The Application of the Fletcher-Reeves Algorithm to Predict Spinach Vegetable Production in Sumatra

  • Mhd. Zoel Ardha STIKOM Tunas Bangsa
  • Verdi Yasin STMIK Jayakarta
  • Solikhun Solikhun STMIK Jayakarta
Keywords: Artificial Neural Network, Fletcher-Reeves, Vegetable Plant.

Abstract

Determination of spinach plant predictions is one of the most critical decision-making processes. In predicting spinach plants in each period, it depends on each period, both the previous and subsequent periods. The production of spinach plants that change every period causes uncertainty in predicting. The method used to indicate the data is the Fletcher-Reeves algorithm, it is an appropriate development technique compared to the backpropagation strategy because this strategy can speed up the preparation time to arrive at the minimum convergence value. This paper does not discuss the prediction results. Still, it discusses the ability of the Fletcher-Reeves algorithm to make predictions based on the spinach production dataset obtained from the Central Statistics Agency. The purpose of this research is to see the accuracy and performance measurement of the algorithm in the search for the best results to solve the prediction of spinach plants in Sumatra. The research data used are spinach vegetable production data in North Sumatra. Based on this data, a network architecture model will be formed and determined, including 2-20-1, 2-30-1, 2-35-1, 2-45-1, and 2-50-1. After training and testing, these five models show that the best architectural model is 2-20-1 with an MSE value of 0.00608399, the lowest among the other four models. So the model can be used to predict spinach plants in Sumatra.A well-prepared abstract enables the reader to identify the basic content of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety.

References

[1] M. A. P. Hutabarat, M. Julham, and A. Wanto, “Penerapan Algoritma Backpropagation Dalam Memprediksi Produksi Tanaman Padi Sawah Menurut Kabupaten/Kota Di Sumatera Utara,” Jurnal Semantik, vol. 4, no. 1, pp. 77–86, 2018.
[2] M. A. Aldiansyah, “Pemrosesan Citra Digital Untuk Klasifikasi Tiruan Backpropogation,” Jurnal Teknologi Informasi dan Terapan, vol. 5, no. 1, pp. 31–36, 2018.
[3] H. Al Banna and B. D. Apri Nugroho, “Model Prediksi Level Air Di Lahan Perkebunan Kelapa Sawit Dengan Jaringan Saraf Tiruan Berdasarkan Pengukuran Sensor Rain Gauge Dan Ultrasonik,” Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering), vol. 10, no. 1, pp. 104–112, 2021, doi: 10.23960/jtep-l.v10i1.104-112.
[4] H. William, A. Hidayatno, and A. A. Zahra, “Aplikasi Jaringan Saraf Tiruan Perambatan Balik Untuk Prakiraan Valuta Gbp/Usd Dalam Forex Trading,” Transient: Jurnal Ilmiah Teknik Elektro, vol. 3, no. 4, pp. 493–500, 2014.
[5] L. Dinar, A. Suyantohadi, and M. Fallah, “Pendugaan Kelas Mutu Berdasarkan Analisa Warna Dan Bentuk Biji Pala (Myristica Fragrans Houtt) Menggunakan Teknologi Pengolahan Citra Dan Jaringan Saraf Tiruan,” Jurnal Keteknikan Pertanian, vol. 26, no. 1, pp. 53–59, 2012.
[6] S. Edi and J. Bobihoe, “Budidaya Tanaman Sayuran,” Repository Publikasi Kementerian Pertanian, 2010. http://repository.pertanian.go.id/handle/123456789/18735 (accessed Jan. 20, 2023).
[7] E. Evanita and M. M. Hakim, “Prediksi Harga Jual Suku Cadang Impor Mesin Rokok Dengan Jaringan Syaraf Tiruan,” Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 9, no. 1, pp. 67–76, 2018, doi: 10.24176/simet.v9i1.1550.
[8] R. Fauzi, B. Daun, M. A. Agmalaro, and A. Suyantohadi, “Identifikasi Jenis Tanaman Tin Sesuai Dengan Bentuk Daun Menggunakan Jaringan Saraf Tiruan (Jst) Dengan Metode Backpropafation,” Jurnal Education and Development, vol. 6, no. 3, pp. 73–77, 2017.
[9] I. Yani and M. E. S. Lubis, “Penggunaan Model Jaringan Saraf Tiruan ( Artificial Neural Network ) Untuk Memprediksi Hasil Tandan Buah Segar ( Tbs ) Kelapa Sawit Berdasar Curah Hujan Dan Hasil Tbs Sebelumnya,” Pusat Penelitian Kelapa Sawit, vol. 26, no. 2, pp. 59–70, 2018.
[10] P. Jaya and G. Sitorus, “Pemodelan Jaringan Saraf Tiruan Pada Pertumbuhan Tanaman Serai Wangi Varietas Mahapegiri Lokal (Cymbopogon Winterianus Jowwit),” Universitas Sriwijaya, 2021.
[11] A. Jeremy and O. Simanjuntak, “Klasifikasi Penyakit Daun Sawit Menggunakan Metode Jaringan Saraf Tiruan Dengan Fitur Local Binary Pattern,” Jurnal Algoritme, vol. 1, no. 1, pp. 1–9, 2022.
[12] H. Mayrowani, “Pengembangan Pertanian Organik di Indonesia,” Pusat Sosial Ekonomi dan Kebijakan Pertanian Kementerian Pertanian, vol. 30, no. 70, pp. 91–108, 2012.
[13] F. Nugraha, B. Irawan, and D. M. Midyanti, “Deteksi Penyakit Pada Tanaman Jeruk Pontianak Dengan Metode Jaringan Saraf Tiruan Backpropagation,” Jurnal Komputer dan Aplikasi, vol. 4, no. 2, pp. 56–65, 2016.
[14] S. Punuindoong, W. J. N. Kumolontang, and R. I. Kawulusan, “Respon Tanaman Bayam (Amaranthus tricolor L.) terhadap Pemberian berbagai Jenis Pupuk Organik pada Tanah Marginal,” Jurnal COCOS, vol. 1, no. 6, pp. 1–8, 2017.
[15] R. S. Rivai, “Konsep Dan Implementasi Pembangunan Pertanian Berkelanjutan Di Indonesia,” Pusat Sosial Ekonomi dan Kebijakan Pertanian Kementerian Pertanian, vol. 29, no. 1, pp. 13–25, 2011.
[16] D. Sinaga, S. Solikhun, and I. Parlina, “Jaringan Syaraf Tiruan untuk Memprediksi Penjualan Kelapa Sawit Menggunakan Algoritma Backpropagation,” in Prosiding Seminar Nasional Riset Information Science, 2019, no. 1, pp. 418–426.
[17] I. L. Sirait, J. M. Gultom, J. Tindaon, R. J. Tampubolon, and W. J. Mawaddah, “Peramalan tingkat produktivitas kedelai di indonesia menggunakan algoritma,” Jurnal Semantik, vol. 4, no. 2, pp. 183–192, 2018.
[18] A. D. Surya, M. Sapriyaldi, A. Wanto, A. P. Windarto, and I. S. Damanik, “Komparasi Algoritma Machine Learning untuk Penentuan Performance Terbaik Pada Prediksi Produksi Tanaman Jahe di Indonesia,” in Prosiding Seminar Nasional Ilmu Sosial dan Teknologi : SANISTEK 2021, 2021, pp. 276–284.
[19] A. Suyantohadi, A. D. Guritno, and D. Kastono, “Identifikasi Pertumbuhan Bayam (Amaranthus Sp.) Dengan Metoda Jaringan Saraf Tiruan Untuk Prediksi Hasil Panen Berdasarkan Pemberian Pupuk Organik Cair,” Universitas Gadjah Mada, 2019.
[20] N. Nurfadillah, “Analisis Teknik Line Search pada Algoritma Conjugate Gradient untuk Optimasi Pembelajaran Backpropagation Studi kasus Peramalan Parameter Cuaca,” Universitas Telkom, 2013.
[21] A. Thahara, N. Nuri, I. T. Siregar, A. Wanto, and A. P. Windarto, “Analisis Kinerja Metode Fletcher Reeves Untuk Prediksi Ekspor Minyak Sawit Berdasarkan Negara Tujuan Utama,” in Seminar Nasional Ilmu Sosial Dan Teknologi - SANISTEK, 2021, pp. 248–255.
[22] A. Wanto, “Optimasi Prediksi Dengan Algoritma Backpropagation Dan Conjugate Gradient Beale-Powell Restarts,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 3, no. 3, pp. 370–380, 2018, doi: 10.25077/teknosi.v3i3.2017.370-380.
[23] M. S. Wibawa, “Pengaruh Fungsi Aktivasi, Optimisasi dan Jumlah Epoch Terhadap Performa Jaringan Saraf Tiruan,” Jurnal Sistem dan Informatika (JSI), vol. 11, no. 2, pp. 167-174., 2018, doi: 10.13140/RG.2.2.21139.94241.
Published
2023-01-24
How to Cite
[1]
M. Ardha, V. Yasin, and S. Solikhun, “The Application of the Fletcher-Reeves Algorithm to Predict Spinach Vegetable Production in Sumatra”, International Journal of Engineering and Computer Science Applications (IJECSA), vol. 2, no. 1, pp. 9-20, Jan. 2023.
Section
Articles