Using fuzzy time series with and without markov chain: to forecast of edible bird nest exported from Indonesia

Edible bird nest (EBN) were traditional medicine consumed by the Tiongkok. This study compared two-algorithm method. Fuzzy time series and Markov chain as forecast method the number of bird nest exported from Indonesia. The secondary data between 2012 and 2018 were from Bureau Central Statistic (BPS). The scope using in this study were bird nest between 2012 until 2018, with a unit of volume kilograms (Kg). Used secondary export data, collected from BPS of Indonesia. Data were analysed using Fuzzy Time Series with and without Markov Chain using R Studio. The result showed that Fuzzy Time Series with and without Markov Chain method performs better in the forecasting ability in short-term period prediction and the values of Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE) tends to be smaller than the Fuzzy Time Series without Markov Chain. It can be concluded the number of exported can be used Fuzzy time series.

PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHEN UNTUK PERAMALAN HARGA PALADIUM

Palladium is one of the precious metal commodities with the best performance since 3 years ago. Palladium has many benefits, including being used in the electronics, medical, jewelry and chemical industries. The benefits of palladium in the chemical field are that it can help speed up chemical reactions, filter out toxic gases in exhaust gases, and convert the gas into safer substances, so palladium is usually used as a catalyst for cars. Forecasting is a process of processing past data and projected for future interest using several mathematical models. The model used in this study is the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods. The process of forecasting palladium prices using monthly data from January 2011 to December 2020 with the Double Exponential Smoothing Holt method and the Fuzzy Time Series Chen method will be carried out in this study to describe the performance of the two methods. Based on the results of the analysis, it can be concluded that the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods have equally good performance with sMAPE values of 6.21% for Double Exponential Smoothing Holt and 9.554% for Fuzzy Time Series Chen. Forecasting for the next 3 periods using these two methods generally produces forecasting values that are close to the actual data.

Peramalan Suku Bunga Acuan (BI-7 Day Repo Rate) Dengan Metode Fuzzy Time Series

BI-7 Day Repo Rate merupakan suku bunga kebijakan yang mencerminkan sikap atau stance kebijakan moneter yang ditetapkan oleh Bank Indonesia dan diumumkan kepada publik. BI 7-Day Repo Rate sangat mempengaruhi banyak sektor ekonomi, yang pada akhirnya dapat mempengruhi tingkat atau laju inflansi. Para pelaku ekonomi sangat memperhatikan BI-7 Day Repo Rate yang ditetapkan oleh Dewan Gubernur. Apabila suku bunga kebijakan yang ditetapkan oleh Dewan Gubernur tidak sesuai dengan tren kondisi ekonomi diwaktu tertentu maka akan berdampak negatif kepada kondisi ekonomi Indonesia. Hal inilah yang menyebabkan pentingya dilakukan peramalan BI-7 Day Repo Rate dengan harapan para pelaku ekonomi dapat mengantisipasi dampak jangka panjang dari penetapan BI-7 Day Repo Rate ini. Metode yang digunakan dalam penelitian ini adalah fuzzy time series dengan model terbaik orde 15 dengan tingkat akurasi yang dihitung menggunakan MAPE sebesar . Metode fuzzy time series bekerja dengan menangkap pola dari data historis kemudian digunakan untuk memproyeksikan data yang akan datang. Hasil penelitian ini menunjukkan bahwa pergerakan tingkat Suku Bunga Acuan dari bulan Desember 2020 – Desember 2021 adalah rentang .