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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) EPI International Journal of Engineering Jurnal Pengabdian UNDIKMA Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat Bangsa
Putri Ayu Maharani
Institut Teknologi Bacharuddin Jusuf Habibie
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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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IoT-Based Irrigation System Using Machine Learning for Precision Shallot Farming Mardhiyyah Rafrin; Muh. Agus; Putri Ayu Maharani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5579

Abstract

Despite the massive production of shallots in Enrekang Regency, South Sulawesi Province, Indonesia, the cultivation method is still very conventional. Shallot cultivation is very challenging because it requires precision irrigation and pest prevention. In this research, we proposed a smart irrigation system to help farmers manage irrigation with more efficient water usage without hampering their pest prevention. The results of the system were three options: 1) no water needed, 2) water is required and is efficient for irrigation, and 3) water is required but it is not efficient for irrigation. We used Wireless Sensor Networks and IoT to collect yield parameters, designed a firebase database, and developed a mobile application and a web service embedded with a machine learning application. All applications interacted using the representational state transfer application programming interface. The proposed system architecture successfully gathered cropland data and distributed them to all applications within the system. Furthermore, we analyzed four supervised learning algorithms (decision trees, random forest, gradient boosting, and K-Nearest neighbor), and the random forest was deployed in the web service because it outperformed other algorithms with a precision of 94% and an AUC score of 0.90.
Co-Authors Eka Qadri Nuranti Gunadin, Indar Chaerah Indar Chaerah Gunadin M. Fauzan Iskandar Mahdaniar Mahdaniar Mar'atuttahirah Mar'atuttahirah Mardhiyyah Rafrin Mardhiyyah Rafrin Muh. Abubakar Tunru Muhammad Aldi Alfatih Muhammad Faruq Al-Fauzi S. Muhammad Rivaldi Jefri Naili Suri Intizhami Nur Inaya Bahar Osama Iyad Al Ghozy Sitti Rahma Yunus