Penggunaan Metode Smoothing Eksponensial Dalam Meramal Pergerakan Inflasi Kota Palu

Biri, Romy; Langi, Yohanes; Paendong, Marline

doi:10.35799/jis.13.1.2013.2035

Cited by 10 publications

(9 citation statements)

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“…The algorithm built can predict the possibility that will occur in the next few times. Data that has "feature extraction" from a form of value will later be analyzed and processed in a classification manner through calculations and comparisons, after which the data is filtered to eliminate data randomness [11]. The data will be processed and divided into two parts: data training and testing [12].…”

Section: Research Methodology 21 Research Stages Figure 1 System Designmentioning

confidence: 99%

The Application Of Multi-Sensor Data Fusion Method with Fuzzy Time Series Model to Improve Indoor Water Prediction Accuracy Quality

Khoiri

Erfianto

2023

bits

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There is a lot of indoor air pollution, especially from cigarette smoke, wall paint, air fresheners and gas. With this situation, the room uses Air Box WP6003 air quality detection device by transmitting information about air quality through visualization index. This study aims to improve prediction accuracy with fuzzy time series methods processed through 2 naïve and moving average models using forecast transformers and without transformers. The level of prediction accuracy is calculated through several metrics, namely Mean Absolute Percentage Error (MAPE), Sum of Squares Error (SSE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). These results can be calculated between the actual value and the predicted value. The data used is 204584 data from 4 parameters including Temperature, TVOC, HCHO and CO2. The test results with the difference from the forecast transformer and without transformer are comparable. Temperature value obtained using naïve with transformer from RMSE of 0.158866 and naïve without transformer of 0.782397, data using moving average with transformer obtained by 0.147546 and moving average without transformer of 0.772570. This can be explained by the error analysis that was tried, where the error rate continued to increase so that the experimental results continued to be far from the actual number. From the test results it can be concluded that the accuracy of air quality prediction using naïve forecast transformer is pretty accurate.

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Section: Research Methodology 21 Research Stages Figure 1 System Designmentioning

confidence: 99%

The Application Of Multi-Sensor Data Fusion Method with Fuzzy Time Series Model to Improve Indoor Water Prediction Accuracy Quality

Khoiri

Erfianto

2023

bits

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“…The SES method is a forecasting technique developed from simple moving averages [27]. This method is carried out with several levels of α, starting from 0.1 to 0.9.…”

Section: Single Exponential Smoothing Methodsmentioning

confidence: 99%

Forecasting Model Selection of Curly Red Chili Price at Retail Level

Sukiyono

2019

InJAR

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Chilli is one of strategic commodity in Indonesia due to its contribution to inflation level. For this reason, future price information is very importance for designing price policy. Future price merely can be provided by conducting a price forecasting. Various forecasting models can be applied for this purpose; the problem is which the best model for forecasting is. This study aims to select the most accurate forecasting model of curly red chili prices at the retail level. The data used are monthly data, from 2011 - 2017. Five forecasting models are applied and estimated including Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, Decomposition, and ARIMA. The best model is selected based on the smallest MAPE, MSE and MAD values. The results show that the most accurate forecasting model is ARIMA (1,1,9).

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“…This method is also called a single exponential smoothing that is commonly used by traders for short-term forecasting. The model assumes if data fluctuates around a fixed mean value and also without a trend or consistent growth pattern [11]. Unlike the Moving Average, Exponential Smoothing will offer greater emphasis at a time through the use of a smoothing constant.…”

Section: E Single Exponential Smoothingmentioning

confidence: 99%

Forecasting the Inflation using Hybrid SARIMA-Single Exponential Smoothing for Determining Minimum Costs of Living Index in Bandung City

Sumitra¹,

Fachrudin²,

Supatmi³

2019

Proceedings of the Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITES

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This research aims to produce inflation data forecast and then to forecast the results to an estimation of the Costs of Living Index (KHL). However, this research is limited and merely discuss the KHL by categories of food and beverages entirely. Although inflation could be predicted with high accuracy, surely it could rely upon for making the Government in anticipating future economic activity. Based on previous research, the SARIMA method and Single Exponential Smoothing could show the results of forecasting that is adequate to follow; the value of inflation can be measured by the movements of the actual data. However, this study tried to combine these two methods where the results of the combined method produce more accurate forecasting compared to the results that only used a single method. Based on the results of merging SARIMA with a model (1,0,1) (1,0,1) 12 and Single Exponential Smoothing with an alpha value of 0.6 overall, produces the smallest error value with MAD 114, MSE 0,017 and 0,39% for MAPE. This indicates that the results of the forecasting method of SARIMA method and Single Exponential Smoothing value against inflation are very superfine and accurate. After that, the results of the inflation forecasting needs to be calculated with the value of KHL to become a reference for forecasting the value of KHL for a similar period with inflation forecasting results.

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Penggunaan Metode Smoothing Eksponensial Dalam Meramal Pergerakan Inflasi Kota Palu

Cited by 10 publications

References 0 publications

The Application Of Multi-Sensor Data Fusion Method with Fuzzy Time Series Model to Improve Indoor Water Prediction Accuracy Quality

The Application Of Multi-Sensor Data Fusion Method with Fuzzy Time Series Model to Improve Indoor Water Prediction Accuracy Quality

Forecasting Model Selection of Curly Red Chili Price at Retail Level

Forecasting the Inflation using Hybrid SARIMA-Single Exponential Smoothing for Determining Minimum Costs of Living Index in Bandung City

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