SVM ALGORITHM FOR PREDICTING RICE YIELDS

  • Nur Nafi'iyah Universitas Islam Lamongan
Keywords: SMV linear, prediksi hasil panen padi, normalisasi

Abstract

Agriculture in Indonesia is highly dependent on reservoir irrigation water sources and rain. Because some agricultural land in Indonesia is rainfed. Plants in Indonesia rely on water from rain and irrigation. Weather conditions greatly affect the number of farmers' harvest. Farmers often experience crop failures due to changing weather. From data from the Central Statistics Agency, it is stated that the number of rice yields in 2019 decreased by 7.76% compared to 2018. In order to avoid rice imports and rice food shortages, a breakthrough is needed that can help the government in making policies. One of the breakthroughs is creating a rice yield prediction system. The research process consisted of collecting data via the web: https://www.pertanian.go.id/home/?show=page&act=view&id=61. The data shows the variables of province, year, land area, production. The total number of data is 170 rows, with a division of 130 lines for training, and 40 for testing. Furthermore, the data is processed and processed and normalized. The results of data processing are then trained and predicted with a linear SVM kernel. The results of SVM prediction with original data without normalization of MAPE 6635.53%, and RMSE 1094810.74. The results of SVM prediction with normalized data first, the MAPE value was 9427.714%, and RMSE 0.017.

References

E. Hartanto, D. Sitorus and A. Wanto, "Analisis Jaringan Saraf Tiruan untuk Prediksi Luas Panen Biofarmaka di Indonesia," SEMANTIK, vol. 4, no. 1, pp. 49-56, 2018.

F. Maspiyanti and M. I. Fanany, "Klasifikasi Fase Pertumbuhan Padi Berdasarkan Citra Hiperspektral dengan Modifikasi Logika Fuzzy," Jurnal Pengindraan Jauh, vol. 10, no. 1, pp. 41-48, 2013.

H. Herminingsih, "Hubungan Adaptasi Petani Terhadap Perubahan Iklim dengan Produktivitas Tembakau pada Lahan Sawah dan Tegalan di Kabupaten Jember," JSEP, vol. 7, no. 2, pp. 31-44, 2014.

S. Putra, "Pengaruh Jarak Tanam Terhadap Peningkatan Hasil Padi Gogo Varietas Situ Patenggang," Agrin, vol. 15, no. 1, pp. 54-63, 2011.

D. A. Mardhika, B. D. Setiawan and R. C. Wihandika, "Penerapan Algoritma Support Vector Regression Pada Peramalan Hasil Panen Padi Studi Kasus Kabupaten Malang," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 10, pp. 9402-9412, 2019.

E. I. A. Warih and Y. Rahayu, "PENERAPAN DATA MINING UNTUK MENENTUKAN ESTIMASI PRODUKTIVITAS TANAMAN TEBU DENGAN MENGGUNAKAN ALGORITMA LINIER REGRESI BERGANDA DI KABUPATEN REMBANG," Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang.

W. T. Panjaitan, E. Utami and H. Al-Fatta, "PREDIKSI PANEN PADI MENGGUNAKAN ALGORITMA K-NEAREST NEIGBOUR," in SNATIF Universitas Muara Kudus, Kudus, 2018.

Fatkhuroji, S. Santosa and R. A. Pramunendar, "PREDIKSI HARGA KEDELAI LOKAL DAN KEDELAI IMPOR DENGAN METODE SUPPORT VECTOR MACHINE BERBASIS FORWARD SELECTION," Jurnal Teknologi Informasi, vol. 15, no. 1, pp. 61-77, 2019.

A. Mayasari and Y. Rahayu, "PENERAPAN ALGORITMA LINIER REGRESSION UNTUK MENENTUKAN ESTIMASI LUAS LAHAN PANEN TANAMAN JAGUNG TERHADAP CURAH HUJAN DAN AREA TAMBAH TANAM DI KABUPATEN REMBANG," Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang.

R. Nuari, A. Apriliyani, Juwari and Kusrini, "IMPLEMENTASI METODE K-NEAREST NEIGHBOR (KNN) UNTUK MEMPREDIKSI VARIETAS PADI YANG COCOK UNTUK LAHAN PERTANIAN," Jurnal INFORMA Politeknik Indonusa Surakarta, vol. 4, no. 2, pp. 28-34, 2018.

D. Rohmayani, "ANALYSIS OF STUDENT TUITION FEEPAY DELAY PREDICTION USING NAIVE BAYES ALGORITHM WITH PARTICLE SWARM OPTIMATION OPTIMAZATION (CASE STUDY : POLITEKNIK TEDC BANDUNG)," Jurnal Teknologi Informasi dan Pendidikan, vol. 13, no. 2, pp. 1-8, 2020.

E. Prasetyo, Data Mining Konsep dan Aplikasi Menggunakan Matlab, Yogyakarta: Andi, 2012.

B. Santosa, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis Teori dan Aplikasi, Yogyakarta: Graha Ilmu, 2007.

B. Santosa, Data Mining Terapan dengan Matlab, Yogyakarta: Graha Ilmu, 2007.

Published
2020-10-17
How to Cite
Nafi’iyah, N. (2020). SVM ALGORITHM FOR PREDICTING RICE YIELDS. Jurnal Teknologi Informasi Dan Pendidikan, 13(2), 50-54. https://doi.org/10.24036/tip.v13i2.341
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