PREDIKSI PRODUKSI SONGKOK MENGGUNAKAN METODE ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE) DAN EXPONENTIAL SMOOTHING PADA TEBU MAS GRESIK

Nafisah, Sayyidah (2025) PREDIKSI PRODUKSI SONGKOK MENGGUNAKAN METODE ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE) DAN EXPONENTIAL SMOOTHING PADA TEBU MAS GRESIK. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

Production forecasting is a critical component of supply chain management, particularly in industries with seasonal demand fluctuations, such as songkok manufacturing. This study aims to analyze and forecast songkok production at Tebu Mas Gresik using the Autoregressive Integrated Moving Average (ARIMA) and Simple Exponential Smoothing (SES) methods. Historical sales data from 2020 to 2024 were utilized to develop forecasting models, considering trend patterns, seasonality, and demand fluctuations. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the ARIMA (1,1,1) model outperforms SES in terms of accuracy, with an MAE of 17.43, MSE of 656.38, and MAPE of 24.11%. The integration of these predictions into raw material planning enhances inventory management efficiency, with an average increase in material consumption of 2.1% to 2.2% per production period, aligning with production fluctuation trends. The application of accurate forecasting models not only improves production planning precision but also mitigates inventory imbalances and optimizes operational efficiency in responding to demand variability.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Najaf, Abdul Rezha Efrat	NIDN0029099403	rezha.efrat.sifo@upnjatim.ac.id
Thesis advisor	Ananto, Prasasti Karunia Farista	NIDN2004079701	prasasti.karunia.fasilkom@upnjatim.ac.id

Subjects:

Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General) > T58.6-58.62 Management Information Systems
T Technology > TS Manufactures > TS 155-194 Production Management, Operations Management

Divisions:

Faculty of Computer Science > Departemen of Information Systems

Depositing User:

Sayyidah Nafisah

Date Deposited:

13 Jun 2025 07:40

Last Modified:

13 Jun 2025 07:40

URI:

https://repository.upnjatim.ac.id/id/eprint/37073