FUZZY TIME SERIES CHENG OPTIMASI ADAPTIVE PARTICLE SWARM OPTIMIZATION (APSO) UNTUK OPTIMALISASI PREDIKSI HARGA BERAS DI KOTA SURABAYA BERBASIS GUI STREAMLIT

Ulayya, Yasmin (2025) FUZZY TIME SERIES CHENG OPTIMASI ADAPTIVE PARTICLE SWARM OPTIMIZATION (APSO) UNTUK OPTIMALISASI PREDIKSI HARGA BERAS DI KOTA SURABAYA BERBASIS GUI STREAMLIT. Undergraduate thesis, Universitas Pembangunan Nasional Veteran Jawa Timur.

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Abstract

Rice as the primary staple food of Indonesian society is vulnerable to price fluctuations that significantly impact the welfare of low-income groups. This study aims to forecast rice prices in Surabaya City using the Fuzzy Time Series Cheng method optimized with Adaptive Particle Swarm Optimization (APSO) to handle non-linear data and price pattern uncertainty. Secondary data was obtained from Siskaperbapo East Java for the period January 1, 2023 to April 30, 2025, covering two types of rice (premium and medium) in five markets: Tambahrejo, Pucang Anom, Keputran, Soponyono, and Bendul Mrisi. Research results show that FTS Cheng-APSO provides excellent performance with significant MAPE reduction across various markets. Tambahrejo Market achieved MAPE reduction for premium rice from 0.46% to 0.08% and medium rice from 0.68% to 0.16%. Pucang Anom Market experienced dramatic MAPE reduction for premium rice from 4.22% to 0.70% and medium rice from 1.05% to 0.95%. Keputran and Bendul Mrisi Markets also showed accuracy improvements with MAPE decreasing from 3.44% to 3.03% and 1.26% to 0.29% respectively. Soponyono Market demonstrated a unique phenomenon with premium rice MAPE slightly increasing from 0.28% to 0.29% due to high initial data stability, while medium rice decreased from 0.68% to 0.25%. This method combination produces more precise and accurate prediction models, expected to contribute to rice stock management, agricultural production planning, and household economic stability through reliable price forecasting.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Idhom, Mohammad	19830310 202121 1 006	idhom@upnjatim.ac.id
Thesis advisor	Diyasa, I Gede Susrama Mas	19700619 2021211 009	igsusrama.if@upnjatim.ac.id

Subjects:

Q Science > QA Mathematics > QA76.6 Computer Programming

Divisions:

Faculty of Computer Science > Departemen of Data Science

Depositing User:

. Yasmin Ulayya

Date Deposited:

20 Jun 2025 02:11

Last Modified:

20 Jun 2025 02:11

URI:

https://repository.upnjatim.ac.id/id/eprint/38608