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Perum Bulog as a State-Owned Enterprise has the main task, which is to conduct a quality and adequate basic food logistics business for the survival of the people. The problem that occurred in Perum Bulog Sub Divre Medan is that the rice supply in Bulog does not consider the demand in the market. Forecasting is an important tool in effective and efficient planning. Therefore, prediction is indispensable for predicting future events. This method essentially uses past data initiated by performing an exponentially decreasing weighting of older observational values or newer values. Brown's double exponential smoothing is a linear model proposed by Brown. This double exponential smoothing method is used when the data indicate a trend. In this study, the terbaik best parameter for forecasting the Number of Rice Sales in Perum Bulog Sub Divre Medan was α = 0.2 with MAPE of 0.27%. And the results of the forecast for Rice Sales at Perum Bulog Sub Divre Medan in 2022 are decreasing every month.

This research to analyze the contribution of variables from three economic policies, with monetary policy through interest rate variables, exchange rates, and money supply in facing economic recession. Where the fiscal policy variable is through tax value. Then macroprudential policy through Non Performing Loan and Capital Adequacy Ratio variables. This study uses secondary data or time series, namely from December 2019 to February 2021. The data analysis model in this study is the Vector Autoregression (VAR) model which is seen from being sharpened with Impulse Response Function (IRF) analysis and Forecast Error Variance Decomposition (FEVD), Panel ARDL, and Different Tests. The results of the IRF analysis show that the stability of the response of all variables is formed in period 8 or the medium and long term, where the response of other variables to changes in one variable shows different variations, both from positive responses to negative responses or vice versa, and there are variables whose responses remain positive or remain negative from the short term to the long term. The results of the FEVD analysis show that for the short-term inflation variable it is influenced by inflation itself and in the medium and long term it is influenced by interest rates. For the JUB variable in the short term it is influenced by JUB itself and in the medium and long term it is influenced by NPL. For the interest rate variable in the short term it is influenced by JUB while in the medium and long term it is influenced by the exchange rate itself and CAR. For the tax variable in the short, medium and long term it is influenced by the tax itself and JUB. For the NPL variable in the short, medium and long term it is influenced by JUB and tax. For the CAR variable in the short, medium and long term it is influenced by JUB and tax. Then the results of the ARDL Panel analysis show that the country that is able to become a leading indicator in controlling the economic recession in the Four of The Group Twenty, namely Turkey, is only done by interest rates. While South Africa is done by interest rates, taxes, NPL, and CAR. For Russia, it is done by all variables, namely the amount of money in circulation, interest rates, exchange rates, taxes, NPL, and CAR. Meanwhile, Indonesia is carried out by exchange rates, taxes, NPL and CAR.

Abstrak – Wabah Covid 2019 adalah penyakit menular serta dapat menyerang organ pernapasan yang sangat mematikan di Negara Tiongkok. Masyarakat Indonesia yang terjangkit virus Covid 2019 ini perlu dilakukan peramalan untuk mengetahui jumlah kasus masyarakat yang terjangkit wabah Covid 2019 pada bulan berikutnya. Dengan menggunakan Metode Single Exponential Smoothing, Double Exponential Smoothing, dan Triple Exponential Smoothing ini kita gunakan pada aplikasi RStudio untuk mengetahui nilai parameter α, β, dan γ kita dapat mengetahui perbandingan dari ketiga metode tersebut. Dari ketiga metode tersebut akan menggunakan parameter nilai α, β, dan γ. Dari ketiga metode tersebut dicari nilai SSE yang terkecil. Dengan mengetahui nilai SSE yang terkecil maka akan di dapatkan hasil peramalan yang lebih akurat. Data yang saya gunakan berjumlah 30 periode. Dengan menggunakan 30 periode kita mendapatkan nilai SSE terkecil 33042318. Dengan nilai tersebut kita mendapatkan nilai coefficient 1179.6161 atau masyarakat yang terjangkit wabah covid 2019 pada hari berikutnya berjumlah 1741 orang. Dengan dilakukannya penelitian ini diharapkan untuk setiap masyarakat dapat menjaga kesehatannya dengan cara menjaga kesehatan, kebersihan, serta mengkonsumsi makanan yang sehat dan bergizi sehingga dapat terhindar dari virus covid-19. Dengan menggunakan Metode Single, Double, Triple Exponential Smoothing kita dapat meramalkan kasus covid-19 di Indonesia selama beberapa bulan kedepan.   Abstract – The 2019 Covid outbreak is an infectious disease and can attack the respiratory organs which is very deadly in China. For the Indonesian people who have been infected with the 2019 Covid virus, forecasting needs to be done to find out the number of community cases infected with the 2019 Covid outbreak in the following month. By using the Single Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing methods, we use the RStudio application to determine the value of the parameters α, β, and γ we can find out the comparison of the three methods. Of the three methods will use the parameter values ​​ α, β, and γ. From the three methods, the smallest SSE value is sought. By knowing the smallest SSE value, more accurate forecasting results will be obtained. The data that I use is 30 periods. By using 30 periods, we get the smallest SSE value of 33042318. With this value we get a coefficient value of 1179.6161 or the people who were infected with the 2019 covid outbreak on the next day amounted to 1741 people. With this research, it is hoped that every community can maintain their health by maintaining health, hygiene, and consuming healthy and nutritious food so that they can avoid the Covid-19 virus. By using the Single, Double, Triple Exponential Smoothing method, we can predict COVID-19 cases in Indonesia over the next few months.

Abstrak – Obat merupakan bahan biologis yang sangat penting digunakan untuk penyembuhan dan peningkatan kesehatan untuk manusia. Kebutuhan obat akan semakin terus meningkat seiring dengan menuanya penuduk, sehingga diperlukan peramalan penjualan ketersediaan obat. Peramalan merupakan proses menyusun informasi untuk mendapatkan informasi yang baru. Peramalan memiliki banyak metode, pada penelitian ini menggunakan Metode Single, Double, dan Triple Exponential Smoothing dengan menggunakan studi kasus obat. Ketiga algoritma ini akan dilakukan perbandingan untuk mengetahui metode mana yang terbaik dalam peramalan. Hasil penelitian ini menunjukan metode Triple Exponential Smoothing merupakan metode yang terbaik degan nilai SSE 3306.302, jika dibandingan dengan Singel Exponential Smoothing sebesar 3945.069 dan Double Exponential Smoothing sebesar 4673.829.   Abstract – Medicine is a very important biological material used for healing and improving health for humans. The need for drugs will continue to increase along with the aging of the population, so it is necessary to forecast sales of drug availability. Forecasting is the process of compiling information to obtain new information. Forecasting has many methods, in this study using the Single, Double, and Triple Exponential Smoothing method using drug case studies. These three algorithms will be compared to find out which method is the best in forecasting. The results of this study indicate that the Triple Exponential Smoothing method is the best method with an SSE value of 3306,302, when compared with Single Exponential Smoothing of 3945,069 and Double Exponential Smoothing of 4673,829.

Accurate weather prediction information is important for various fields that are closely related to weather forecasting, such as agriculture, fisheries and many more. Because precise weather forecasts are very useful for various fields of carrying out various activities. Because of that, it is necessary to make an application to find weather or rainfall prediction information, so that the information can be utilized optimally by the community. In this journal the authors apply the k-nearest neighbors (k-NN) method based on rainfall data obtained from the Bogor climatology station from 2016-2017 and the test results show that the predicted rainfall for the Bogor area with the K-Nearest Neighbor algorithm obtained a value of 0, 93148.  

The AWD Mranggen store is a store that is engaged in the sale of bags, belts, shoes with sales developments increasing from year to year, with fairly tight business competition, the AWD Mranggen store must be able to calculate the estimated number of items to be purchased based on previous sales data, the prediction is very influential on the decision to determine the number of items to be provided by the AWD Mranggen Store for the next sales period data. Inventory of goods that are not right cause some losses in terms of time and also costs, it is necessary to have a forecasting system. Forecasting is a technique to identify a model that can be used to predict conditions in the future. By using the weight moving average method, it can be seen that the error value is more than smaller than other methods and the estimated results can be more precise so that it can help owners make decisions in carrying out inventory.

Forecasting is an estimate of future demand based on several forecasting variables based on historical time series or a process of using historical data (past data) that has been owned to use this model and use this model to estimate future conditions.The Ivori mini market SME group is known to be a mini market that sells daily necessities. The goods provided by the ivori mini market are not focused on only one type of goods, but include all types of goods. Ivori mini market often runs out of stock because there is no inventory planning. The main purpose of making this application is to assist employees in determining inventory planning that must be provided next month. While the method used to make this forecast is a single moving average, one of the time series methods in forecasting. Single Moving Average is a forecasting method that is done by collecting a group of observed values, looking for the average value as a forecast for the future period. The result of this forecasting is to predict the number of sales that will occur in the coming month.

Kemudahan proses pembelian rumah menyebabkan peningkatan permintaan akan produksi rumah. Citragrand merupakan satu dari banyak perusahaan  real estate di Semarang, Jawa Tengah. Penjualan yang tidak menentu setiap bulannya, membuat perusahaan kesulitan dalam menentukan target penjualan pada awal tahun dan juga dalam menentukan top product di tahun tersebut. Dengan masalah tersebut, perusahaan membutuhkan solusi yang dapat meramalkan penjualan di tahun mendatang, serta menentukan tipe rumah apa yang bisa menjadi top product hingga penjualan paling sedikit, sehingga dapat memperbaiki kualitas rumah dan menguntungkan perusahaan. Peramalan penjualan atau forecasting adalah metode analisa perhitungan dengan hasil perkiraan peristiwa di masa depan yang membutuhkan data masa lalu sebagai referensi dan memakai pendekatan kualitatif ataupun kuantitatif. Forecasting yang digunakan dalam hal ini menggunakan metode Double Exponential Smoothing. Selain di ramalkan penjualannya, juga dilakukan proses perangkingan produk perumahan terbaik yang dihitung menggunakan metode simple additive weighting (SAW). Dengan menggabungkan forecasting dan perangkingan produk, diharapkan mampu mengatasi salah satu masalah penjualan yang dialami oleh perusahaan Citragrand Semarang. Kedua metode ini dibuat dengan berbasis website yang dibangun dengan bahasa permrogaman PHP dan framework Codeigniter sebagai server program. Hasil dari sistem ini yaitu Sistem Pendukung keputusan dan peramalan dengan tujuan mendapatkan hasil top product dan peramalan penjualan untuk tahun berikutnya.

Strategic capacity involves an investment decision that must match resource capabilities to a long term demand forecast. This paper explain factors to be taken into account in selecting capapcity additions for both manufacturing and service include : the likely effects of economies of scale, the effects of experience curve, the impact of changing facility focus and balance among production stages and the degree of flexibility of facilities and the workforce.