Comparing Holt-Winter and Multi Layer Perceptron in Forecasting The Amount of Rice Supply

Sugiarto, Dedy; Hidayat, Wahyu; Ariatmanto, Dhani; Yaqin, Ainul

doi:10.1109/icoiact53268.2021.9563977

Cited by 3 publications

(4 citation statements)

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“…Sehingga keuntungan atau laba tersebut menjadi tolak ukur agar berguna dalam memberikan prediksi penjualan, prakiraan dengan bahasa lainnya adalah peramalan yang sangat diperlukan untuk dapat menyesuaikan beberapa perbedaan diantara waktu kemarin, waktu disaat ini dan waktu dimasa mendatang (Atika & Rasim, 2019). Metode jaringan saraf tiruan (Sugiarto et al, 2021) yang diantara beberapa metode peramalan yang sering digunakan dalam penelitian. (Gaikindo, 2021) Menurut Nusraningrum et al (2021) terdapat dua pengertian dari persepsi kunsumen dalam memilih produk diantaranya "perilaku yang diperlihatkan konsumen dalam mencari, membeli, menggunakan, mengevaluasi dan menghabiskan produk dan jasa yang mereka harapkan akan kebutuhan mereka" serta "semua kegiatan, tindakan, serta proses psikologis yang mendorong tindakan tersebut pada saat sebelum membeli, menggunakan, menghabiskan produk dan jasa setelah melakukan hal-hal di atas atau kegiatan mengevaluasi.…”

Section: Pendahuluanunclassified

“…Dari penelitian kali ini menggunakan tiga metode diantaranya holt winters, multi layer perceptron 1 hidden layer dan multi layer perceptron menggunakan 2 hidden layer. Diantaranya holt winter merupakan metode konvensional dengan multi layer perceptron metode yang saat ini menjadi pegangan beberapa peneliti untuk meramalkan diantaranya prakiraan pasokan jumlah beras di wilayah Cirebon yang dipengaruhi perubahan iklim sehingga harus menentukan prediksi jumlah beras pasokan dengan tepat (Sugiarto et al, 2021).…”

Section: Pendahuluanunclassified

“…Hasil pola jaringan yang tampak pada metode Multi Layer Perceptron yang digunakan adalah 6 nilai input 5 hidden layer pertama, 3 hidden layer kedua dan 1 output. Hasil ini memperkuat penelitian sebelumnya yang menjelaskan bahwa model dengan dua hidden layer lebih akurat dalam memprediksi (Thomas et al, 2017) dan metode Multi Layer Perceptron lebih akurat daripada metode Holt Winters (Sugiarto et al, 2021). Pada kenyataannya validasi model peramalan menunjukan bahwa ketiga model tersebut valid dimana peramalan untuk 3 bulan kedepan menunjukan nilai produksi yang dekat meskipun beberapa pola berbeda dengan pola aktual.…”

Section: Hasil Dan Pembahasanunclassified

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Comparison of Multi Layer Perceptron and Holt Winter Accuracy in Forecasting Suzuki Car Brand Production in Indonesia

Rachmadan

2023

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Car production based on brand holding agents (APM) reports production value to the Indonesian Association of Automotive Industries (Gaikindo) in 2021 of 863,348 units with a percentage range of 11.4% by the Suzuki brand. Based on the public's interest in the need for a car which is the preferred option, the range and selling price are part of the considerations in determining the product of choice as the vehicle owner. This choice is important in activities to meet daily transportation needs. The purpose of this study is to obtain the most effective method and to maintain the number of vehicle unit production, production forecasts are needed according to people's purchasing power patterns using monthly data, especially the Suzuki brand. The research uses the Holt Winters measurement method with two hidden layers on the Multi Layer Perceptron (MLP) measurement method. The findings from this study are comparisons that can be said to be valid and can be an option in predicting data by utilizing methods to predict the amount of production by brand-holding agents. These results can contribute to optimizing the number of production results so that there are no excess or shortage of unit stock. The results showed that forecasting using a Multi Layer Perceptron with two hidden layers produced an accurate value where the lowest value at the Root Mean Square Error (RMSE) was 889.851 and the Mean Absolute Percentage Error (MAPE) was 9.3368.

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Section: Pendahuluanunclassified

Section: Hasil Dan Pembahasanunclassified

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Comparison of Multi Layer Perceptron and Holt Winter Accuracy in Forecasting Suzuki Car Brand Production in Indonesia

Rachmadan

2023

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“…Climate change can affect rice production because it is related to the change of planting season. Therefore, floods and droughts that occur at certain times can lead to instability in rice prices [4]. Apart from the climate, several factors cause rice price instability in Indonesia, including Rupiah's exchange rates, distribution, international and domestic markets, and so on [3].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters

Astiningrum

Wijayaningrum

Putri

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The large number of Indonesians who consume rice as their primary food makes rice price a benchmark for determining the other staple food prices. The instability of rice prices due to climate change or other uncontrollable factors makes it difficult for Indonesians to estimate the rice prices, especially for the poor. This study proposes the usage of the Improved Crow Search Algorithm (ICSA) to optimize the Support Vector Regression (SVR) parameter in building a regression model to predict the price of staple foods. The forecasting process is carried out based on time series data of 11 staples for four years. The proposed ICSA optimizes the six parameters used in the SVR to form a regression model, consisting of lambda, epsilon, sigma, learning rate, soft margin constant, and the number of iterations. Algorithm performance is measured using MAPE and NRMSE by comparing the actual price of staple foods and forecasting results to get the error rate. With this parameter optimization mechanism, the forecasting results given are good enough with a small error value, in the form of MAPE of 17.081 and NRMSE of 1.594. A MAPE value between 10 and 20 indicates that the forecasting result is acceptable, while an NRMSE value of less than 10 indicates that the forecasting accuracy is excellent. The improvised technique on Crow Search Algorithm is proven to improve the performance of Support Vector Regression in forecasting the price of staple foods.

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Online Trading Application to Support Rice Selling: An Experimental Study of Doi Saket Community

Longani

Somyana

2022

2022 Joint International Conference on Digital Arts, Media and Technology With ECTI Northern Section Conference on Electrical,

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Comparing Holt-Winter and Multi Layer Perceptron in Forecasting The Amount of Rice Supply

Cited by 3 publications

References 10 publications

Comparison of Multi Layer Perceptron and Holt Winter Accuracy in Forecasting Suzuki Car Brand Production in Indonesia

Comparison of Multi Layer Perceptron and Holt Winter Accuracy in Forecasting Suzuki Car Brand Production in Indonesia

Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters

Online Trading Application to Support Rice Selling: An Experimental Study of Doi Saket Community

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