PREDIKSI HASIL PANEN PADI KABUPATEN & KOTA DI PROPINSI NUSA TENGGARA TIMUR DENGAN FUZZY INFERENCE SYSTEM (FIS)

Authors

  • Yampi R. Kaesmetan

DOI:

https://doi.org/10.52972/hoaq.vol10no1.p42-48

Keywords:

Rice, Fuzzy Inference System, FIS, MAPE, Matlab 2012

Abstract

Rice (Oryza sativa) is a staple food source for the people of Indonesia. Most of the rice consumed is the result of national rice productivity. Often the government has difficulty in estimating the adequacy of basic food items that can be provided by domestic agriculture. Therefore a method is needed to predict rice yields accurately and precisely. The agricultural sector in East Nusa Tenggara is not a flagship of the community's economic activities. This is due to the geographical conditions of NTT which are less supportive for business activities in the agricultural sector. Even so, the prediction of agricultural products, especially rice yields, is needed to be predicted so that a forecast can be obtained in determining rice yields in 2017.  Fuzzy logic method in this case Fuzzy Inference System (FIS) is widely applied for forecasting or prediction. Fuzzy logic has a slowness in predicting crop yields for the following year based on crop yields in the previous year and information taken from the fuzzy information provided. Fuzzyinformation can be made a rule or rule as a consideration in predicting yields. By using the formula of Mean Absolute Percentage Error (MAPE) or Average Absolute Error, from the Fuzzy Mamdani model The Fuzzy Inference System (FIS) with the Mamdani model that has been built can be used to estimate the amount of rice production in the City District in NTT with the truth value reaching 97.8%. To determine the amount of rice production in 2017, the data is processed by using the help of the Matlab 2012 fuzzy toolbox software using the centroid method for defuzzification.

References

Abdurrahman, Ginanjar. 2011. Penerapan metode Tsukamoto (Logika Fuzzy) Dalam Sistem Pendukung Keputusan Untuk Menentukan Jumlah Produksi Barang Berdasarkan Data Persediaan dan Jumlah Permintaan. Jurnal Universitas Negeri Yogyakarta.

Ginting, Rosnani. 2007. Sistem Produksi. Yogyakarta: Graha Ilmu 2018.

Engelbrecht, A.P (2007). Computational Intelligence an Introduction. John Willey&Sons Ltd. England

Jang, J.S.R, Tsai-Sun.C, Mizutani, E. (1997). Neuro Fuzzy and Soft Computing. Prentice Hall International, New Jersey

Haryati, N.E. Perencanaan Jumlah Produk Menggunakan Fuzzy Mamdani Berdasarkan Prediksi Permintaan.

Ika, K. 2007. Sistem Pendukung Keputusan Penanganan Kesehatan Balita Menggunakan Fuzzy Mamdani.

Kartina, D. 2010. Penerapan Inferensi Fuzzy Untuk Kendali Suhu Ruangan Pada Pendingin Ruangan (AC).

Kusumadewi, S. 2002. Analisis Desain Sistem Fuzzy Menggunakan Tool Box Matlab. Yogyakarta : Graha Ilmu.

Kusumadewi, S. dan Purnomo. 2006. Aplikasi Logika Fuzzy Untuk Mendukung Keputusan. Yogyakarta : Graha Ilmu.

Makridakis. 1999. Metode Aplikasi Peramalan Edisi Kedua Jilid Satu. Jakarta Barat : Binarupa Aksara.

Octavia, M. Perencanaan Jumlah Produksi Meja Aluminium Untuk Meminimalkan Biaya Produksi Dengan Menggunakan Fuzzy Mamdani.

Pal. K. Sankar. 1989. Fuzzy Pendekatan Matematik Untuk Pengenalan Pola.

Jakarta : Universitas Indonesia.

Setiadji. 2009. Himpunan dan Logika Samar serta Aplikasinya. Yogyakarta : Graha Ilmu.

Susilo, Frans. SJ. 2006. Himpunan dan Logika Kabur serta Aplikasinya. Yogyakarta : Graha Ilmu.

Sutikno. Perbandingan Metode Defuzzifikasi Aturan Mamdani Pada Sistem Kendali Logika Fuzzy (Studi Kasus Pada Pengaturan Kecepatan Motor DC).

Yulyantari, 2011. Computer Aided Instruction Artificial Intelligence. . diakses tanggal 25 April 2019.

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Published

31-05-2018

How to Cite

Kaesmetan, Y. R. . (2018). PREDIKSI HASIL PANEN PADI KABUPATEN & KOTA DI PROPINSI NUSA TENGGARA TIMUR DENGAN FUZZY INFERENCE SYSTEM (FIS). HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi, 10(1), 42–48. https://doi.org/10.52972/hoaq.vol10no1.p42-48