Prediksi Penjualan Kertas Menggunakan Metode Double Exponential Smoothing

Putro, Erinsyah Aditya Nugroho; Rimawati, Elistya; Vulandari, Retno Tri

doi:10.30646/tikomsin.v9i1.548

Cited by 16 publications

(16 citation statements)

References 2 publications

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“…Suatu teknik yang dapat meramalkan dan menduga kondisi yang akan terjadi pada masa depan berdasarkan kejadian masa lampau dan sekarang sehingga menghasilkan keputusan merupakan pengertian forecasting [11], [12]. Mengantisipasi kemungkinan keadaan yang akan terjadi selanjutnya membutuhkan rangkaian perencanaan dan akan sangat berguna diberbagai bidang kehidupan [13].…”

Section: Pendahuluanunclassified

Implementasi Triple Exponential Smoothing dan Double Moving Average Untuk Peramalan Produksi Kernel Kelapa Sawit

Sandika,

Gusti,

Handayani

et al. 2023

josh

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The production of palm kernel is a significant product for the company and plays a crucial role. Nevertheless, the stability of kernel production is not always consistent, and the quality of the kernel can be detrimental to the company. As consumer demands change over time, companies must anticipate every fluctuation in palm kernel production. Hence it is vital to figure the long run with a settlement prepare utilizing information mining utilizing information within the past. The Triple Exponential Smoothing and Double Moving Average methods, which are data mining methods for future forecasting, were used in this study. The aim of this research is to predict the yield of future oil palm kernel production using the Triple Exponential Smoothing and Double Moving Average methods and to determine the level of forecasting errors using the Mean Absolute Percentage Error (MAPE) method. The data for the last ten years, from January 2013 to December 2022, were used in this study. After testing the Triple Exponential Smoothing method with parameters α=0.2,β=0.γ=0.2, the error rate using MAPE was 9.48%, and the Double Moving Average method had an error rate of 11.2%. The MAPE results of the Triple Exponential Smoothing method are considered very good, while the MAPE results of the Double Moving Average method are categorized as good based on the range of MAPE values. This research is expected to provide information to related companies as a supporting reference in anticipating palm oil kernel production. The conclusion of the research is that the Triple Exponential Smoothing method with the test parameters is the best method for forecasting.

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Section: Pendahuluanunclassified

Implementasi Triple Exponential Smoothing dan Double Moving Average Untuk Peramalan Produksi Kernel Kelapa Sawit

Sandika,

Gusti,

Handayani

et al. 2023

josh

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“…where n is the number of data, Y(t) is the actual data at time t-th, and Ŷ(t) represents the predicted value at time t-th. Table 1 shows the standard value prediction criteria for MAPE [27].…”

Section: Prediction Evaluationmentioning

confidence: 99%

Hybrid Fuzzy K-Medoids and Cat and Mouse-Based Optimizer for Markov Weighted Fuzzy Time Series

et al. 2023

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This study seeks to test novel capabilities, specifically those of the hybrid fuzzy k-medoids (FKM) and cat and mouse-based optimizer (CMBO) partitioning approach, in overcoming the Markov weighted fuzzy time series (MWFTS) limitation in creating U talk intervals without fundamental standards. Researchers created a hybrid cat and mouse-based optimizer–fuzzy k-medoids (CMBOFKM) approach to be used with MWTS, since these limits may impair the accuracy of the MWFTS approach. Symmetrically, the hybrid method of CMBOFKM is an amalgamation of the FKM and CMBO methods, with the CMBO method playing a part in optimizing the cluster center of the FKM partition method to obtain the best U membership matrix value as the medoid value that will be used in the MWFTS’s fuzzification stage. Air quality data from Klang, Malaysia are used in the MWFTS–CMBOFKM technique. The evaluation of the model error values, known as mean absolute percentage error (MAPE) and root mean square error, yields the MWFTS–CMBOFKM evaluation findings that are displayed (RMSE). A 6.85% MAPE percentage and a 6071 RMSE score are shown by MWFTS–CMBOFKM using air quality data from Klang, Malaysia. The FKM partition approach can be hybridized with additional optimization techniques in the future to increase the MWFTS method’s precision.

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“…Prediksi kebutuhan air juga dapat dimanfaatkan oleh perusahan untuk mengalokasikan pendistribusian air ke pelanggan guna menghindari kejadian kekurangan atau pemborosan pemakaian air. Hasil prediksi yang akurat juga dapat menekan tingkat kerugian air dan juga biaya yang ditanggung oleh PDAM serta dengan menggunakan air secara efisien (Putro et al, 2018).…”

Section: Pendahuluanunclassified

Prediksi Jumlah Pemakaian Air Bersih Menggunakan Metode Hybrid Singular Spectrum Analysis (Ssa) Dan Sarima Di Pdam Tirtanadi Sibolangit

Salsalina,

Widyasari

2023

SCI TECH ED MATH

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Clean water is one of the basic human needs that is needed on an ongoing basis. Water has an important role in human life. As the population increases, the use of clean water also increases, resulting in the need for demand for the availability of clean water to continue to increase, this is explained by data from the Central Statistics Agency (2022) that the population growth rate in Sibolangit District from 2021-2022 is 0.25% and that This is also explained by data on the amount of clean water consumption by PDAM Tirtanadi Sibolangit which has increased by 6.5%. The purpose of this study is to apply the SSA-SARIMA hybrid model to predict the amount of clean water consumption in the coming period at PDAM Tirtandi Sibolangit so that there is no shortage and wastage of clean water. Hybrid SSA-SARIMA is a combination of two data analysis methods that take advantage of the advantages of each method, namely Singular Spectrum Analysis (SSA) and Seasonal Autoregressive Moving Average (SARIMA). SSA is a technique used to separate signals into periodic and non-periodic components while SARIMA is used to model time series data with trend and seasonal patterns and make predictions for future periods. SARIMA cannot separate periodic and non-periodic signals in data like SSA did. The data used in this study is monthly data on clean water usage from January 2018 to December 2022. The prediction results for the amount of clean water consumption in PDAM Tirtanadi Sibolangit in 2024 use the SSA-SARIMA(1,1,0)(1,0,0) hybrid model. )12 experienced a decrease in the use of clean water with a level of forecasting accuracy having a MAPE value of 6.920446%.

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Prediksi Penjualan Kertas Menggunakan Metode Double Exponential Smoothing

Cited by 16 publications

References 2 publications

Implementasi Triple Exponential Smoothing dan Double Moving Average Untuk Peramalan Produksi Kernel Kelapa Sawit

Implementasi Triple Exponential Smoothing dan Double Moving Average Untuk Peramalan Produksi Kernel Kelapa Sawit

Hybrid Fuzzy K-Medoids and Cat and Mouse-Based Optimizer for Markov Weighted Fuzzy Time Series

Prediksi Jumlah Pemakaian Air Bersih Menggunakan Metode Hybrid Singular Spectrum Analysis (Ssa) Dan Sarima Di Pdam Tirtanadi Sibolangit

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