KLASIFIKASI KECOCOKAN TANAMAN DENGAN PARAMETER DATA PH DAN KELEMBABAN TANAH MENGGUNAKAN ALGORITMA RUN LENGTH ENCODING (RLE) DAN MULTILAYER PERCEPTRON (MLP) BERBASIS INTERNET OF THING (IOT)

PRATAMA, YOGI TIARA and Sukemi, Sukemi and Tutuko, Bambang (2024) KLASIFIKASI KECOCOKAN TANAMAN DENGAN PARAMETER DATA PH DAN KELEMBABAN TANAH MENGGUNAKAN ALGORITMA RUN LENGTH ENCODING (RLE) DAN MULTILAYER PERCEPTRON (MLP) BERBASIS INTERNET OF THING (IOT). Undergraduate thesis, Sriwijaya University.

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Abstract

Terdapat peningkatan penggunaan kemajuan teknologi khusunya dalam bidang kecerdasan buatan dan Internet of Things (IoT). Penggunaan kecerdasan buatan dalam bidang pertanian seperti Internet of Things (IoT) dapat mengoptimalkan hasil panen petani, misalnya aplikasi pemantauan Ph dan Kelembahan yang membuat petani mengatahui nilai Ph dan Kelembaban secara real time dan menampilkan tanaman apa yang cocok pada daerah tersebut berdasarkan pengukuran Ph dan Kelembabannya. Acuannya adalah beberapa kumpulan dataset yang memiliki 2200 instance dan 2 atribut serta 22 tanaman berbeda yang dapat direkomendasikan. Dengan menggunakan metode pembelajaran Run-length encoding (RLE) untuk kompresi data agar data dapat dikirim secara realtime tanpa mengalami kendala dan klasifikasi menggunakan algoritma multi-layer perceptron hasilnya akan ditampilkan pada website sehingga petani dapat memantau keadaan ph dan kelembaban tanah secara real-time serta menentukan tanaman apa yang cocok untuk keadaan tanah tersebut.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: RLE, MLP, Internet of Things, Smart Agriculture
Subjects: Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
Divisions: 09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User: Yogi Tiara Pratama
Date Deposited: 25 Sep 2024 02:44
Last Modified: 25 Sep 2024 02:44
URI: http://repository.unsri.ac.id/id/eprint/157996

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