini Faktor Exacta Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Elektronika dan Telekomunikasi Jurnal ULTIMA Computing TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Jurnal Sistem Cerdas DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) KURVATEK BERNAS: Jurnal Pengabdian Kepada Masyarakat REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Community Empowerment Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat)
Hadi Putri, Dewi Indriati
Universitas Pendidikan Indonesia
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Journal : mechatronics electrical power and vehicular technology

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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.
An evaluation of stereo vision for distance estimation using the SGBM algorithm in the CARLA simulator Sakti, Rizky Hamdani; Venica, Liptia; Putri, Dewi Indriati Hadi; Kosmaga, Shinta Rohmatika; Rijanto, Estiko
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1284

Abstract

This paper presents an evaluation of stereo vision based on the semi-global block matching (SGBM) algorithm for distance estimation in an autonomous parking scenario using the CARLA simulator. Distance-disparity regression functions are explored to enhance distance estimation accuracy. The proposed distance estimation model was evaluated using the design science research methodology (DSRM) framework, with experimental validation conducted in CARLA’s promenade environment. The evaluation employed root mean square error (RMSE) and relative error metrics to assess performance. Experiments were performed within a range of 40-350 cm, which is relevant for autonomous parking applications. The experimental results show that the algorithm achieves an overall RMSE of 1.69 cm and an average relative error of 1.1 %. The findings contribute to the advancement of perception systems for autonomous vehicles, particularly in challenging environments.
Co-Authors Adhi, Himmawan Sapta Afifudin, Shodiq Agung Satria Pamungkas Ahmad Fauzi Alimudinsyah Alrasyid Andranetta Gracelynne Eka Pramudita, Eufrasia Baha’udin, Muhammad Dany Syauqi Nazhif Diana Eka Putri Diky Zakaria Dulhadi, Dulhadi Elviani, Ulva Elysa Nensy Irawan Endah Setyowati Endah Setyowati Estiko Rijanto Fauzi Ahmad Muda Fauzie Salman Galura Muhammad Suranegara Haffiyan Putra Pratama Hafiziani Eka Putri, Hafiziani Eka Hasanah, Muntaha Ichwan Nul Ichsan Kautsar, Makna A’raaf Kholidatuzzahra, Zamzam Kosmaga, Shinta Rohmatika Kurniawan, Muhammad Randy Liptia Venica Mahmudah Salwa Gianti Makna A'raaf Kautsar Makna A’raaf Kautsar Maranatha, Jojor Renta Maulana, Farhan Mohammad Arsyad Muhammad Bilal Hamzah Muhammad Rizalul Wahid Nina Herlina Nugraha, Ariel Dwika Nugroho, Yohanes Nuur Wachid Abdul Majid Pramudita, Eufrasia Andranetta Gracelynne Eka Pratama, Hafiyyan Putra Putra Pratama, Hafiyyan Rabbani, Raihan Fakhri Rezka Bunaiya Prayudha Rojabi, Alpiyan Arif Sakti, Rizky Hamdani Salmadiina, Ashrida Sari, Nadia Tiara Antik Sodikin, Reisa Aulia Subagja, Banda Sudjani Sudjani Sutia, Diar Dwi Suyanta Suyanta Syifaul Fuada Taufik Ridwan Taufik Ridwan, Taufik Vormes Gema Merdeka Yohanes Adi Nugroho Zahra, Hana Fatimah Zahron, Aulia Aufa