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Sprite drink is a soda drink that is very popular among all groups. Demand is uncertain and always changes from time to time, making product availability difficult to control and often causes overstock or stockout problems. Therefore, inventory control is needed, which can be done by forecasting, determining safety stock and good re-order points. To obtain effective and efficient planning, the number of orders must be based on the number of past mass requests so as to reduce the occurrence of overstock or stockouts. With the problems experienced by PT. XYZ, the forecasting method used is the time series forecasting method. In this case, the time series methods used are Simple Average, Single Moving Average and also Single Exponential Smoothing. After carrying out several calculations, we obtained a Mean Absolute Centage Error (MAPE) value of 49.379%, a Mean Absolute Deviation (MAD) of 2297.145, a Root Mean Squared Error (RMSE) of 2912.495 and also a Mean Squared Error (MSE) of 8,482 .628 and forecasting results of 4504 pcs every month. Based on the calculation results, the proposal given is to reorder Sprite 250ML when the inventory in the warehouse reaches 1548 pcs with a safety stock of 540 pcs.

PT. Kalonika Bara Kusuma is a company operating in the mining sector located in the city of Samarinda, East Kalimantan province. To achieve maximum profits, PT. Kalonika Bara Kusuma adds or subtracts units according to the amount of turnover obtained in the previous month. However, after being evaluated, it turned out that this method was not effective. Because you only see at a glance the fluctuations in historical data. Sometimes when you have reduced units, it turns out that demand in the following month actually increases. This results in less than optimal profits because they cannot serve existing customer requests. Vice versa. This is what causes PT. Kalonika Bara Kusuma experienced difficulty in making a decision to add or subtract units. From this problem, the author created an application that can predict the amount of turnover in the next month and provide recommendations for deciding which camera units should be increased or decreased in number. To predict the amount of turnover using the Multiple Linear Regression method. After obtaining the predicted results for the amount of turnover, a test was carried out using the Mean Absolute Percentage (MAPE) with a result of 200%, which means that the Multiple Linear Regression method is not suitable to be used to predict the amount of turnover in the next period. Production forecasting is a form of decision making that is used as a basis in many manufacturing and service industries. Therefore, companies that are able to produce products on time and in the right quantities are companies that are able to survive the competition. This demand forecasting is used to forecast demand for products that are independent (not dependent), such as forecasting finished products. The multiple linear regression method is an analytical technique that tries to explain the relationship between two or more variables, especially between variables that contain cause and effect, called regression analysis. So in relation to the description above, this research aims to determine production forecasting using the multiple linear regression method at PT. Kalonica Bara Kusuma.The mining industry is a series of activities that have a long period of time and costs a lot of money, a series of industrial activities, namely mining activities which include digging, loading and hauling to obtain optimal profits from activities. One of the mining industries needs to be a study of operational costs for transportation equipment

This research aims to evaluate the effectiveness of linear regression as a forecasting tool to estimate the Provincial Minimum Wage (UMP) in Indonesia. Utilizing UMP data from various provinces during the period 2002-2022, this study employs linear regression to analyze the factors influencing UMP determination. The predicted UMP for North Sumatra in 2023 demonstrates a high level of accuracy (R-squared = 0.9678), affirming the potential of linear regression as an effective tool to understand regional economic dynamics. The research provides a crucial foundation for policymakers in regional economic planning and suggests avenues for further investigation, including exploring alternative prediction methods and analyzing the impact of UMP regulation policies.

Mitsubishi Chemical Indonesia is a producer of Purify Terepthalic Acid (PTA) in Indonesia with an important role in meeting the primary needs of the Indonesian population by managing raw materials to produce purified terepthalic acid. Purified terepthalic acid is the main raw material for polyester fiber. The problem that occurs is the phenomenon of the bullwhip effect, namely the occurrence of fluctuations between orders and demand which results in a shortage of raw materials or low inventory which can cause production to stop. So the purpose of this study is to calculate the value of the bullwhip effect, determine the causes of the bullwhip effect, and determine solutions to reduce the bullwhip effect. This study begins by calculating the value of the bullwhip effect at the two echelons. The method used in this research is FMEA. The results of this study are the value of the bullwhip effect at echelon 1 (suppliers and PPIC) = 1.27 and echelon 2 (PPIC and distributors) = 0.93. This value indicates the occurrence of a bullwhip effect in echelon 1 while echelon 2 does not indicate the occurrence of a bullwhip effect. The dominant causes of the bullwhip effect are market competition, rising material prices, demand forecasting that is not close to accurate and the number of orders for raw materials in large quantities. The proposed improvement is to collaborate with suppliers and customers to get guaranteed orders and demand that are fixed and sustainable. This value indicates the occurrence of a bullwhip effect in echelon 1 while echelon 2 does not indicate the occurrence of a bullwhip effect. The dominant causes of the bullwhip effect are market competition, rising material prices, demand forecasting that is not close to accurate and the number of orders for raw materials in large quantities. The proposed improvement is to collaborate with suppliers and customers to get guaranteed orders and demand that are fixed and sustainable. This value indicates the occurrence of a bullwhip effect in echelon 1 while echelon 2 does not indicate the occurrence of a bullwhip effect. The dominant causes of the bullwhip effect are market competition, rising material prices, demand forecasting that is not close to accurate and the number of orders for raw materials in large quantities. The proposed improvement is to collaborate with suppliers and customers to get guaranteed orders and demand that are fixed and sustainable.      

Kruskal's algorithm in searching for minimum spanning trees can be applied to pipelines installed at the location of PDAM Tirtanadi Tuasan where problem identification starts with the water discharge reaching the consumer is small but the discharge flowing from the reservoir is sufficient, so this research is used as a solution to this problem and also as an optimization of the clean water distribution network in the Tirtanadi Regional Drinking Water Company (PDAM) of the Tuasan Branch with the intention of cutting the direction of the pipe flow to overcome this problem. The data obtained from PDAM Tirtanadi Tuasan Branch is in the form of a floor plan and formed into a weighted graph. After the data is obtained, then it is calculated manually that the length of the installed water pipe is 32,645 m with 86 vertices and 100 edges, then the pipe length is represented as a set of paths and the pipe connection ends are represented as nodes. The pipe length obtained using Kruskal's algorithm and inspection of iterations using the QM for windows software is 22,095 m, with 86 vertices and 85 edges. So, using the Kruskal Algorithm and the help of the QM for windows software, the difference in pipe length obtained is 10,610 m.

Every business must strive to remain stable in its performance in order to survive competition. In Kotagede Yogyakarta, UMKM Yangko Sari Roso produces food. Businesses that are developing in the current era of globalization are characterized by strong competition in all fields, such as business in industry, trade and services. Because the company only produces for one shop and entrusts products to several distributors, Yangko's MSME sales strategy is still not well chosen. In addition, making product packaging that is considered quite expensive and unattractive results in poor sales, which results in high inventories in warehouses and cost losses. The ultimate goal of this work is to find the best solution to the problem of marketing and production costs. The ARIMA method is used to forecast Yangko's short-term sales, production costs, and packaging proposals. The aim of this method is to provide the best results for finding marketing decisions. The results of this research, namely the analysis of marketing strategy observations, show that the company can overcome this problem and find ways to make production costs more efficient by increasing labor.

The role of fractal geometry in analyzing growth patterns of tropical plants and its application in precision agriculture has become an emerging interdisciplinary topic in the modern era. Tropical plants often exhibit complex and irregular structures that cannot be fully described by conventional Euclidean geometry. This study aims to examine fractal-based mathematical models to identify self-similar patterns in tropical leaves and to explore their potential for optimizing precision farming practices. The methodology employs image-based mathematical analysis, using digital images of tropical plants to measure fractal dimensions and quantify growth complexity. The findings reveal that consistent fractal patterns can be observed across different species of tropical plants, particularly in leaf venation and branching structures, indicating a universal growth principle. Such patterns demonstrate high predictive potential for estimating biomass, monitoring plant health, and assessing responses to environmental changes. Furthermore, the study highlights how fractal-based approaches, when combined with precision agriculture technologies, can improve resource efficiency by supporting accurate irrigation scheduling, soil quality monitoring, and yield forecasting. The implications extend to sustainable agricultural development, as fractal analysis provides a scientific foundation for balancing productivity with environmental preservation. In conclusion, this research underscores the significance of fractals not only as mathematical concepts but also as powerful analytical tools with practical benefits, offering new pathways to advance digital farming, ecological monitoring, and sustainable food security in the modern era.

The daily needs of the people of Central Aceh cannot be separated from agricultural commodities such as tomatoes, shallots, garlic, and others. Some of these agricultural commodities have sharp price fluctuations, such as tomatoes. When the supply of tomatoes in the market is reduced, the price can be much higher than the normal price. Conversely, when the supply of tomatoes is excessive, the price will fall far below the normal price. This is influenced by various factors such as the harvest season, the amount of production, the amount of public consumption and others. Based on these problems, we need a method to be able to estimate the price of tomatoes so that it can be used to support decision making related to price issues. Forecasting is one of the solutions to be able to estimate the movement of tomato commodity prices. The method used for forecasting tomato prices is High Order Fuzzy Times Series Multifactors. In this method, subinterval formation is carried out using Fuzzy C–means. To calculate the error rate of forecasting results in this study using the Mean Square Error (MSE). Based on the results of the tests carried out, the large values ​​of the training and order data used in forecasting do not guarantee a low error rate.

Company CV. AM Nanda Putra is located in Sidoarjo and operates in the scaffolding industry. Currently the company is experiencing losses due to a lack of optimization in planning the amount and time of ordering raw materials, which results in shortages and excess material inventory. To overcome this problem, the company uses the MRP (Material Requirement Planning) method in optimizing raw material planning. In terms of the lot sizing approach, the company applies the LFL and EOQ methods. The forecasting methods used are Moving Average (MA), Weight Moving Average (WMA), and Exponential Smoothing (ES). The smallest MAD results were obtained using the Exponential Smoothing (ES) method for all scaffolding products. The forecasting results are obtained to determine the MPS (Master Production Schedule) for the next 10 months. After the determination of  MPS, the results of Material Requirement Planning (MRP) were obtained, namely the supply of raw materials for MF 170 AM scaffolding of 35402 units or 17700 sets, MF 170 K1 scaffolding of 28906 units or 14453 sets, MF 190 AM scaffolding of 16250 units or 8125 sets, and MF 190 K1 scaffolding of 7656 units or 3828 sets. From the results of calculating the cost of raw material requirements using the Lot for Lot (LFL) method and the Economic Order Quantity (EOQ) method, it can be seen that the total cost of planning the smallest raw material inventory with an amount of Rp. 12,975,818,022.

Electrical energy is one of the most important things in human life. Electrical energy is needed in the industrial sector. In meeting the needs of electrical energy, good planning is needed by predicting the needs of electrical energy. Holt's Double Exponential Smoothing method is a method that can be used to predict electrical energy needs. The results of forecasting the demand for electrical energy at PT PLN (Persero) for the North Sumatra Region for 2022 to 2030 use the Double Double Exponential Smoothing method from Holt (with a value of α = 0.99 and γ = 0.1 which has a MAPE value of 2.0372%. ) namely 13933.19 gwh, 14478.46 gwh, 15023.73 gwh, 15569.00 gwh, 16114.26 gwh, 16659.53 gwh, 17204.80 gwh, 17750.06 gwh, 18295.33 gwh.

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.