Analisis Prediksi Emisi Karbon Dioksida Kendaraan Bermotor menggunakan Algoritma Hibrida LSTM dan ARIMA

Fardana, Muhammad Hakam (2025) Analisis Prediksi Emisi Karbon Dioksida Kendaraan Bermotor menggunakan Algoritma Hibrida LSTM dan ARIMA. Undergraduate thesis, UPN Veteran Jawa Timur.

	Text (Cover) 20081010198_cover.pdf Download (1MB)
	Text (Bab 1) 20081010198_bab1.pdf Download (88kB)
	Text (Bab 2) 20081010198_bab2.pdf Restricted to Repository staff only until 12 June 2027. Download (285kB)
	Text (Bab 3) 20081010198_bab3.pdf Restricted to Repository staff only until 12 June 2027. Download (329kB)
	Text (Bab 4) 20081010198_bab4.pdf Restricted to Repository staff only until 12 June 2027. Download (5MB)
	Text (Bab 5) 20081010198_bab5.pdf Download (16kB)
	Text (Daftar Pustaka) 20081010198_daftarpustaka.pdf Download (153kB)

Abstract

CO2 emissions from motor vehicles contribute substantially to climate change. Accurate prediction of emission trends is thus crucial for mitigation strategies. This research evaluates the performance of a Hybrid Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) model for predicting Motor Vehicle CO2 Emissions. This hybrid model integrates ARIMA's capability in handling linear patterns and LSTM's in capturing long-term non-linear dependencies. Using 1000 historical data entries from the Eco-Route Application, the hybrid model was tested and compared with single models. Results show the hybrid model achieved good prediction accuracy with MAE 0.0941, MAPE 10.20%, and RMSE 0.1081 in its best scenario. However, on this specific dataset, the single ARIMA model demonstrated the best overall performance (MAE 0.0835, MAPE 9.33%, RMSE 0.0975). Dataset limitations were identified as affecting the hybrid's capability. The Hybrid LSTM-ARIMA model is determined to be a promising option for CO2 emission prediction, especially when larger datasets are available.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Saputra, Wahyu Syaifullah Jauharis	NIDN0725088601	wahyu.s.j.saputra.if@upnjatim.ac.id
Thesis advisor	Swari, Made Hanindia Prami	NIDN0805028901	madehanindia.fik@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Muhammad Hakam Fardana

Date Deposited:

12 Jun 2025 09:14

Last Modified:

12 Jun 2025 09:14

URI:

https://repository.upnjatim.ac.id/id/eprint/37424