Forecasting Penjualan Sembako Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (Studi Kasus : CV Bima Laras Khatulistiwa)

Fitriansyah, Muhammad Daffa (2024) Forecasting Penjualan Sembako Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (Studi Kasus : CV Bima Laras Khatulistiwa). Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

This study compares the performance of three fuzzy membership functions—Gaussmf, Gbellmf, and Trimf—in forecasting sales of basic commodities. The evaluation was conducted by measuring the Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) on both training and testing data across various window sizes. The results indicate that the Trimf membership function provided the best performance. At a window size of 4, Trimf achieved a testing RMSE of 1.77 and a MAPE of 8.23%, outperforming Gaussmf (RMSE 2.82, MAPE 12.57%) and Gbellmf (RMSE 4.64, MAPE 17.54%). Conversely, Gaussmf and Gbellmf exhibited suboptimal performance on the testing data, particularly with larger window sizes. These findings highlight the importance of selecting the appropriate fuzzy membership function to enhance prediction accuracy. Future research could explore combinations of membership functions or other parameters to further improve forecasting performance.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Anggraeny, Fetty Tri	NIDN0711028201	fettyangraeny.if@upnjatim.ac.id
Thesis advisor	Wahanani, Henni Endah	NIDN0022097811	henniendah.if@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Muhammad Daffa Fitriansyah

Date Deposited:

20 Sep 2024 03:29

Last Modified:

20 Sep 2024 03:29

URI:

https://repository.upnjatim.ac.id/id/eprint/29613