Penerapan Algoritma Support Vector Regression dalam Memprediksi Produksi dan Produktivitas Kelapa Sawit

Authors

  • Adyah Widiarni Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
  • Mustakim Mustakim Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru

DOI:

https://doi.org/10.30865/mib.v7i2.6089

Keywords:

Palm Oil, Prediction, Production, Productivity, Support Vector Regression Algorithm

Abstract

Palm oil is a plantation crop that provides the highest economic value in Indonesia. Riau is currently the highest palm oil producing province in Indonesia with a state-run palm oil company, PTPN V. However, palm oil production is not always stable every month, whichexperiences ups and downs in the amount of production and productivity due to several factors including irregular rainfall, climate, soil fertility and most importantly fruit bunches that are not ready to harvest. So the data mining processing process is carried out by predicting the amount of production and productivity of oil palm applying the Support Vector Regression (SVR) algorithm with three kernels such as the Linear kernel, RBF kernel and Polynomial kernel. Experimental results on palm oil production and productivity show that the best kernel is the RBF kernel because the prediction results are close to the actual value. The accurate rate on palm oil production is 75.4% and palm oil productivity produces an accuracy value of 71%. It also produces an error value on palm oil production of 1.8%, for productivity of 2.1%. The results of the study can be used as an estimated picture in the company's future decision making.

Author Biography

Adyah Widiarni, Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru

Prodi Sistem Informasi

References

Dindha Amelia, “Statistik Kelapa Sawit Indonesia 2020,†vol. 21, no. 1, pp. 1–9, 2020, [Online]. Available: http://mpoc.org.my/malaysian-palm-oil-industry/

S. Agustian and H. Wibowo, “Perbandingan Metode Moving Average untuk Prediksi Hasil Produksi Kelapa Sawit,†Semin. Nas. Teknol. Informasi, Komun. dan Ind., no. November, pp. 156–162, 2019.

D. Marpaung, S. Sumarno, and I. Gunawan, “Prediksi Produktivitas Kelapa Sawit di PTPN IV dengan Algoritma Backpropagation,†Kaji. Ilm. Inform. Komput., vol. 1, no. 2, pp. 35–41, 2020.

A. Nurkholis and I. S. Sitanggang, “Optimization for prediction model of palm oil land suitability using spatial decision tree algorithm,†J. Teknol. dan Sist. Komput., vol. 8, no. 3, pp. 192–200, 2020, doi: 10.14710/jtsiskom.2020.13657.

D. F. Pasaribu, I. S. Damanik, E. Irawan, Suhada, and H. S. Tambunan, “Memanfaatkan Algoritma K-Means Dalam Memetakan Potensi Hasil Produksi Kelapa Sawit PTPN IV Marihat,†BIOS J. Teknol. Inf. dan Rekayasa Komput., vol. 2, no. 1, pp. 11–20, 2021, doi: 10.37148/bios.v2i1.17.

A. Widiarni and Mustakim, “Penerapan Algoritma Support Vector Regression untuk Prediksi Jumlah Pasien Covid-19 di Provinsi Riau,†Build. Informatics, Technol. Sci. J., vol. 3, no. 2, pp. 71–78, 2021, doi: 10.47065/bits.v3i2.1004.

R. Rosmala, “Fungsi komunikasi korporat Humas PT. Perkebunan Nusantara V Pekanbaru,†PRofesi Humas J. Ilm. Ilmu Hub. Masy., vol. 5, no. 2, p. 143, 2021, doi: 10.24198/prh.v5i2.28329.

I. Kusumanto, “Analisis Produktivitas PT. Perkebunan Nusantara V (PKS) Sei Galuh Dengan Menggunakan Metode American Productivity Center (APC),†J. Tek. Ind. J. Has. Penelit. dan Karya Ilm. dalam Bid. Tek. Ind., vol. 2, no. 2, p. 129, 2016, doi: 10.24014/jti.v2i2.5098.

D. P. R. Ika Oktavianti, Ermatita, “Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana,†vol. 1, no. 10, pp. 282–287, 2019.

Kurniawati, “Analisis Prediksi Harga Saham PT. Astra International Tbk Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) dan Support Vector Regression (SVR),†J. Ilm. KOMPUTASI, vol. 20, no. September, pp. 417–423, 2021.

I. H. Mustakim, Agus Buono, “PERFORMANCE COMPARISON BETWEEN SUPPORT VECTOR REGRESSION AND ARTIFICIAL NEURAL NETWORK FOR PREDICTION OF OIL PALM PRODUCTION,†J. Comput. Sci. Inf., vol. 9, no. 1, pp. 1–8, 2016.

Mustakim, A. Buono, and I. Hermadi, “SUPPORT VECTOR REGRESSION UNTUK PREDIKSI PRODUKTIVITAS KELAPA SAWIT DI PROVINSI RIAU,†J. Sains, Teknol. dan Ind., vol. 12, no. 2, pp. 179–188, 2015.

A. Solichin, U. B. Luhur, U. Hasanah, U. B. Luhur, U. P. Nasional, and V. Jakarta, “Development of Prediction System for Crude Palm Oil ( CPO ) Production with Time Series Data Mining Approach,†Int. Conf. Informatics, Multimedia, Cyber Inf. Syst., pp. 147–152, 2020.

M. Mustakim, C. Bella, and Y. R. Pratama, “Prediksi Jumlah Tunggakan Pajak Kendaraan Jatuh Tempo Menggunakan Algoritma Support Vector Regression,†Semin. Nas. Teknol. Informasi, Komun. dan Ind. 11, no. November 2017, pp. 1–11, 2019.

Y. Andini et al., “PENERAPAN DATA MINING TERHADAP TATA LETAK BUKU,†J. Technol. Informatics Comput. Syst., vol. XI, no. 1, pp. 9–15, 2022.

H. Budiarto, Eko and D. Susilawati, Susanto, “Penerapan Data Mining Untuk Rekomendasi Beasiswa Pada SD Maria Mediatrix Menggunakan Algoritma C4.5,†vol. 2, 2022.

Sharyanto and D. Lestari, “Penerapan Data Mining Untuk Menentukan Segmentasi Pelanggan Dengan Menggunakan Algoritma K-Means dan Model RFM Pada E-Commerce,†JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 4, pp. 866–871, 2022, doi: 10.30865/jurikom.v9i4.4525.

P. B. N. Setio, D. R. S. Saputro, and Bowo Winarno, “Klasifikasi Dengan Pohon Keputusan Berbasis Algoritme C4.5,†Prism. Pros. Semin. Nas. Mat., vol. 3, pp. 64–71, 2020.

M. Yuwantoro, I. Mahmud, and T. U. Murdiansyah Danang Triantoro, “Prediksi Harga Beras Premium dengan Metode Algoritma K-Nearest Neighbor,†e-Proceeding Eng., vol. 7, no. 1, pp. 2714–2724, 2019.

N. Feri Rahmadani, Akim M.H. Pardede, “JARINGAN SYARAF TIRUAN PREDIKSI JUMLAH PENGIRIMAN BARANG MENGGUNAKAN METODE BACKPROPAGATION ( STUDI KASUS: KANTOR POS BINJAI ),†Jtik (Jurnal Tek. Inform. Kaputama), vol. 5, no. 1, pp. 100–106, 2021, [Online]. Available: https://jurnal.kaputama.ac.id/index.php/JTIK/article/view/444/375

F. Ali Ma, A. Pratama, I. Sholihin, and A. Rizki Rinaldi, “Penerapan Model Prediksi Menggunakan Algoritma C.45 Untuk Prediksi Kelulusan Siswa SMK Wahidin,†J. Data Sci. Inform., vol. 1, no. 1, pp. 16–20, 2021.

H. Heriyanto, A. Asrol, D. Karya, and V. Y. Ningsih, “Analisis Faktor Produksi Kalapa Sawit Rakyat Menurut Tipologi Lahan di Kabupaten Indragiri Hilir Provinsi Riau,†J. Lahan Suboptimal, vol. 7, no. 1, pp. 14–25, 2019, doi: 10.33230/jlso.7.1.2018.366.

D. Sartika, N. Eliza, and A. Ilyas, “Pengaruh POC Urine Kambing Terhadap Pertumbuhan Bibit Kelapa Sawit (Elaeis guineensis Jacq.) Pada Fase Main Nursery Untuk Menekan Biaya Produksi,†Ekon. Dan Bisnis, vol. 24, no. 1, pp. 220–234, 2017.

Munawir, “POTENSI TANDAN KOSONG SAWIT UNTUK MEMPRODUKSI KOMPOS,†J. Manaj. Bisnis Equilib. POINT, vol. 1, pp. 11–16, 2018.

S. D. Agustina, Mustakim, Okfalisa, C. Bella, and M. A. Ramadhan, “Support Vector Regression Algorithm Modeling to Predict the Availability of Foodstuff in Indonesia to Face the Demographic Bonus,†J. Phys. Conf. Ser., vol. 1028, no. 1, 2018, doi: 10.1088/1742-6596/1028/1/012240.

D. A. Nasution, H. H. Khotimah, and N. Chamidah, “Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN,†Comput. Eng. Sci. Syst. J., vol. 4, no. 1, p. 78, 2019, doi: 10.24114/cess.v4i1.11458.

M. D. Purbolaksono, M. Irvan Tantowi, A. Imam Hidayat, and A. Adiwijaya, “Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 393–399, 2021, doi: 10.29207/resti.v5i2.3008.

D. K. Sumba, “Sistem Cerdas Prediksi Hasil Tanaman Jagung Di Indonesia Menggunakan Support Vector Regression,†J. Cosphi, vol. 2, no. 1, pp. 21–25, 2018, [Online]. Available: https://www.cosphijournal.unisan.ac.id/index.php/cosphihome/article/view/74%0Ahttps://www.cosphijournal.unisan.ac.id/index.php/cosphihome/article/download/74/54

S. Ghimire, B. Bhandari, D. Casillas-Pérez, R. C. Deo, and S. Salcedo-Sanz, “Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia,†Eng. Appl. Artif. Intell., vol. 112, no. April, p. 104860, 2022, doi: 10.1016/j.engappai.2022.104860.

T. O. Owolabi and M. A. Abd Rahman, “Modeling the optical properties of a polyvinyl alcohol-based composite using a particle swarm optimized support vector regression algorithm,†Polymers (Basel)., vol. 13, no. 16, 2021, doi: 10.3390/polym13162697.

D. Haryadi, A. R. Hakim, D. M. U. Atmaja, and S. N. Yutia, “Implementation of Support Vector Regression for Polkadot Cryptocurrency Price Prediction,†Int. J. Informatics Vis., vol. 6, no. 1–2, pp. 201–207, 2022, doi: 10.30630/joiv.6.1-2.945.

S. S. M. Evy Sulistianingsih, “Prediksi Nilai Tukar Dolar Amerika Serikat Terhadap Rupiah Dengan Metode Support Vector Regression (Svr),†Bimaster Bul. Ilm. Mat. Stat. dan Ter., vol. 8, no. 1, pp. 1–10, 2018, doi: 10.26418/bbimst.v8i1.30503.

R. P. Saputri, W. S. Winahju, and K. Fithriasari, “Klasifikasi Sentimen Wisatawan Candi Borobudur pada Situs TripAdvisor Menggunakan Support Vector Machine dan K-Nearest Neighbor,†J. Sains dan Seni ITS, vol. 8, no. 2, 2020, doi: 10.12962/j23373520.v8i2.44391.

N. Fitriyah, B. Warsito, and D. A. I. Maruddani, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm,†J. Gaussian, vol. 9, no. 3, pp. 376–390, 2020, doi: 10.14710/j.gauss.v9i3.28932.

T. G. Adi Putranto and I. Candradewi, “Sistem Klasifikasi Tingkat Keparahan Retinopati Diabetik Menggunakan Support Vector Machine,†IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 8, no. 1, p. 37, 2018, doi: 10.22146/ijeis.31206.

M. F. Rasyid, D. Imran, and ..., “Prediksi penyebaran Sub Varian omicron di Indonesia menggunakan Machine Learning,†SISITI Semin. Ilm. …, vol. XI, no. 1, pp. 1–7, 2022, [Online]. Available: http://ejurnal.dipanegara.ac.id/index.php/sisiti/article/view/936

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Published

2023-04-27

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Vol. 7 No. 2 (2023): April 2023

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