Prediction of Palm Oil Seed Stock Production Results with the Back-propagation Algorithm
Keywords:Predictions, Production, Seed, Palm Oil, Back-propagation
Palm oil is the largest plantation export commodity in Indonesia because Indonesia has a soil structure that is suitable for planting oil palms. As is the case with the production of oil palm seed stock, of course, it does not always increase, and the production of oil palm seed stock will undoubtedly decrease. Therefore, an algorithm is needed to predict it so that the company can find out the future development of oil palm seed stock production using the Back-propagation algorithm. The Back-propagation Algorithm is used to predict the yield of oil palm seed stock production using data from the Marihat unit Oil Palm Research Center (PPKS) in 2019-2022. The Back-propagation Algorithm is an algorithm that reduces the error rate by adjusting the weights based on the desired output and target, as well as Testing the Back-propagation algorithm using Matlab. Based on the test results of the five architectural models used, one best architectural model was obtained, namely 2-14-1, using the Back-propagation method, which produced an MSE value of 0.0551030 with a Training time of 08:00 seconds with a test accuracy of 75%. Based on the research results obtained, it is expected to be input, suggestions, and efforts, especially for the Marihat Unit PPKS company, increase the stock of oil palm production seeds in each period to increase company profits more optimally.
W. Purba and D. Ardiyanti, “Dinamika Kerjasama Perdagangan Indonesia dalam Ekspor Kelapa Sawit ke India Tahun 2014-2019,” Jurnal FISK, vol. 2, no. 1, pp. 133–140, 2019.
N. N. Duakajui, F. Juita, and I. E. Anshori, “Analisis Ekonomi Pendapatan Usaha Perkebunan Kelapa Sawit Desa Sukomulyo Kecamatan Sepaku Kabupaten Penajam Paser Utara,” Paradigma Agribisnis, vol. 4, no. 2, p. 84, 2022, doi: 10.33603/jpa.v4i2.6790.
F. J. Lairi Fajriadi, Anwar Deli, “Sawit Pada Perkebunan Rakyat Di Kabupaten Aceh Singkil ( Risk Analysis Of Production And Price Of Fresh Palm Oil Fruit In People Plantation In Aceh Singkil District ) Program Studi Agribinis , Fakultas Pertanian , Universitas Syiah Kuala PENDAHULUAN Aceh,” vol. 4, no. 1, pp. 274–287, 2019.
S. Wati, J. Dedy Irawan, and Y. Agus Pranoto, “Rancang Bangun Pembibitan Kelapa Sawit Berbasis Iot(Internet of Things),” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 6, no. 1, pp. 145–153, 2022, doi: 10.36040/jati.v6i1.4509.
R. P. Novianto, “Analisis Pendektesi Gelombang Tsunami Dengan Menggunakan Jaringan Syaraf Tiruan,” Kaos GL Dergisi, vol. 8, no. 75, pp. 147–154, 2020.
H. Rohayani et al., “Prediksi Penentuan Program Studi Berdasarkan Nilai Siswa dengan Algoritma Backpropagation,” Journal of Information System Research, vol. 3, no. 4, pp. 651–657, 2022, doi: 10.47065/josh.v3i4.1935.
B. K. Sihotang and A. Wanto, “Analisis Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Tamu Pada Hotel Non Bintang,” Jurnal Teknologi Informasi Techno, vol. 17, no. 4, pp. 333–346, 2018.
W. Saputra, J. T. Hardinata, and A. Wanto, “Resilient method in determining the best architectural model for predicting open unemployment in Indonesia,” IOP Conference Series: Materials Science and Engineering, vol. 725, p. 012115, Jan. 2020, doi: 10.1088/1757-899X/725/1/012115.
O. Armaya Putri, H. Satria Tambunan, S. Tunas Bangsa, S. Utara, A. Tunas Bangsa, and I. A. Jln Sudirman Blok No, “Prediksi Kunjungan Wisatawan Mancanegara Ke Indonesia Menggunakan Jaringan Saraf Tiruan Dengan Algoritma Backpropagation,” Januari, vol. 2, no. 1, pp. 1–7, 2021.
A. Wanto and J. T. Hardinata, “Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4.0,” IOP Conference Series: Materials Science and Engineering, vol. 725, p. 012114, Jan. 2020, doi: 10.1088/1757-899X/725/1/012114.
I. A. R. Simbolon, F. Yatussa’ada, and A. Wanto, “Penerapan Algoritma Backpropagation dalam Memprediksi Persentase Penduduk Buta Huruf di Indonesia,” Jurnal Informatika Upgris, vol. 4, no. 2, pp. 163–169, 2018.
M. Thoriq, “Peramalan Jumlah Permintaan Produksi Menggunakan Jaringan Saraf Tiruan Algoritma Backpropagation,” Jurnal Informasi dan Teknologi, vol. 4, pp. 27–32, 2022, doi: 10.37034/jidt.v4i1.178.
A. Wanto, “Prediksi Produktivitas Jagung Di Indonesia Sebagai Upaya Antisipasi Impor Menggunakan Jaringan Saraf Tiruan Backpropagation,” SINTECH (Science and Information Technology) Journal, vol. 2, no. 1, pp. 53–62, 2019, doi: 10.31598/sintechjournal.v2i1.355.
A. Wanto et al., “Epoch Analysis and Accuracy 3 ANN Algorithm using Consumer Price Index Data in Indonesia,” in Proceedings of the 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology (ICEST), 2021, pp. 35–41. doi: 10.5220/0010037400350041.
A. Zulhamsyah and M. R. Lubis, “Penerapan backpropagation dalam memprediksi produksi kelapa sawit unit kebun marjandi,” vol. 3, pp. 779–787, 2019, doi: 10.30865/komik.v3i1.1693.
V. V. Utari, A. Wanto, I. Gunawan, and Z. M. Nasution, “Prediksi Hasil Produksi Kelapa Sawit PTPN IV Bahjambi Menggunakan Algoritma Backpropagation,” Journal of Computer System and Informatics (JoSYC, vol. 2, no. 3, pp. 271–279, 2021.
D. Marpaung, S. Sumarno, and I. Gunawan, “Prediksi Produktivitas Kelapa Sawit di PTPN IV dengan Algoritma Backpropagation,” Kajian Ilmiah Informatika & Komputer, vol. 1, no. 2, pp. 35–41, 2020.
Safruddin, E. Efendi, R. M. Ch, and A. Wanto, “Pemanfaatan Algoritma BFGS Quasi-Newton untuk Melihat Potensi Perkembangan Luas Tanaman Kopi di Pulau Sumatera,” Jurnal Media Informatika Budidarma, vol. 7, no. 1, pp. 473–483, 2023, doi: 10.30865/mib.v7i1.5524.
A. Wanto, N. L. W. S. R. Ginantra, S. Hendraputra, I. O. Kirana, and A. R. Damanik, “Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model,” Matrik: Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer, vol. 22, no. 1, pp. 51–82, 2022, doi: 10.30812/matrik.v22i1.1826.
Nurhayati, M. B. Sibuea, D. Kusbiantoro, M. Silaban, and A. Wanto, “Implementasi Algoritma Resilient untuk Prediksi Potensi Produksi Bawang Merah di Indonesia,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 2, pp. 1051–1060, 2022, doi: 10.47065/bits.v4i2.2269.
R. Puspadini, A. Wanto, and N. Arminarahmah, “Penerapan ML dengan Teknik Bayesian Regulation untuk Peramalan,” Journal of Computer System and Informatics (JoSYC), vol. 3, no. 3, pp. 147–155, 2022, doi: 10.47065/josyc.v3i3.1692.
N. L. W. S. R. Ginantra, A. D. GS, S. Andini, and A. Wanto, “Pemanfaatan Algoritma Fletcher-Reeves untuk Penentuan Model Prediksi Harga Nilai Ekspor Menurut Golongan SITC,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 4, pp. 679–685, 2022, doi: 10.47065/bits.v3i4.1449.
S. Setti and A. Wanto, “Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World,” JOIN (Jurnal Online Informatika), vol. 3, no. 2, pp. 110–115, 2018, doi: 10.15575/join.
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