Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee

Muhammad, Mahadi; Wahyuningsih, Sri; Siringoringo, Meiliyani

doi:10.34312/jjom.v3i1.5940

Cited by 17 publications

(22 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The smaller its validation value, the more accurate the forecasting technique and it also applies otherwise. The result of forecasting can be classified as good if it has a value of MAPE less than 10% and its ability in forecasting is classified as good when its MAPE value is less than 20% [24].…”

Section: Mean Absolute Percentage Errormentioning

confidence: 99%

“…Calculation of the 𝐹 ̂(𝑡 + 1) forecast value based on step 7 using equation (21) Step 9. Calculation of the error value based on equation (24) Step 10. The error value (MAPE) obtained is then used as 𝑃 𝑏𝑒𝑠𝑡 and 𝐺 𝑏𝑒𝑠𝑡 to calculate PSO optimization.…”

Section: Proposed Model and Algorithmmentioning

confidence: 99%

“…PSO calculation will be done iteratively by fixing the lower bound and upper bound that have been formed previously. In equation (24), the value of w is obtained from equation (23). Initial 𝑉 𝑖𝑑 𝑡 values are determined randomly, where the value of 𝑋 𝑖𝑑 𝑡 is obtained from the previous interval.…”

Section: Proposed Model and Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

The New Approach Optimization Markov Weighted Fuzzy Time Series using Particle Swarm Algorithm

Surono¹,

Siregar²

2022

J.Edu.Sci.

View full text Add to dashboard Cite

Markov Weighted Fuzzy Time Series is a forecasting method that applies fuzzy logic to form linguistic variables from existing data. The formation of linguistic variables makes it possible for the forecasting process to be more accurate by considering the uncertainty aspect in decision-making. Its formation is started by grouping the data into a certain number of clusters. The next steps are fuzzification, transition matrix formation, and defuzzification for forecasting. In the process of grouping, the existing data will be grouped into several clusters so that it results in the interval length of each cluster. One of the problems of this grouping is the absence of a base standard in the clustering process so it is prone to have a different value in forecasting accuracy. The difference in the number of the class or interval length will result in different accuracy even though the clustering method that is used is the same. In this study, the author proposes the idea of using Particle Swarm Optimization to improve the interval length. The initial interval that is already obtained through the K-means clustering algorithm will be evaluated using the Particle Swarm Optimization method so that it will have a new interval that later will be used in the fuzzification process and forecasting. The accuracy of forecasting can be calculated by using Mean Absolute Percentage Error from Markov Weighted Fuzzy Time Series conventional method and Markov Weighted Fuzzy Time Series method with Particle Swarm Optimization. The result of this study gives an improvement in error value from 8.03% to 5.88%.

show abstract

Section: Mean Absolute Percentage Errormentioning

confidence: 99%

Section: Proposed Model and Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

The New Approach Optimization Markov Weighted Fuzzy Time Series using Particle Swarm Algorithm

Surono¹,

Siregar²

2022

J.Edu.Sci.

View full text Add to dashboard Cite

show abstract

“…Several time-series prediction methods for air quality measurements have been performed, including autoregressive integrated moving average (ARIMA) [2], support vector machine (SVM) [3], and fuzzy time series (FTS) [4]. The concept of FTS was introduced by Song and Chissom [5] through applying the principles of fuzzy logic in predicting a problem in which the actual data is converted into the form of linguistic values known as fuzzy sets [6,7]. The advantage of FTS is that it is able to predict linguistic data where it is impossible to calculate using ordinary time series methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fuzzy K-Medoids and Cat and Mouse-Based Optimizer for Markov Weighted Fuzzy Time Series

et al. 2023

View full text Add to dashboard Cite

This study seeks to test novel capabilities, specifically those of the hybrid fuzzy k-medoids (FKM) and cat and mouse-based optimizer (CMBO) partitioning approach, in overcoming the Markov weighted fuzzy time series (MWFTS) limitation in creating U talk intervals without fundamental standards. Researchers created a hybrid cat and mouse-based optimizer–fuzzy k-medoids (CMBOFKM) approach to be used with MWTS, since these limits may impair the accuracy of the MWFTS approach. Symmetrically, the hybrid method of CMBOFKM is an amalgamation of the FKM and CMBO methods, with the CMBO method playing a part in optimizing the cluster center of the FKM partition method to obtain the best U membership matrix value as the medoid value that will be used in the MWFTS’s fuzzification stage. Air quality data from Klang, Malaysia are used in the MWFTS–CMBOFKM technique. The evaluation of the model error values, known as mean absolute percentage error (MAPE) and root mean square error, yields the MWFTS–CMBOFKM evaluation findings that are displayed (RMSE). A 6.85% MAPE percentage and a 6071 RMSE score are shown by MWFTS–CMBOFKM using air quality data from Klang, Malaysia. The FKM partition approach can be hybridized with additional optimization techniques in the future to increase the MWFTS method’s precision.

show abstract

“…Meanwhile, FTS Lee has also been introduced, which is used to forecast short-term models on static and non-static data patterns. This concept has also been used in forecasting the exchange rate model of farmers in the livestock sub-sector [11] and forecasting the price of gold [12]. Furthermore, Tsaur in 2012 proposed the FTS Markov chain, which is also known as FTS Tsaur, where Tsaur combines the FTS method with the Markov chain in his research on the analysis of the accuracy of forecasting the Taiwan currency exchange rate against the US dollar [13].…”

Section: Introductionmentioning

confidence: 99%

Time Series Modeling of Natural Gas Future Price With Fuzzy Time Series Chen, Lee and Tsaur

Devianto

Zuardin

Maiyastri

2022

BAREKENG: J. Math. & App.

View full text Add to dashboard Cite

Investment is the process of investing money or capital for profit or material results. The investor carefully calculates the investment object to minimize losses and maximize profits. One of the essential investment objects is the futures price of natural gas considered a commodity that plays a vital role in the Indonesian economy. The movement of natural gas futures prices can be modeled using a time series model. The data in the time series model is believed to have particular pattern to model the data in the future. The natural gas futures price is modeled into a time series method by using fuzzy time series (FTS) approach of the FTS Chen, Lee and Tsaur. Model accuracy is calculated using the criteria of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The three FTS methods have good performance of accuracy for this time series data, where FTS Tsaur as fuzzy times series approach with average based method shows the best results with the smallest error rate to the data of natural gas future price.

show abstract

Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee

Cited by 17 publications

References 4 publications

The New Approach Optimization Markov Weighted Fuzzy Time Series using Particle Swarm Algorithm

The New Approach Optimization Markov Weighted Fuzzy Time Series using Particle Swarm Algorithm

Hybrid Fuzzy K-Medoids and Cat and Mouse-Based Optimizer for Markov Weighted Fuzzy Time Series

Time Series Modeling of Natural Gas Future Price With Fuzzy Time Series Chen, Lee and Tsaur

Contact Info

Product

Resources

About