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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science
Adyah Widiarni
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Penerapan Algoritma Support Vector Regression untuk Prediksi Jumlah Pasien Covid-19 di Provinsi Riau Adyah Widiarni; Mustakim Mustakim
Building of Informatics, Technology and Science (BITS) Vol 3 No 2 (2021): September 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.346 KB) | DOI: 10.47065/bits.v3i2.1004

Abstract

In 2019, at the end of December, there was an outbreak of a disease with an unknown cause in Wuhan, Hubei Province, China. The World Health Organization has named the outbreak of the disease as coronavirus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or Covid-19. Covid-19 is a disease outbreak that has spread in various regions of Indonesia, such as in Riau, at PT. Nusantara V Plantation (PTPN V). So we need a way to increase awareness and vigilance, namely by presenting information using Data Mining in predicting the number of cases with thealgorithm Support Vector Regression (SVR). The prediction process is carried out using SVR by specifying the SVR and Kernel Linear parameters. The SVR algorithm can predict the number of Covid-19 patients in the next 30 days so that the Correlation Coefficienti (R) level is 85% and the Mean Square Error (MSE) value is 0.196. From the results of the experiment, there was a decrease in cases of Covid-19 patients at PT. Perkebunan Nusantara V in the next 30 days, with the acquisition of the best minimum sensitivity value of 0.09
Penerapan Algoritma Support Vector Regression dalam Memprediksi Produksi dan Produktivitas Kelapa Sawit Adyah Widiarni; Mustakim Mustakim
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6089

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.
Co-Authors Mustakim Mustakim