Optimasi Model SARIMA dengan Penanganan Additive Outlier Menggunakan Algoritma Firefly untuk Peramalan Jumlah Penumpang Kereta Api di Stasiun Kediri

Ardani, Nabila Lintang (2026) Optimasi Model SARIMA dengan Penanganan Additive Outlier Menggunakan Algoritma Firefly untuk Peramalan Jumlah Penumpang Kereta Api di Stasiun Kediri. Undergraduate thesis, Universitas Pembangunan Nasional Veteran Jawa Timur.

	Text 22083010105.-cover.pdf Download (1MB)
	Text (Bab 1) 22083010105.-bab 1.pdf Download (218kB)
	Text (Bab 2) 22083010105.-bab 2.pdf Restricted to Repository staff only until 19 July 2028. Download (618kB)
	Text (Bab 3) 22083010105.-bab 3.pdf Restricted to Repository staff only until 19 July 2028. Download (616kB)
	Text (Bab 4) 22083010105.-bab 4.pdf Restricted to Repository staff only until 19 July 2028. Download (3MB)
	Text (Bab 5) 22083010105.-bab 5.pdf Download (225kB)
	Text (Daftar Pustaka) 22083010105.-daftarpustaka.pdf Download (175kB)
	Text (Lampiran) 22083010105.-lampiran.pdf Restricted to Repository staff only Download (441kB)

Abstract

Kediri Station is the second-busiest mobility hub in Daop 7 Madiun, yet it experiences unstable passenger volume fluctuations, including extreme surges during holidays that cause congestion and resource unpreparedness. This research develops a forecasting model for highly volatile data using a hybrid SARIMA Additive Outlier (SARIMA-AO) approach optimized with the Firefly Algorithm, based on daily passenger data from January 1, 2024 to September 30, 2025. The initial SARIMA model was obtained through Box-Cox transformation, ADF and KPSS stationarity tests, model identification from ACF and PACF plots, parameter estimation, and residual diagnostic testing, yielding an AIC of 8292, MAE of 110.07, RMSE of 129.05, and MAPE of 10.34%. Additive outlier treatment through exogenous dummy variables produced a SARIMA-AO model with better performance (AIC 8180, MAE 105.22, RMSE 123.45, MAPE 9.87%). Parameter optimization using the Firefly Algorithm subsequently produced the SARIMA-AO- Firefly model with an AIC of 8074, MAE of 104.70, RMSE of 120.07, and MAPE of 9.93%. The results show that combining additive outlier treatment with Firefly optimization improves the quality and accuracy of the model in forecasting train passenger numbers at Kediri Station. The forecasting results were implemented into a web-based Graphical User Interface (GUI) using Streamlit as a practical tool for Kediri Station in ticket management, crowd control, and workforce allocation.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Hindrayani, Kartika Maulidya	NIDN0009099205	kartika.maulida.ds@upnjatim.ac.id
Thesis advisor	Wara, Shindi Shella May	NUPTK1850774675230252	shindi.shella.fasilkom@upnjatim.ac.id

Subjects:

H Social Sciences > HA Statistics
Q Science > QA Mathematics
Q Science > QA Mathematics > QA76.6 Computer Programming

Divisions:

Faculty of Computer Science > Departemen of Data Science

Depositing User:

Nabila Lintang Ardani

Date Deposited:

20 Jul 2026 06:49

Last Modified:

20 Jul 2026 06:49

URI:

https://repository.upnjatim.ac.id/id/eprint/56352