Perbandingan Metode Peramalan Menggunakan Model Time Series

Rini, Monanda Wandita; Ananda, Nessa

doi:10.31001/tekinfo.v10i2.1419

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…[5] Untuk menghadapi permintaan konsumen yang tidak pasti, perusahaan perlu merencanakan produksi dengan memperhitungkan permintaan pasar dan melakukan peramalan untuk memprediksi jumlah permintaan di masa depan. [6].Salah satu teknik yang dapat diterapkan agar kebutuhan pasar dapat terpenuhi dengan optimal adalah dengan merencanakan semua parameter produksi, seperti menentukan produk yang akan diproduksi, jumlah yang diproduksi, kapan produk harus selesai diproduksi dan parameter lain [7]. Peramalan merupakan bagian awal dari suatu proses pengambilan suatu keputusan.Sebelum melakukan peramalan harus diketahui terlebih dahulu apa sebenarnya persoalan dalam pengambilan keputusan itu.…”

Section: Pendahuluanunclassified

Evaluasi Peramalan Permintaan Produk Kopi Bubuk Menggunakan Pendekatan Time Series Di Ukm Eyang Kakung - Gresik

Amiludin,

Mahbubah

2023

SGTk

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Accuracy is a key point to minimize extra goods. This research aims in evaluating goods demand in a small to medium-sized enterprise. Time series was used as a research approach. The data used in this research is the demand data for powdered coffee within and outside the city for one year. This resurch begun with data collection, analysis, and finally forecasting stage using the time series method. The analysis results show that the demand data for Eyang Kakung powdered coffee SMEs has a horizontal data pattern. The time series method used is moving averages and exponential smoothing according to the data pattern classification. After forecasting, the results show MAD=56.59, MSE=4676.52, and MAPE=10.74% for demand within the city and MAD=24.34, MSE=969.46, and MAPE=4.65% for demand outside the city. Based on the forecasting results, it can be concluded that the time series method can be used as a tool to forecast demand for powdered coffee within and outside the city. By using exponential smoothing with α=0.5, it can help SMEs make decisions in preparing raw material stocks and improving production efficiency.

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Section: Pendahuluanunclassified

Evaluasi Peramalan Permintaan Produk Kopi Bubuk Menggunakan Pendekatan Time Series Di Ukm Eyang Kakung - Gresik

Amiludin,

Mahbubah

2023

SGTk

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“…Peramalan (Forecasting) adalah seni dan ilmu untuk memperkirakan kejadian di masa depan sebagai dasar mempersiapkan sumber daya sejak masa sekarang [9]. Hal tersebut dapat dilakukan dengan menggunakan data historis dan proses kalkulasi untuk memprediksikan sebuah proyeksi atau kejadian di masa datang.…”

Section: Data Jumlah Penumpangunclassified

Peramalan Jumlah Penumpang LRT Sumsel dengan Metode Exponential Smoothing

Lailiyah,

Agustiani

2023

diophantine

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Forecasting is critical in the pandemic sector as part of the effort to adapt the post-pandemic system, particularly in transportation. This research carried out on the South Sumatra Integrated Railroad (LRT) in the post-pandemic Covid-19 period. Forecasting is done in this study using the exponential smoothing method using alpha that is α=0.1, α=0.5, and α= 0.9. Comparison with the smallest error using the exponential smoothing method dropped the choice at alpha 0.1 with the smallest error calculation value. Forecasting using the exponential smoothing method with 0.1 alpha sample data on the number of LRT Sumsel passengers during the Covid-19 period in 2020 produces a forecast of 66,538 passengers with an error rate of Mean Absolute Deviation (MAD)=9,486, Mean Square Error (MSE)=1,150, and Mean Absolute Percentage Error (MAPE)=24.58%. Meanwhile, from the sampel data on the number of South Sumatra LRT passengers on post-pandemic Covid-19 period in 2022, it produced a forecast of 187,566 passengers with Mean Absolute Deviation (MAD) = 25,816, Mean Square Error (MSE) = 9,477, and Mean Absolute Percentage Error (MAPE) = 16.60%.

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“…Mean Square Error(MSE) dihitung dengan menjumlahkan kuadrat semua kesalahan peramalan pada setiap periode dan membaginya dengan jumlah periode peramalan [14]. Ukuran pertama kesalahan peramalan keseluruhan untuk sebuah model adalah Mean Absolute Deviation (MAD).…”

Section: Metode Akurasi Peramalanunclassified

Analisis Peramalan Obat Menggunakan Metode Simple Moving Average, Weighted Moving Average, Dan Exponential Smoothing

et al. 2022

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Forecasting is one of the essential measures in decision making, especially for estimating the number of needs. Some large companies often hire consultants to analyze and advise on forecast product demand. Especially in the health sector, forecasting is needed to meet customer drug demand. In the last two years, the Healthy Prayer Clinic experienced problems in planning the procurement of non-generic drugs due to the COVID-19 pandemic, where the number of drug requests was greater than the number of available drug stocks. This happened because the Healthy Prayer Clinic ordered non-generic drugs without planning. This study aims to determine the demand for non-generic medications in the next period and provide recommendations for the best forecasting method to meet customer needs. The methods used in this research are Simple Moving Average (SMA), Weighted Moving Average (WMA), and Exponential Smoothing (ES). Then test the accuracy of forecasting using Mean Square Error (MSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE). The results of the overall forecasting calculation using the Simple Moving Average method of 19.774, then the Weighted Moving Average method of 19.573, and the Exponential Smoothing method of 20.851. The results of the calculation of the best demand forecasting using the Exponential Smoothing (ES) method for 3 months carried out from April 2021 to April 2022 obtained results of 20.851 with forecasting error accuracy using Mean Square Error (MSE), Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) is 1.341.137, respectively; 883; and 0.90%. The best method with the smallest accuracy rate for forecasting errors is the Exponential Smoothing method with forecasting values for the next month, April 2022, amounting to 20.851

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Perbandingan Metode Peramalan Menggunakan Model Time Series

Cited by 4 publications

References 6 publications

Evaluasi Peramalan Permintaan Produk Kopi Bubuk Menggunakan Pendekatan Time Series Di Ukm Eyang Kakung - Gresik

Evaluasi Peramalan Permintaan Produk Kopi Bubuk Menggunakan Pendekatan Time Series Di Ukm Eyang Kakung - Gresik

Peramalan Jumlah Penumpang LRT Sumsel dengan Metode Exponential Smoothing

Analisis Peramalan Obat Menggunakan Metode Simple Moving Average, Weighted Moving Average, Dan Exponential Smoothing

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