Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee

Muhammad, Mahadi; Wahyuningsih, Sri; Siringoringo, Meiliyani

doi:10.34312/jjom.v1i1.5940

Cited by 4 publications

(4 citation statements)

References 1 publication

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“…One method of calculating the model accuracy is the Mean Absolute Percentage Error (MAPE) which determines the error generated from the related model. Therefore, forecasting results are excellent when they have a MAPE value of less than 10% [41,42].…”

Section: -1-mean Absolute Percentage Errormentioning

confidence: 99%

Optimization of Markov Weighted Fuzzy Time Series Forecasting Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

et al. 2022

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The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making predictions based on developing a fuzzy time series (FTS) algorithm. The MWTS has overcome certain limitations of FTS, such as repetition of fuzzy logic relationships and weight considerations of fuzzy logic relationships. The main challenge of the MWFTS method is the absence of standardized rules for determining partition intervals. This study compares the MWFTS model to the partition methods Genetic Algorithm-Fuzzy K-Medoids clustering (GA-FKM) and Fuzzy K-Medoids clustering-Particle Swarm Optimization (FKM-PSO) to solve the problem of determining the partition interval and develop an algorithm. Optimal partition optimization. The GA optimization algorithm’s performance on GA-FKM depends on optimizing the clustering of FKM to obtain the most significant partition interval. Implementing the PSO optimization algorithm on FKM-PSO involves maximizing the interval length following the FKM procedure. The proposed method was applied to Anand Vihar, India’s air quality data. The MWFTS method combined with the GA-FKM partitioning method reduced the mean absolute square error (MAPE) from 17.440 to 16.85%. While the results of forecasting using the MWFTS method in conjunction with the FKM-PSO partition method were able to reduce the MAPE percentage from 9.78% to 7.58%, the MAPE percentage was still 9.78%. Initially, the root mean square error (RMSE) score for the GA-FKM partitioning technique was 48,179 to 47,01. After applying the FKM-PSO method, the initial RMSE score of 30,638 was reduced to 24,863. Doi: 10.28991/ESJ-2022-06-06-010 Full Text: PDF

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Section: -1-mean Absolute Percentage Errormentioning

confidence: 99%

Optimization of Markov Weighted Fuzzy Time Series Forecasting Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

et al. 2022

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“…Peramalan merupakan salah satu teknik untuk memprediksi kejadian yang akan datang dengan mengamati data masa lalu maupun data masa kini. Peramalan terjadi karena adanya kebutuhan pada masa mendatang dengan peristiwa itu sendiri dalam jangka waktu tertentu (Muhammad, dkk., 2021). Data yang digunakan dalam peramalan menggunakan data time series (data runtun waktu).…”

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Forecasting Shallot Prices in West Sumatra Province Using the Fuzzy Time Series Method of the Singh Model and the Cheng Model

Khaira

Fitri²,

Amalita³

et al. 2023

ujsds

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Shallots are one of the leading spices that are widely used by humans as food seasoning and traditional medicine. The price of shallots always fluctuates which can affect the buying and selling of consumers and producers. Therefore, forecasting is used as a reference to be able to predict the price of shallots in the future and can provide convenience to the public for the condition of shallot prices in the next period. The forecasting method used is the fuzzy time series (FTS) method. FTS is a method whose forecasting uses data in the form of fuzzy sets sourced from real numbers to the universe set on actual data. Forecasting models used in this study are Singh's FTS model and Cheng's model. The data used is monthly data on shallot prices in West Sumatra Province for the period January 2018 to March 2022. The results obtained in this forecast are the Singh model FTS has a smaller MAPE value of 4.41% with a forecasting accuracy value of 95.59 %. This means that Singh's FTS model is better at predicting the price of shallots in West Sumatra Province.

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“…dianalisis menggunakan pola keterhubungan antara variabel penelitian dengan variabel waktu yang dicatat secara beruntun [2]. Metode analisis time series memiliki beberapa pilihan dalam peramalan data, seperti ARIMA, SARIMA, smoothing, dan lain-lain, namun metode ini membutuhkan data historis dalam jumlah besar dan asumsi-asumsi yang harus dipenuhi [3]. Seiring dengan perkembangan metode time series, banyak pilihan metode yang digunakan untuk menganalisis data time series, salah satunya menggunakan fuzzy time series.…”

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Perbandingan Tingkat Akurasi Metode Average Based Fuzzy Time Series Markov Chain dan Algoritma Novel Fuzzy Time Series

Al-adawiyah

Alisah

Aziz

2022

JRMM

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Fuzzy time series method can be applied in predicting the situation in food price development data such as rice. The position of rice as a staple food has resulted in this commodity being one of the indicators of economic growth. The importance of suppressing rice prices so that they are stable can be done by forecasting rice prices in Indonesia in the future. The research method used for forecasting is average based fuzzy time series Markov chain and novel algorithms fuzzy time series. Researchers will compare the two methods in the case of rice prices by looking at the level of accuracy that is better. The data used in this study is the average monthly rice price at the wholesale trade level from January 2015 to March 2021 in units of Rp/Kg as much as 75 data. The results of the comparison of the level of accuracy using the value of Mean Absolute Percentage Error (MAPE), obtained the forecast of the average price of rice at the Indonesian wholesale trade level for average based fuzzy time series Markov chain which is 0.36%, while the MAPE value for novel algorithm fuzzy time series is 0.19%. Based on the MAPE results, it can be concluded that the novel algorithm method fuzzy time series produces a better level of accuracy compared to the method average based fuzzy time series Markov chain.

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Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee

Cited by 4 publications

References 1 publication

Optimization of Markov Weighted Fuzzy Time Series Forecasting Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

Optimization of Markov Weighted Fuzzy Time Series Forecasting Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

Forecasting Shallot Prices in West Sumatra Province Using the Fuzzy Time Series Method of the Singh Model and the Cheng Model

Perbandingan Tingkat Akurasi Metode Average Based Fuzzy Time Series Markov Chain dan Algoritma Novel Fuzzy Time Series

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