Pengembangan Sistem Prediksi Hama Wereng Berdasarkan Data Cuaca Sensor Dan Cuaca Online Menggunakan Metode Naive Bayes

Santoso, Rudy Agus (2018) Pengembangan Sistem Prediksi Hama Wereng Berdasarkan Data Cuaca Sensor Dan Cuaca Online Menggunakan Metode Naive Bayes. Sarjana thesis, Universitas Brawijaya.

Abstract

Padi (Oryza sativa L) adalah komoditas penting dalam bidang pertanian karena beras merupakan kebutuhan pokok rakyat Indonesia. Hama wereng coklat (Nilaparvata lugens) merupakan salah satu kendala yang menyebabkan keringnya tanaman padi sehingga berdampak pada menurunnya hasil panen. Pada penelitian ini akan dikembangkan sistem untuk memprediksi hama wereng berdasarkan parameter suhu, kelembaban dan curah hujan menggunakan metode Naive Bayes . Data uji fitur suhu dan kelembaban didapatkan dari sensor DHT11 sedangkan data curah hujan didapatkan secara online dari penyedia situs cuaca melalui modul ESP8266. Data latih keadaan serangan hama dan cuaca didapatkan dari lembaga yang terkait dengan serangan hama wereng, dan informasi data cuaca. Dari data uji dan data latih tersebut akan diproses oleh Mikrokontroler Arduino Uno untuk memprediksi ada atau tidaknya hama wereng menggunakan metode Naive Bayes . Pada pengambilan data uji yang dilakukan sensor DHT11, hasil pembacaan fitur suhu dan kelembaban mempunyai nilai error sebesar 2,14 % untuk suhu dan 1,71 % pada kelembaban. Dalam pengujian pembacaan data curah hujan, terdapat perbedaan nilai antara situs cuaca dan BMKG dengan nilai error sebesar 22,51 %. Untuk pengujian akurasi klasifikasi Naive Bayes , didapatkan nilai akurasi sebesar 83,33% dengan 6 data uji dari 17 data latih. Pada pengujian akurasi dengan data uji dari sensor didapatkan nilai akurasi sebesar 85,71% dengan 19 data latih dan 21 kali pengambilan data uji.

English Abstract

Rice (Oryza sativa L) is an important commodity in agriculture because the needs for rice as the primary food of the Indonesian people. Brown planthopper (Nilaparvata lugens) is one of the obstacles that causes rice plants dry so that impact on the decreasing yields. This research will be developing a system to predict pests of planthopper based on parameters of temperature, humidity and rainfall using Naive Bayes method. Temperature and humidity features using test data that obtained from the DHT11 sensor while rainfall data was obtained online from an online weather site provider via ESP8266 module. The Data training of pest and weather conditions are obtained from institutions associated with Brown Grasshopper attack, and weather data information. The test data and training data will be processed by Arduino Uno Microcontroller to predict the presence of Brown Grasshopper using Naive Bayes method. In the test data taken by DHT11 sensor, the temperature and humidity feature readings have a good accuracy value with an error value of 2.14% for temperature and 1.71% on humidity. In testing of rainfall data readings, there is difference of value between weather site and BMKG with error value equal to 22,51%. To test the accuracy of Naive Bayes classification, obtained an accuracy of 83.33% with 6 test data from 17 data train. In testing the accuracy with the test data from the sensor obtained an accuracy of 85.71% with 19 training data and 21 times the data collection test.

Item Type: Thesis (Sarjana)
Identification Number: SKR/FTIK/2018/246/051801779
Uncontrolled Keywords: Tanaman padi, Hama wereng, Naive Bayes , cuaca, DHT11, ESP8266
Subjects: 000 Computer science, information and general works > 004 Computer science > 004.015 1 Finite mathematic
Divisions: Fakultas Ilmu Komputer > Teknik Informatika
Depositing User: Yusuf Dwi N.
Date Deposited: 24 May 2018 00:55
Last Modified: 27 Oct 2021 04:24
URI: http://repository.ub.ac.id/id/eprint/10942
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