Predictive Analysis of Rice Pest Distribution in Bali Province Using Backpropagation Neural Network

https://doi.org/10.22146/ijccs.85584

I Kadek Agus Dwipayana(1), putu sugiartawan(2*)

(1) Institut Bisnis dan Teknologi Indonesia
(2) Institut Bisnis dan Teknologi Indonesia
(*) Corresponding Author

Abstract


The distribution of pests in rice plants results in significant losses in production and damage to rice plants for farmers, seen from data on the area of rice borer attacks in the province of Bali in Tabanan district. Therefore, by predicting the distribution of rice pests, we can know the pattern of pest attacks so that we can anticipate them because predicting can provide accuracy and error values through the test results. One of the prediction models is BPNN, where BPNN's advantages for solving complex problems are very suitable for use where large amounts of data are involved and many input/output variables, BPNN is also capable of modeling nonlinear relationships between input and output variables, which may be difficult to capture by this type of predictive model. other. Backpropagation includes supervised learning, which means it can learn from labeled examples and can make accurate predictions on new, unlabeled data. Split data using K-fold cross-validation serves to assess the process performance of an algorithmic method by dividing random data samples and grouping the data as many as K k-fold values.

Keywords


Rice Pest Prediction; Backpropagation Analysis; Machine Learning

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References

M. Sarumaha and M. Pracaya, “Identifikasi serangga hama pada tanaman padi di desa bawolowalani,” J. Educ. Dev., vol. 8, no. 3, pp. 86–91, 2020. [2] M. Syamsiah and A. F. Dikri, “PENGGUNAAN BEBERAPA PERANGKAP UNTUK MENGENDALIKAN HAMA PENGGEREK BATANG PADI PANDANWANGI (Oryza sativa var. aromatic) PADA FASE GENERATIF,” Pro-STek, vol. 1, no. 1, p. 51, 2020, doi: 10.35194/prs.v1i1.821. [3] P. Ongsulee, “Artificial intelligence, machine learning and deep learning,” Int. Conf. ICT Knowl. Eng., pp. 1–6, Jan. 2018, doi: 10.1109/ICTKE.2017.8259629. [4] S. A. Salloum, M. Alshurideh, A. Elnagar, and K. Shaalan, “Mining in Educational Data: Review and Future Directions,” Adv. Intell. Syst. Comput., vol. 1153 AISC, pp. 92–102, 2020, doi: 10.1007/978-3-030-44289-7_9. [5] P. Wijaya, R. W. Sembiring, and S. S, “Analisis Metode Backpropagation Memprediksi Penerimaan Santri/Wati di Pondok Pesantren Modern Al-Kautsar,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., vol. 6, no. 1, p. 140, 2021, doi: 10.30645/jurasik.v6i1.278. [6] S. Sudewi, A. Ala, B. Baharuddin, and M. F. BDR, “Keragaman Organisme Pengganggu Tanaman (OPT) pada Tanaman Padi Varietas Unggul Baru (VUB) dan Varietas Lokal pada Percobaan Semi Lapangan,” Agrikultura, vol. 31, no. 1, p. 15, 2020, doi: 10.24198/agrikultura.v31i1.25046. [7] M. Suarsana, P. Parmila, P. S. Wahyuni, and I. G. M. Suarmika, “Pengaruh Serangan Hama Penggerek Batang dan Penyakit Tungro terhadap Produktivitas Sembilan Varietas Padi di Lokapaksa, Bali,” Agro Bali Agric. J., vol. 3, no. 1, pp. 84–90, 2020, doi: 10.37637/ab.v3i1.461. [8] A. Roihan, P. A. Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 5, no. 1, pp. 75–82, 2020, doi: 10.31294/ijcit.v5i1.7951. [9] M. N. Fadilah, A. Yusuf, and N. Huda, “Prediksi Beban Listrik Di Kota Banjarbaru Menggunakan Jaringan Syaraf Tiruan Backpropagation,” J. Mat. Murni Dan Terap. Epsil., vol. 14, no. 2, p. 81, 2021, doi: 10.20527/epsilon.v14i2.2961. [10] G. Dewantoro and J. N. Sukamto, “Implementasi Kendali PID Menggunakan Jaringan Syaraf Tiruan Backpropagation,” Elkha, vol. 11, no. 1, p. 12, 2019, doi: 10.26418/elkha.v11i1.29959. [11] J. R. Prabowo, R. Santoso, and H. Yasin, “IMPLEMENTASI JARINGAN SYARAF TIRUAN BACKPROPAGATION DENGAN ALGORITMA CONJUGATE GRADIENT UNTUK KLASIFIKASI KONDISI RUMAH (Studi Kasus di Kabupaten Cilacap Tahun 2018),” J. Gaussian, vol. 9, no. 1, pp. 41–49, 2020, doi: 10.14710/j.gauss.v9i1.27522. [12] A. E. Radho, P. Sugiartawan, and G. A. Santiago, “Prediksi Jumlah Kasus COVID-19 Menggunakan Teknik Sliding Wondows dengan Metode BPNN,” J. Sist. Inf. dan Komput. Terap. Indones., vol. 4, no. 1, pp. 12–23, 2022, doi: 10.33173/jsikti.123. [13] H. Putra and N. Ulfa Walmi, “Penerapan Prediksi Produksi Padi Menggunakan Artificial Neural Network Algoritma Backpropagation,” J. Nas. Teknol. dan Sist. Inf., vol. 6, no. 2, pp. 100–107, 2020, doi: 10.25077/teknosi.v6i2.2020.100-107.



DOI: https://doi.org/10.22146/ijccs.85584

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