Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis

Caraka, Rezzy Eko; Yasin, Hasbi; Basyiruddin, Adi Waridi

doi:10.24843/jmat.2017.v07.i01.p81

Cited by 13 publications

(15 citation statements)

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“…Persamaan dari penelitian ini dengan penelitian yang dilakukan oleh penulis adalah membahas tentang kelapa sawit. Perbedaannya adalah penulis mengolah data TBS Kelapa Sawit yang terdiri dari minyak sawit kasar (CPO) dan inti sawit (IKS) dan menentukannya dengan menggunakan metode fuzzy logic, sedangkan penelitian ini mengolah data CPO dan melakukan peramalan harga CPO menggunakan metode SVR [4].…”

Section: Pendahuluanunclassified

Penentuan Harga Tandan Buah Segar (TBS) Kelapa Sawit Menggunakan Metode Fuzzy Logic

2020

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The purpose of this study is to create a system for determining the price of oil palm fresh fruit bunches using the Fuzzy Logic method. Oil palm plantations are plantations that have natural resources that can generate large profits, which are used as cooking oil and much more. Palm oil FFB consists of Crude Palm Oil (CPO), which is fruit flesh oil and Palm Kernel (PK), which is the kernel of palm oil. This research makes the system use 5 input variables that is CPO price, IKS price, K Index, CPO Rendemen and IKS Rendemen, and one output variable which is the price of oil palm FFB. This system uses the Fuzzy Logic method because in this study there is fuzzy that can be determined, namely the price of oil palm-based TBS. This system uses 100 data to request using MAPE so that a percentage value is 85.75%. While the average value of the difference between the actual data and the results of the fuzzy Mamdani data is 17852 with an average percentage of errors of the fuzzy Mamdani data is 14.25%. The result of this system is the variable price of oil palm FFB output based on 5 variable inputs. The Mamdani Fuzzy Logic method determines the outcome of an uncertain value. Fuzzy logic starts from the formation of fuzzy sets, then implications, then rules, and then defuzzification.This is an open access article under the CC-BY-SA license.

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Section: Pendahuluanunclassified

Penentuan Harga Tandan Buah Segar (TBS) Kelapa Sawit Menggunakan Metode Fuzzy Logic

2020

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“…Tujuan SVR adalah untuk menemukan fungsi f(x) sebagai hyperplane yang berupa fungsi regresi, pada semua input data memiliki deviasi paling besar  dari target aktual yi untuk semua data training dan membuat error setipis mungkin [8]]. Keuntungan menggunakan SVR yaitu SVR sangat cocok untuk data set berdimensi tinggi dan sangat cocok digunakan untuk kasus non linier dengan menggunakan fungsi Kernel [9].…”

Section: A Teori Dasar Support Vector Regression (Svr)unclassified

“…Persamaan (5) di bawah ini merupakan formulasi dual untuk penyelesaian problem optimasi dari SVR, sebagai berikut : Performansi model SVR tergantung penggunaan hyperparameter yaitu C, epsilon (ε), degree, parameter kernel dan fungsi kernel yang digunakan [ [9]]. Menurut Santosa,2007, perbedaan pada loss function akan menghasilkan formulasi SVR yang berbeda dirumuskan dengan Formulasi ε-insensitive loss function sebagai berikut:…”

Section: A Teori Dasar Support Vector Regression (Svr)unclassified

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Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen

Cahyono

Sugiono

Tjandra

2019

jtim

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The stability of commodity prices for food is very influential on the economy of a region because stable prices have a direct impact on the level of people's purchasing power. The need to maintain the stability of food commodity prices is the background of this research and this can be anticipated by forecasting the Consumer Price Index (CPI). The CPI is an index number that calculates the average change in prices of goods and services consumed by households and society. The purpose of this study is to predict the CPI of the Foodstuff Group using the Support Vector Regression (SVR) method by utilizing Linear, Polynomial, Gaussian Radial Basis Function (RBF) and SPLine Kernel Functions. Selection of this SVR method, because SVR is able to map input vectors into higher dimensions and can produce a function with a trend of bumpy data following the data path formed, resulting in more accurate predictive values Research is limited to the city of Surabaya, the period of time the study begins January 1, 2016 until December 31, 2018. The data source used is the Surabaya Food Basic Price data as an input variable with 34 input attributes and the CPI data for Surabaya city Foodstuffs group as output variables. The results of this study are expected to be able to predict CPI with an error rate below 5%, which is indicated by MSE (Mean Square Error) < 0.05

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“…Therefore, it takes a method that can be used to make predictions or forecasting and result in good accuracy and low model error rates. Time series predictions are possible because the future depends on the past or analog because there is a relationship between the future and the past (Caraka, Yasin and Diponegoro, 2017). Another popular learning method that has been implemented in the predictive time series is the Support Vector Machine (SVM) method.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ozone (O3) Values Using Support Vector Regression Method

Puspitasari

Rokhman²,

Wahyono³

2021

JIP

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A large number of motor vehicles that cause congestion is a major factor in the poor air quality in big cities. Ozone (O3) is one of the main indicators in measuring the level of air pollution in the city of Surabaya to find out how air quality. Prediction of Ozone (O3) value is important as a support for the community and government in efforts to improve the air quality. This study aims to predict the value of Ozone (O3) in the form of time series data using the Support Vector Regression (SVR) method with the Linear, Polynomial, RBF, and ANOVA kernels. The data used in this study are 549 primary data from the daily average of ozone (O3) value of Surabaya in the period 1 July 2017 - 31 December 2018. The data will be used in the training and testing process until prediction results are obtained. The results obtained from this study are the Linear kernel produces the best prediction model with a MAPE value of 21.78% with a parameter value 𝜆 = 0.3; 𝜀 = 0.00001; cLR = 0.005; and C = 0.5. The results of the Polynomial kernel are not much different from the Linear kernel which has a MAPE value of 21.83%. While the RBF and ANOVA kernels each produce a model with MAPE value of 24.49% and 22.0%. These results indicate that the SVR method with the kernels used can predict Ozone values quite well.

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Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis

Cited by 13 publications

References 0 publications

Penentuan Harga Tandan Buah Segar (TBS) Kelapa Sawit Menggunakan Metode Fuzzy Logic

Penentuan Harga Tandan Buah Segar (TBS) Kelapa Sawit Menggunakan Metode Fuzzy Logic

Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen

Prediction of Ozone (O3) Values Using Support Vector Regression Method

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