Forecasting Crude Palm Oil Prices Using Fuzzy Rule-Based Time Series Method

Rahim, Nur Fazliana; Othman, Mahmod; Sokkalingam, Rajalingam; Kadir, Evizal Abdul

doi:10.1109/access.2018.2846809

Cited by 27 publications

(12 citation statements)

References 20 publications

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“…Banyak tenaga kerja yang bergantung pada produksi kelapa sawit. Di Indonesia CPO menjadi kontributor yang memicu perkembangan investsasi, teknologi, dan manajemen pengetahuan [2]. Persaingan antar negara produsen CPO membuat harga CPO menjadi fluktuatif.…”

Section: A Crude Palm Oil (Cpo)unclassified

“…Minyak kelapa sawit banyak digunakan untuk berbagai keperluan seperti untuk pelumas, memasak, bahan pembuatan sabun, dan berbagai material barang lainnya. Di Indonesia CPO menjadi kontributor yang memicu perkembangan investsasi, teknologi, dan manajemen pengetahuan [2]. Produksi CPO merupakan aspek yang penting bagi kegiatan perekonomian di Indonesia.…”

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“…Data dibagi menjadi dua kelompok yaitu data training dan data testing. Merujuk pada penelitian terdahulu yang dilakukan oleh[18] proporsi yang digunakan untuk pembagian data pada penelitian ini adalah 75% untuk data training dan 25% untuk data testing 2…”

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Optimasi Fuzzy Artificial Neural Network dengan Algoritma Genetika untuk Prediksi Harga Crude Palm Oil

Rifa'i

2020

JuTISI

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Crude Palm Oil (CPO) is one of Indonesia's best export commodities. CPO production competition causes price fluctuations so that it can trigger losses. The solution that can be taken to avoid losses is to predict the price of CPO. Time series data in the previous months, starting from January 2009 until January 2020, are used as a reference to predict the next CPO price. In this research, CPO price prediction is carried out with a combination of artificial intelligence concepts, namely Radial Basis Function Neural Network (RBFNN), and fuzzy logic. The combination of these methods, namely Fuzzy Radial Basis Function Neural Network (FRBFNN), is then optimized using genetic algorithms. The prediction results show that the error based on the MAPE value for FRBFNN prediction on training data is 11.7% and the MAPE value for testing data is 9.4%. In the FRBFNN prediction that was optimized using a genetic algorithm, the MAPE value was 10.2% for training data and 8.3% for testing data.

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Section: A Crude Palm Oil (Cpo)unclassified

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Optimasi Fuzzy Artificial Neural Network dengan Algoritma Genetika untuk Prediksi Harga Crude Palm Oil

Rifa'i

2020

JuTISI

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“…To obtain forecasts, operators and the fuzzy rules obtained were applied to the FLRGs for each of the observations. The weighted subsethood-based algorithm which referred to [33] was used to develop the fuzzy rules. These operators and fuzzy rules were used to screen out or include the fuzzy relationships of the observations.…”

Section: Proposed Forecasting Modelmentioning

confidence: 99%

“…The research applies operators and fuzzy rules that were generated using the weighted subsethood-based algorithm for each date of the forecasts as in [33]. In Table 5, the research applies ∧ m (intersection operator) to all the forecasts obtained.…”

Section: According Tomentioning

confidence: 99%

Type 2 Fuzzy Inference-Based Time Series Model

et al. 2019

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Fuzzy techniques have been suggested as useful method for forecasting performance. However, its dependency on experts’ knowledge causes difficulties in information extraction and data collection. Therefore, to overcome the difficulties, this research proposed a new type 2 fuzzy time series (T2FTS) forecasting model. The T2FTS model was used to exploit more information in time series forecasting. The concepts of sliding window method (SWM) and fuzzy rule-based systems (FRBS) were incorporated in the utilization of T2FTS to obtain forecasting values. A sliding window method was proposed to find a proper and systematic measurement for predicting the number of class intervals. Furthermore, the weighted subsethood-based algorithm was applied in developing fuzzy IF–THEN rules, where it was later used to perform forecasting. This approach provides inferences based on how people think and make judgments. In this research, the data sets from previous studies of crude palm oil prices were used to further analyze and validate the proposed model. With suitable class intervals and fuzzy rules generated, the forecasting values obtained were more precise and closer to the actual values. The findings of this paper proved that the proposed forecasting method could be used as an alternative for improved forecasting of sustainable crude palm oil prices.

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