Rancang Bangun Sistem Pemetaan Kebutuhan Air Irigasi Secara Real Time Menggunakan Metode K-Means Clustering

Azmi, Dzulfikar Fakhri (2022) Rancang Bangun Sistem Pemetaan Kebutuhan Air Irigasi Secara Real Time Menggunakan Metode K-Means Clustering. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Irigasi bertekanan merupakan metode irigasi yang paling efisien saat ini. Pembagian area irigasi kedalam beberapa sektor meningkatkan efisiensi pemakaian energi, karena kebutuhan pompa lebih kecil dengan pengaturan irigasi yang bergantian. Pemberian air ke tanaman dengan sinyal balikan kondisi kelembaban tanah menjadikan irigasi lebih akurat dan efisien. Namun, sebuah area irigasi memungkinkan memiliki kandungan air yang berbeda antara satu lokasi dengan yang lain. Oleh karena itu, diperlukan sistem pemetaan area irigasi yang akurat berbasis sensor kelembaban tanah untuk meningkatkan efisiensi dan akurasi irigasi. Pada tugas akhir ini telah didesain dan diimplementasikan sebuah sistem pemetaan kelembaban tanah secara real time menggunakan metode K-means clustering dan sensor kelembaban tanah. Sistem pemetaan ini terdiri atas server berbasis Raspberry pi, sensor kelembaban tanah, komunikasi data berbasis jaringan LoRaWAN dengan protokol MQTT. Sistem pemetaan ini diaplikasikan dalam sebuah miniatur irigasi pancaran. Hasil pengujian menunjukkan jarak maksimal pengiriman sinyal LoRa sejauh 503 meter dan metode K-means clustering mampu memetakan kelembapan tanah secara optimal menjadi 4 kluster dengan rata-rata silhouette score sebesar 0,628
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Pressurized irrigation is the most efficient irrigation method today. The division of the irrigation area into several sectors increases the efficiency of energy use, because the need for smaller pumps with alternating irrigation arrangements. Providing water to plants with a return signal of soil moisture conditions makes irrigation more accurate and efficient. However, an irrigation area may have a different water content from one location to another. Therefore, an accurate irrigation location system based on soil moisture sensors is needed to increase the efficiency and accuracy of irrigation. In this final project, a real time soil moisture vulnerability system has been designed and implemented using the K-means clustering method and soil moisture sensors. This deployment system consists of a Raspberry pi-based server, soil moisture sensor, LoRaWAN network-based data communication with the MQTT protocol. This difficulty system is applied in miniature jet irrigation. The test results show that the maximum distance for transmitting LoRa signals is 503 meters and the K-means clustering method can optimally reduce soil moisture to 4 clusters with an average silhouette score of 0.628

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Irigasi Pancaran, K-Means clustering, LoRaWAN, Sprinkler Irrigation, K-Means clustering, LoRaWAN
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T58.62 Decision support systems T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK351 Electric measurements. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5103.2 Wireless communication systems. Two way wireless communication T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105.546 Computer algorithms T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7871.674 Detectors. Sensors
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Dzulfikar Fakhri Azmi
Date Deposited:	06 Feb 2023 01:28
Last Modified:	06 Feb 2023 01:28
URI:	http://repository.its.ac.id/id/eprint/96105

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