International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Contact Us

IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Palm Oil Prediction Production Using Extreme Learning Machine

[Full Text]



Yudi Triyanto, Ronal Watrianthos, Pristiyono, Yusmaidar Sepriani, Khairul Rizal



Extreme Learning Machine, Neural Networks, Palm Oil, Prediction, Production.



The total production of Indonesian palm oil (CPO) in 2018 reached 43.9 million tons, with a land area of 12.3 million hectares. However, every month there are still many companies that have problems in predicting palm oil production. Problems in predicting this production can be solved by calculation methods in the field of artificial neural networks, namely the Extreme Learning Machine (ELM) method. This method can solve linear and non-linear data problems and provide better average computation compared to other methods in predicting oil palm production. The data used is palm oil production data at PT Indo Palm Oil Labuhan Batu with a total of 297 in the period 2017-2018. While the parameters used are planting age, land area, number of trees, and yields. The results of the best-hidden neuron test are 13 with 2 technical data features and the training data pattern is pattern 1. The average MAPE value is 20.1% with the fastest computing time is the use of the number of hidden neurons 2. So based on the test results, the method ELM has a predictive model with quite good performance because the MAPE value is in the range of 20% -50%.



[1] D. J. P. K. Pertanian, “Volume produksi kelapa sawit (CPO), 2000-2018,” 2018. [Online]. Available: https://lokadata.beritagar.id/chart/preview/volume-produksi-kelapa-sawit-cpo-2000-2018-1550473390. [Accessed: 05-Jul-2019].
[2] Directorate General of Estate Crops, “Tree Crop Estate Statistik of Indonesia (2005-2017 Palm Oil),” 2017.
[3] E. Agasta, I. Cholissodin, and D. E. Ratnawati, “Prediksi Jumlah Produksi Kelapa Sawit Dengan Menggunakan Metode Extreme Learning Machine ( ELM ) ( Studi kasus : PT . Sandabi Indah Lestari Kota Bengkulu ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 5751–5759, 2018.
[4] W. M. Yohansyah and I. Lubis, “Analisis Produktivitas Kelapa Sawit (Elaeis guineensis Jacq.) di PT. Perdana Inti Sawit Perkasa I, Riau,” Bul. Agrohorti, vol. 2, no. 1, p. 125, 2018.
[5] S. Ding, H. Zhao, Y. Zhang, X. Xu, and R. Nie, “Extreme learning machine: algorithm, theory and applications,” Artif. Intell. Rev., vol. 44, no. 1, pp. 103–115, 2015.
[6] A. N. Alfiyatin, W. F. Mahmudy, C. F. Ananda, and Y. P. Anggodo, “Penerapan Extreme Learning Machine (ELM) untuk Peramalan Laju Inflasi di Indonesia,” J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 2, p. 179, 2019.
[7] B. Wang, “Prediction of Fatigue Stress Concentration Factor Using Extreme Learning Machine,” Comput. Mater. Sci., vol. 125, pp. 136–145, 2016.
[8] V. Sari, “Aplikasi Extreme Learning Machine Untuk Peramalan Data Time Series,” 5TH URECOL PROCEEDING, vol. 5, no. February, pp. 1294–1299, 2017.
[9] L. Yibo, L. Fang, and C. Qi, “A Review of the Research on the Prediction Model of Extreme Learning Machine,” J. Phys. Conf. Ser., vol. 1213, p. 042013, 2019.
[10] Z. M. Yaseen, “Predicting Compressive Strength of Lightweight Foamed Concrete Using Extreme Learning Machine Model,” Adv. Eng. Softw., vol. 115, pp. 112–115, 2018.
[11] P.-C. Chang, Y.-W. Wang, and C.-H. Liu, “The Development of a Weighted Evolving Fuzzy Neural Network for PCB Sales Forecasting,” Expert Syst. Appl., vol. 32, pp. 88–89, 2007.