Syatauw, Lukas (2016) Prediksi Ketersediaan Benih Padi (Oryza Sativa.L) Di Provinsi Maluku Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (Anfis) - Prediction Of The Rice Seeds Availability (Oryza Sativa.L) In The Province Of Maluku By Using Adaptive Neuro Fuzzy Inferensi System (ANFIS) Method. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Sistem perbenihan yang tangguh (produktif, efisien, berdaya saing dan
berkelanjutan) sangat diperlukan untuk mendukung upaya peningkatan produksi benih
dan mutu produk pertanian. Penelitian ini bertujuan untuk memprediksi ketersediaan
benih padi yang ada di Provinsi Maluku.dari data yang tersedia yaitu variabel Sisa
periode lalu, tambah stok, jumlah stok, tersalur dan sisa stok. Variabel tersebut
kemudian diolah membentuk tiga selang linguistik yaitu rendah, sedang dan tinggi
kemudian diolah menggunakan Adaptive Neuro Fuzzy (Anfis) yang membentuk lima
Member Function input dan satu output. Hasil dari proses training anfis menunjukan
bahwa pada iterasi kedua telah mencapai eror konstan sebesar 0,057018 dan nilai eror
rata-rata 0,0015364. Perhitungan tersbut kemudian dibandingkan dengan data aktual
yang ada untuk menghitung MAPE dan RMSE diperoleh hasil MAPE sebesar
5.316884, dan RMSE sebesar 28.28583 dengan demikian sistim yang dipergunakan
cukup valid dan dapat dipergunakan untuk menentukan prediksi ketersediaan padi.
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A formidable seeding process (productive, efficient, competitive and continuous
seeding process) is highly necessary to support the effort to increase seeds production
quantity and the quality of agricultural product. Our propose is done to predict the
availability of rice seeds in Maluku by using Adaptive Neuro Fuzzy (Anfis)which
forming five Members Function input and one output. from the availability data, which
variable of the last period, additional stock, total stock, is channeled and the last stock.
The variable then process to be 3 pipes of linguistic which are low, medium, and high
by using the Adaptive Neuro Fuzzy (Anfis) which make five input member functions
and one output. The result of this anfis training process shows that the constant error
value of 0,057018 and the average error value of 0,0015364have been reached on the
second iteration. This calculation, which used artificial intelligence, is then compared
to the actual data to calculate MAPE and RMSE which are gained from the MAPE
result of 5.316884, and RMSE as much as 28.2858; thus, the system used here is valid
and can be used to determine the prediction of rice seeds availability.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTE 629.229 3 Sya p |
| Uncontrolled Keywords: | Prediksi, Anfis, benih padi, Prediction, Anfis, Rice Seeds |
| Subjects: | T Technology > TJ Mechanical engineering and machinery > TJ217.6 Predictive Control |
| Divisions: | Faculty of Industrial Technology > Electrical Engineering |
| Depositing User: | ansi aflacha |
| Date Deposited: | 17 Dec 2019 02:42 |
| Last Modified: | 17 Dec 2019 02:42 |
| URI: | http://repository.its.ac.id/id/eprint/72384 |
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