Palm Oil Price Research Articles

Power Business Intelligence In The Data Science Visualization Process To Forecast CPO Prices

Forecasting is one of the techniques in data mining by utilizing the data available in the data warehouse. With the development of science, forecasting techniques have also entered the computational field where the forecasting technique uses the artificial neural network (ANN) method. Where is the method for simple forecasting using the Time Series method. However, the ability to create data visualizations certainly hinders researchers from maximizing research results. Of course, with the development of the Power BI software, the data science process is more neatly presented in the form of visualization, where the data science process involves various fields so that in this paper the results of forecasting the price of crude palm oil (CPO) are presented for the development of the CPO business with the hope of implementing the Business Process. intelligence (BI) by involving ANN, namely the time series for forecasting. From the final results, accuracy in forecasting with time series involves 2 accuracy techniques, the first using MAPE and getting a result of 0.03214% and the second using MSE to get 962.91 results.

Analysis of The Effect of Three-Month DCA Accuracy on Optimistic Bias In Fulfilling Cooking Oil For DCA Recipient Families

Cooking oil is one of the basic needs for the people of Indonesia and is included in the nine staples. Currently, there is an increase in the price of cooking oil as a result of the price of crude palm oil on the international market increasing and reaching the highest price ever recorded in history. So that the government’s action to overcome the increase in cooking oil prices is to provide Direct Cash Assistance in the form of electronic money or non-cash assistance with the main target being Families Receiving Non-Cash Food Assistance and Family Hope Program and Street Vendors who sell fried food. This study aims to see the bias that occurs against DCA given to beneficiary families and provide suggestions for the government based on the results of cost accuracy in fulfilling cooking oil for 3 months. The method used in this research is the statistical test of single sample mean and double sample mean to determine the accuracy of respondents’ answers to the movement of cooking oil prices. The results showed that there was an optimistic bias towards the estimation of cooking oil prices by the community. People expect the price of cooking oil to fall, but in actual conditions the price of cooking oil for a month from May to June shows a fixed price neither rising nor falling.

Seasonal price variation analysis of palm oil: case study of Nigeria (2016–2022)

To enhance the benefits derived from palm oil processing by the farmers, there is a need for the producers (farmers) and buyers of palm oil to understand the seasonal price for making informed decisions on when to buy and sell the commodity. In order to determine price at the appropriate time of the season, the study looked at the seasonal price variation analysis of palm oil and the relationship between producers and buyers. This was done by understanding buyers’ requirements, producers’ effort, measurements taken, and contracting with buyers’ credit from customers. A multiphase sampling strategy was used. Organized interviews and questionnaires were utilized to collect primary data, while the National Bureau of Statistics provided secondary time series data on the average monthly retail price of palm oil from 2016 to 2022. The need for bio-fuel, production growth, governmental regulations, market dynamics, and the seasonality of palm oil products are then identified by the study as important factors influencing palm oil price fluctuation throughout the year. The study then suggests that there’s a need for economic diversification to reduce reliance on a single sector. This study will serve as a basis for future research on palm oil price variation analysis.

MODELING COOKING OIL PRODUCTION WITH CRUDE PALM OIL (CPO) PRICE USING TRANSFER FUNCTION

Indonesia is the largest palm oil producer in the world. However, Indonesia is currently experiencing a significant scarcity of cooking oil for the basic needs of the community. One of the causes of this problem is the high price of crude palm oil (CPO). The price of CPO affects the production of cooking oil produced by the cooking oil industry. CPO prices are released in the form of daily prices in the market which form a time series pattern that affects the production of cooking oil which is also produced daily in the industry. To forecast cooking oil production from CPO prices can be done with a transfer function model. The stages of analysis are forming an ARIMA model, processing input time series whitening, and output time series whitening. Checking white noise by plotting ACF αt, ACF βt. Make a CCF graph and determine the order (r,s,b). Estimate the first parameter of the disturbance series (nt) and identify the disturbance series (nt) in the ARIMA model. Next, perform a diagnostic check of the transfer function model by checking the cross-correlation between Xt & Yt and checking the autocorrelation of Xt and Yt for model feasibility testing. Then make an ACF graph of the transfer function model's residuals to test the model's suitability. The final stage is to select the best model based on the smallest AIC value, ensuring the accuracy of the selected model using MAPE value, and making predictions. As a result, the model for CPO price is an input series variable, and cooking oil production is an output series variable. However, the MAPE result was 100%, indicating that the model is not very accurate for this data. Nevertheless, by ignoring the result and continuing with the forecasting, this model shows that the forecasted values for cooking oil based on CPO price have increased from the form of the model obtained for the time data from 2013 to 2022.

Deep Learning Based Prediction Model for Crude Palm Oil Prices Using News Sentiment Analysis with Sliding Window

Crude palm oil (CPO) price prediction plays an important role in agricultural economic development. Various economics and agricultural related factors have been used to predict CPO prices. Nevertheless, understanding news sentiment features will also be important in CPO price prediction. This paper proposes a CPO price prediction model to help the plantation organizations in the palm oil sector to successfully anticipate CPO price fluctuations and manage the resources more effectively. The CPO price behavior is non-linear in nature, and thus prediction is very difficult. In this paper, an improved version of recurrent network, Long Short-Term Memory (LSTM) based CPO price prediction model with news sentiment is used to produce an enhanced prediction model. The findings of this study show that the LSTM based forecasting model with news headline sentiment using a six-month sliding window produced the best result in forecasting the CPO price movement compared to other sliding window sizes.

STRATEGI PENETAPAN HARGA KELAPA SAWIT PETANI PLASMA PADA PT. ADINUSA CAKRA MANDIRI DI KECAMATAN SEPAUK KABUPATEN SINTANG

The pricing of Fresh Fruit Bunches (FFB) (palm fruit can be aimed at protecting plasma farmers such as setting a basic price so that price certainty for farmers is obtained and production incentives are obtained as well as to protect consumers through the determination of the highest price in accordance with the price in the market. Suggestions for this research: It is better to maintain the quality of oil palm fresh fruit bunches (FFB) in plantations owned by plasma farmers at PT. Adinusa Cakra Mandiri In Sepauk District, the company must continue to help in the selection of plasma oil palm land so that the fruit continues to be of high quality. The determination of palm FFB prices should be socialized to farmers who own plasma land through the company's partner cooperatives. Keywords: Determination Strategy, Palm Oil Price

Advanced Machine Learning Techniques for Accurate Forecasting of Crude Palm Oil Price

The accurate forecasting of crude palm oil (CPO) prices is of paramount importance to stakeholders across the agricultural and financial sectors, as it directly influences critical decisions related to production, trading, and investment strategies. Traditional time series models, while valuable, often fall short in capturing the intricate, non-linear dynamics inherent in CPO price fluctuations. This research delves into the application of cutting-edge machine learning techniques, with a particular emphasis on state-of-the-art models like transformers and hybrid architectures, to significantly enhance the precision of CPO price predictions. This study provides a comprehensive overview of existing research on traditional CPO forecasting methodologies, while also exploring the promising potential of machine learning applications in this domain. By critically analyzing previous studies and highlighting emerging trends, this preliminary investigation aims to establish a benchmark for future research in the field of CPO price prediction. The findings presented herein are intended to serve as a valuable reference point, illuminating the progress made thus far and identifying key areas for further exploration.

The position of Indonesia’s palm oil in the vegetable oil international market

Abstract The increase in global crude oil prices and awareness of energy sustainability are driving the development of the biofuel industry. Palm oil has high productivity and competitive prices compared to other vegetable oils, encouraging competition among major palm oil exporters and significant vegetable oils. The research examines factors influencing Indonesia’s position in the global vegetable oil competition and assesses how economic shocks impact Indonesian palm oil competitiveness. This study utilized the Almost Ideal Demand System model on secondary data in the time series period of 1990-2022. The research results showed that the increase in global income is the most influential factor in the rise in the market share of Indonesian palm oil. The increase in crude oil prices favored the position of Indonesian palm oil compared to Malaysia. Fluctuations in Malaysian palm oil prices could affect the competitiveness of Indonesian palm oil. Malaysia’s palm oil, soybean oil, and sunflower oil are more complementary than Indonesia’s. Indonesia needs to collaborate with significant vegetable oil exporters, to create a palm oil commodity exchange, and to synchronize policies from upstream to downstream to ensure the fulfillment of domestic and export palm oil needs.

Exploring sustainable cooking oil consumption patterns in Punjab, Pakistan: A comprehensive scientific study

Abstract Palm oil is the world’s most widely traded vegetable oil, with total trade amounting to 77.22 million metric tons of total vegetable oil production in 2023. Palm oil is usually used as the material of cooking oil and food products, biofuel, cosmetics, and other derivates. Pakistan imported $3.36B in Palm Oil, becoming the third largest importer of Palm Oil in the world. However, palm oil has faced deforestation in the last few years. To solve that, the palm oil plantation must be sustainably certified (RSPO), and the impact will increase the palm oil price. Therefore, this research aims to explore Pakistan’s sustainable cooking oil consumption patterns. This research uses purposive sampling from diverse socio-economic backgrounds and geographical locations within Pakistan. The total respondents are 52 respondents from Punjab, Pakistan. The data analysis uses descriptive statistical analysis. The results show that palm oil was the most used cooking oil (68%), followed by sunflower (22%). Over half of the participants (56%) were willing to pay a premium for environmentally friendly cooking oils. Most participants (84%) were familiar with sustainable certification labels like RSPO, and most participants (82%) believed that adopting sustainable palm oil production practices could alleviate environmental and ethical concerns.

THE ROLE OF EXTENDED VALUE CHAIN ACTIVITIES IN ENHANCING ECONOMIC VALUE ADDED PLANTATION COMPANIES IN MALAYSIA

The palm oil industry in Malaysia has faced issues of lower palm oil price and limited land area, and these issues have created a challenging environment for plantation companies to operate in. Plantation companies are constrained in maximizing their profits. Thus, this paper aims to examine the Economic Value Added (EVA) and the factors that influence the EVA of different value chain activities. The dynamic generalized method of moments (DGMM) estimators were adopted for the analysis and the focus was on the -40 Malaysian plantation companies that were listed in the Bursa Malaysia from 2010 to 2018. The results revealed that both downstream integrated activities, namely oleo-chemicals/biodiesel and refineries activities had a significant influence on the plantation companies’ EVA, with the coefficient value 1.12 and 0.09 respectively. Other factors such as the gross margin, crude palm oil price, and exchange rate also significantly influenced the plantation companies’ EVA. The empirical results which were based on the plantation companies’ EVA were influenced by different value chain activities as expected. The findings show that the downstream integrated activities like refineries, and oleo-chemicals/biodiesel activities have played a significant role in increasing the EVA of plantation companies.

Pengaruh Produksi Kelapa Sawit, Harga Minyak Kelapa Sawit Dunia, dan Emisi Karbon terhadap Volume Ekspor Minyak Kelapa Sawit Indonesia

Indonesia is the largest producer and exporter of palm oil in the world. Unfortunately, Indonesian palm oil exports are influenced by various factors, including environmental issues, which ultimately disrupt export benefits. This research aims to analyze the influence of production, world prices, and carbon emissions on the volume of Indonesian palm oil exports. The analytical method used is Two Stage Least Square (2SLS) with simultaneous supply and demand equations for Indonesian palm oil export volumes from 1991-2021. The research results show that the increase in palm oil plantation areas in Indonesia has positively contributed to the increase in world palm oil prices as demand also increases. Furthermore, the increase in world palm oil prices has encouraged Indonesia's exports to world markets. The increase in Indonesian palm oil production has encouraged an increased volume of exports to the world market. However, market appreciation for environmentally friendly palm oil has not contributed positively to the price and increase in palm oil export volume.

Implementation of Artificial Neural Network in Predicting CPO Prices Using Backpropagation

This research examines the development of a forecasting model for Crude Palm Oil (CPO) prices using artificial neural network algorithms, particularly the backpropagation algorithm. CPO, as Indonesia's main export commodity, has significant economic impacts and affects the income of oil palm farmers. Data on CPO prices taken from CIF Rotterdam from January 2019 to December 2023 were used in this study. The research method involved several stages, including data collection, pre-processing, model design, and model implementation using Python programming. The results of training the model using the backpropagation algorithm showed an error value of 0.537829578 after 1000 epochs, while evaluation using Mean Squared Error (MSE) showed an MSE value of 0.022709 during the training process and 0.017604 during the testing process. The model also produced predictions for CPO prices in the next three months: 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model can predict future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

Dampak Hilirisasi Terhadap Pendapatan Petani Kelapa Sawit di Nagari Talang Binjai Kecamatan Silaut Kabupaten Pesisir Selatan

This research aims to analyze the impact of downstreaming on the income of oil palm farmers in Nagari Talang Binjai, Silaut District, Pesisir Selatan Regency. The main problem in this research is the extent to which palm oil downstreaming helps increase the income of oil palm farmers in Nagari Talang Binjai, Silaut District, Pesisir Selatan Regency. This type of research is field research with a qualitative approach. The data analysis technique used is Miles and Huberman which includes reduction, presentation and drawing conclusions. The results of this research found that the downstreaming of palm oil stabilized the selling price of palm oil to farmers. Which can help stabilize the income of oil palm farmers in Nagari Talang Binjai, Silaut District, Pesisir Selatan Regency. Moreover, the European Union is boycotting palm oil commodities and their derivatives produced in Indonesia. After discussion, the Indonesian government must continue to carry out research regarding downstream palm oil and increase its costs. This is so that scientists can immediately produce one hundred percent vegetable energy, whether in the form of diesel or fuel, even in other forms of vegetable energy. After that, mass production of every vegetable energy product from palm oil will be carried out as soon as possible. Due to economic threats from abroad in the form of boycotts, reducing imports and planting oil palm, several countries have begun to implement them. By downstreaming palm oil, Indonesia will become a developed and strong country, and can bring prosperity to its people, especially oil palm farmers in Nagari Talang Binjai, Silaut District, Pesisir Selatan Regency.

ANALYSIS OF TAXATION AND WELFARE OF ITINERANT PALM OIL MARKETERS IN AKWA IBOM STATE, NIGERIA

The study analyzed the taxation and welfare of palm oil marketers in Akwa Ibom State, Nigeria. Multistage sampling techniques were adopted to select 120 itinerant palm oil marketers for the study. Primary data obtained using structured questionnaires were analyzed using descriptive statistical tools, multiple regression model and mean per-capita household consumption expenditure. Results showed that palm oil marketers paid mostly sales tax with minimum and maximum amounts being N500 and N3000 respectively. The level of market participation (P<0.01), quantity of palm oil sold (P<0.01), and selling price of palm oil (P<0.05) were the determinants of the amount of tax paid by itinerant traders in the study area. The respondents' monthly expenditure on food and non-food items was N111,787.90. Imposing taxes on itinerant palm oil traders decreased their welfare. There is a negative relationship between taxation and welfare of itinerant palm oil marketers, the study recommended that policymakers consider tax incentives like reduction in the amount of tax that marketers are required to pay and exemptions to encourage palm oil marketers to participate in the market to enhance their welfare.

Asymmetric effects of commodity and stock market on Chinese green market: Evidence from wavelet-based quantile-on-quantile approach

This study principally concentrates on evaluating the effects of Chinese commodity and stock market on the green market. The Maximum Overlap Discrete Wavelet Transform (MODWT) technique is utilized for decomposing time series into diverse frequency components. Following this, quantile-on-quantile regression (QQ) is implemented to meticulously demonstrates the dependency structure among the variables under diverse market conditions. Results demonstrate that energy has no short-term impact, while non-ferrous metals, palm oil, sugar, and stock impact the green market at high and low quantiles. Booming gold and declining stock negatively affect the green market. In the medium term, the influence of stocks and most commodities diminishes. Bullish coal and lead prices have a positive impact, while bearish zinc, corn, and palm oil prices have a negative impact. Crude oil does not significantly affect the green market. In the long term, both stock and commodities have a positive impact on the green market, particularly at extreme quantiles. When both markets decline, they negatively affect the green market. Finally, Granger causality test reveals a bi-directional linear and non-linear causality between commodity and stock markets and the green market across all time scales, enabling investors to predict green market returns based on commodity and stock returns.

Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR)

Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR)

Selection of Modelling for Forecasting Crude Palm Oil Prices Using Deep Learning (GRU & LSTM)

The unstable crude palm oil (CPO) prices have an impact on assessments of economic growth and environmental sustainability, as well as market strategies, international trade discussions, and consumer pricing expectations for products made from CPO. Therefore, it is crucial to identify the best prediction method to accurately forecast this cost. This research aims to develop an accurate time series data prediction model for crude palm oil prices using GRU and LSTM methods. The study also aims to identify the best-performing model by comparing their performance. This study uses LSTM and GRU methods as well as Bi-LSTM as a comparison, using crude palm oil price data from the Indonesian Ministry of Trade (August 1, 2018–August 31, 2023) from Medan (1064 data points) and Rotterdam SPOT (617 data), Rotterdam Forward 1 Month (1,022 data), and Rotterdam Forward 2 Month (950 data). Each dataset is then split into training and testing data with a 70:30 ratio. The hyperparameters used are (a) learning rate: (0.001); (b) batch size: 100; (c) node: (512); (d) optimizer: Adam; (e) and epoch: 50. The results of the forecast are highly accurate with a MAPE of less than 10%. Overall, both LSTM and GRU techniques demonstrate excellent performance in forecasting crude palm oil prices, but they may need to be modified based on data features. The ARIMA method can also be considered for forecasting. Future studies may consider optimizing parameters and model structures. Doi: 10.28991/ESJ-2024-08-03-05 Full Text: PDF

팜유 가격 예측을 위한 딥러닝 기반 단기 시계열 예측 모델링

팜유 가격 예측을 위한 딥러닝 기반 단기 시계열 예측 모델링

Application Design For Palm Oil Ramp Search in North Bengkulu Web Based Gis

The development of the world of oil palm plantations in the Bengkulu region is growing very rapidly and one of them is the North Bengkulu district. With the increasing number of oil palm plantations, most people do not know where to sell the products from their oil palm plantations. The lack of available information and the unclear distance to the location makes it difficult for farmers to sell the products from their plantations. This research is aimed at solving problems by creating new applications. a smartphone (Android) based application which aims to provide solutions to the public so that they can easily find out where they are and how far they have to go to reach the oil palm road they want without being limited by time or place. GIS-based applications have been proven to have a major impact on economic and social development, and the use of GIS has become a current trend. The GIS application was developed to help search for and locate oil palm RAMPs. The aim of this research is to help oil palm farmers find and determine the best locations to sell their crops. They can also compare the selling prices of the most expensive palm oil. For this application, the Android operating system is combined with Google Maps, a web-based virtual world map service created by Google. This application has features such as the name of the palm ramp owner, a map of the palm ramp location, palm oil prices, location suggestions, palm oil views, application instructions, and much.

PREDIKSI HARGA KELAPA SAWIT MENGGUNAKAN METODE REGRESI LINEAR BERGANDA Studi Kasus PT. Bakrie Sumatera Plantations, Tbk.

Indonesia's plantation sector, especially palm oil, has a high selling value and its attractiveness continues to increase. The price of palm oil is strongly influenced by external factors, especially the price of crude oil (CPO) and palm oil production globally. Unstable price fluctuations have become a challenge for companies such as PT Bakrie Sumatera Plantations, Tbk. in planning harvest time and optimizing profits. To overcome this uncertainty, this study proposes the use of the Multiple Linear Regression method to predict palm oil prices. Price and production data from 2021 to 2023 are the main basis for developing the prediction model. In this study, 4 independent variables were used, namely year, month, oil price and total production. The amount of data used in this study is 33 data. In testing the Multiple Linear Regression method has an RMSE value of 0.24 and the predicted value of palm oil prices from January to December 2024 is the price of palm oil with the highest value in January 2024 of Rp. 2,276.931 / kg and with the lowest value in December 2024 of Rp. 1,474.75 / kg.