Model Decision Tree Forecasting Berbasis DHT22 pada Smart Hydroponic Microgreen
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Abstract
Sensor DHT22 diharuskan aktif selama 4 hari penuh, jika dilihat dari waktu rata-rata budidaya microgreen kacang hijau menggunakan smart hydroponic. Penggunaan sensor DHT22 dalam jangka waktu tertentu dapat mempengaruhi kestabilan jangka panjang dalam hal pembacaan suhu. Jika pembacaan DHT22 mulai tidak akurat, smart system tidak mampu bekerja maksimal. Peneliti mengusulkan sebuah sistem untuk menggantikan peran sensor tanpa mengurangi atau mengganggu kinerja dari smart system untuk mengurangi ketergantungan komponen elektronika seperti sensor suhu. Seiring kemajuan teknologi, terdapat salah satu model machine learning yang dapat diterapkan untuk menggantikan peran sensor yaitu prediksi suhu melalui forecasting. Algoritma forecasting yang digunakan adalah decision tree. Algoritma ini dipilih karena mampu memprediksi data hanya dengan satu jenis input data berupa waktu dengan proses pelatihan yang cukup cepat. Data latih dihasilkan dari perekaman data selama 4 hari pada smart hydroponic microgreen kacang hijau. Model akan dibuat menggunakan grid search cross validation dan feature scaling. Hasil penelitian menunjukkan channel 80 layak dipilih menjadi model prediksi. Prediksi suhu model decision tree forecasting menghasilkan nilai R-squared sebesar 0,870955 dan mean square error (MSE) sebesar 0,074171. Kedua nilai tersebut menunjukkan bahwa model cukup kuat dalam memprediksi suhu dan layak untuk diterapkan dalam memantau suhu smart hydroponic microgreen kacang hijau.
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