scholarly journals Export intensity and competitiveness of Indonesia's crude palm oil to main destination countries

2021 ◽  
Vol 67 (No. 5) ◽  
pp. 189-199
Author(s):  
Fachry Rosyadi ◽  
Jangkung Handoyo Mulyo ◽  
Hani Perwitasari ◽  
Dwidjono Hadi Darwanto
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Gross Domestic Product ◽  
Palm Oil ◽  
Cross Sections ◽  
Panel Regression ◽  
Crude Palm Oil ◽  
Export Intensity ◽  
Economic Distance ◽  
Negative Effect

Palm oil is a superior product from Indonesia that is continuously and widely used for daily needs such as cooking, grooming, and manufacturing. However, this potential must be supported by oil palm business actors' performance to maintain its intensity and competitiveness. This study investigates how various factors affect Indonesia's crude palm oil (CPO) export intensity and competitiveness by employing panel regression and the basic gravity model. The panel data used here is a 20-year time series with cross-sections from five major importers from 1999 to 2018. The results show that the importer's gross domestic product (GDP) and quantity of export significantly and positively affect Indonesia's CPO export intensity, while the exporter's GDP and economic distance has a significant and negative effect. The factors that positively and significantly influence competitiveness are soybean's import value and Roundtable on Sustainable Palm Oil (RSPO) certification, while Malaysian CPO's export and population of importing countries negatively affect Indonesian CPO competitiveness.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

scholarly journals Causes for Deforestation in Indonesia: Corruption and Palm Tree Plantation

2015 ◽  
Vol 11 (27) ◽  
pp. 120
Author(s):  
Osama Eldeeb ◽  
Petr Prochazka ◽  
Mansoor Maitah
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Forest Cover ◽  
Palm Tree ◽  
Test Statistics ◽  
Crude Palm Oil ◽  
Tree Plantation ◽  
Long Run ◽  
Corruption Perception ◽  
Forest Cover Loss

<p>Indonesian biodiversity is threatened by massive deforestation. In this research paper, claims that deforestation in Indonesia is caused by corruption and supported by crude palm oil production is verified using time series analysis. Using Engel Granger cointegration test, three time series of data, specifically corruption perception index, rate of deforestation and price of crude palm oil are inspected for a long-run relationship. Test statistics suggests that there is no long-run relationship among these variables. Authors provide several explanations for this result. For example, corruption in Indonesia, as measured by CPI is still very high. This may mean that forest cover loss is possible even though there is a positive change in corruption level. According to the results, crude palm oil price has also no effect upon forest cover loss. This is likely due to very low shut-down price of crude palm oil for which production is still economical.</p>

Measurement of Detection Rate Accuracy in Forecasting Crude Palm Oil Production using Fuzzy Time Series

2021 ◽  
Author(s):  
Arif Ridho Lubis ◽  
Santi Prayudani ◽  
Yulia Fatmi ◽  
Al-Khowarizmi ◽  
Julham ◽  
...  
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Detection Rate ◽  
Oil Production ◽  
Fuzzy Time Series ◽  
Crude Palm Oil

scholarly journals Analisis Harga Tandan Buah Segar Kelapa Sawit di Provinsi Jambi

2021 ◽  
Vol 5 (2) ◽  
pp. 315-322
Author(s):  
Mulyani Mulyani ◽  
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Crude Palm Oil

Penelitian ini dilakukan untuk menganalisis faktor-faktor yang mempengaruhi harga tandan buah segar kelapa sawit di Provinsi Jambi. Data yang dikumpulkan akan diolah secara tabulasi dan dianalisis menggunakan software SPSS dengan melakukan uji F dan Uji t untuk melihat apakah jumlah produksi tandan buah segar (TBS), jumlah pabrik kelapa sawit yang ada di Provinsi Jambi (sebagai representasi permintaan kelapa sawit dalam Provinsi Jambi), Jumlah ekspor CPO (crude palm oil) Provinsi Jambi, dan harga CPO (crude palm oil) berpengaruh terhadap harga tandan buah segar (TBS) di Provinsi Jambi. Data yang digunakan adalah data time series tahun 2009- 2019. Hasil penelitian dengan uji F menunjukkan bahwa secara bersama-sama jumlah produksi tandan buah segar (TBS) kelapa sawit, jumlah pabrik kelapa sawit, jumlah ekspor CPO (crude palm oil) dan harga CPO (crude palm oil) berpengaruh signifikan terhadap harga tandan buah segar (TBS) kelapa sawit di Provinsi Jambi. Sedangkan hasil uji t menunjukkan bahwa jumlah produksi tandan buah segar (TBS) kelapa sawit, jumlah pabrik kelapa sawit, dan jumlah ekspor CPO (crude palm oil) tidak berpengaruh signifikan terhadap harga tandan buah segar (TBS) kelapa sawit di Provinsi Jambi. Sedangkan harga CPO (crude palm oil) berpengaruh signifikan terhadap harga tandan buah segar (TBS) kelapa sawit di Provinsi Jambi.

Dynamic Data Sampling Approach by Using Price Distribution of Crude Palm Oil to Forecast High Magnitude Price Movement

2019 ◽  
Vol 892 ◽  
pp. 106-113
Author(s):  
Kwan Hua Sim ◽  
Isaac Goh ◽  
Kwan Yong Sim
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Raw Materials ◽  
Palm Kernel ◽  
Price Volatility ◽  
Sampling Interval ◽  
Data Sampling ◽  
Crude Palm Oil ◽  
High Magnitude ◽  
Price Distribution

Traditionally the chemical industry uses coal, minerals and petroleum as its basic raw materials, but palm oil and palm kernel oil are being increasingly used as economical raw materials especially for the production of oleochemicals. High magnitude palm oil price volatility in recently years has been a major challenge faced the industry. Though many time series models have been developed, few have wide adoption in the industry, and one of the key issues is the sampling interval used in the models. To date, little effort has been spent on mining historical data to determine the representativeness of interval sampling. This paper presents a novel approach in identifying price equilibrium for crude palm oil by mining the sampling amount through historical price distribution. Evaluation is done on the outcomes of the experiment, and analysis is performed on the attributes of each different criteria of the price distribution. The performance of the proposed approach is also compared to the conventional Bollinger Band with static sampling size. Overall, the preliminary results show that price distribution with leptokurtic distribution outperforms other price distribution patterns, this will definitely assist further works to devise a novel financial time series analysis technique.

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN PERBANDINGAN ALGORITMA GREEDY DENGAN ALGORITMA GENETIKA PADA PREDIKSI RENTET WAKTU HARGA CRUDE PALM OIL

2017 ◽  
Vol 2 (1) ◽  
pp. 21-26
Author(s):  
Desy Ika Puspitasari
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Time Series ◽  
Palm Oil ◽  
Forward Selection ◽  
Crude Palm Oil

Penelitian ini menerapkan data mining pada prediksi harga CPO (Crude Palm Oil) dengan membandingkan pemodelan optimasi seleksi fitur algoritma genetika dan algoritma greedy pada metode neural network (NN). Prediksi harga CPO dilakukan untuk memenuhi kebutuhan investor kelapa sawit, melalui analisa masalah fluktuasi harga CPO time series yang tidak pasti. Guna mempermudah dalam melakukan perhitungan, langkah-langkah dari algoritma Genetika dan algoritma Greedy diimplementasikan dengan program komputer Rapid Miner Studio. Adapun tujuan penelitian ini yaitu mengetahui perbandingan akurasi dengan parameter evaluasi RMSE yang dihasilkan dan waktu eksekusi program yang dibutuhkan oleh algoritma Genetika dan algoritma Greedy dalam menyelesaikan masalah prediksi harga CPO. Hasil pengujian akurasi menunjukkan bahwa penggunaan metode NN optimasi algoritma Genetika secara umum memberikan nilai RMSE yang lebih baik (0,096) dibandingkan algoritma Greedy-forward selection (0,111) dan algoritma Greedy-backward selection (0,101). Akan tetapi jika ditinjau dari waktu eksekusi program yang dibutuhkan dalam menyelesaikan masalah prediksi harga CPO, maka algoritma Genetika membutuhkan waktu yang lebih lama dari pada algoritma Greedy.Kata Kunci: algoritma Genetika, algoritma Greedy, neural network, prediksi harga CPO, time series.

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