Peramalan Tingkat Inflasi di Indonesia Menggunakan Artificial Bee Colony dan XGBoost

Mohammad, Farrel Adel (2024) Peramalan Tingkat Inflasi di Indonesia Menggunakan Artificial Bee Colony dan XGBoost. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

Economic growth and price stability are the main focus for countries, including Indonesia. Inflation, as an indicator of fluctuations in the prices of goods and services, plays an important role in economic stability. Inflation forecasting is key for governments and economic stakeholders to design responsive policies. Machine learning models, such as XGBoost, have been used for this purpose, but optimal hyperparameter tuning is key to its success. Optimization algorithms such as Artificial Bee Colony (ABC) can automate the hyperparameter tuning process of XGBoost, improving the efficiency and performance of the model. Previous research shows the success of ABC-XGBoost in different applications, such as single sand body identification. Therefore, this study aims to explore the capability of ABC-XGBoost in forecasting the inflation rate in Indonesia. The goal is to develop an accurate model with minimal error. Using historical inflation data from the Central Bureau of Statistics, this study proves that the combination of Artificial Bee Colony and XGBoost can successfully forecast the monthly inflation rate in Indonesia with accurate results. The implementation of this method gives an average RMSE score of 0.155066, MAE score of 0.115655, and MAPE score of 0.795767.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Rizki, Agung Mustika	NIDN0025079302	agung.mustika.if@upnjatim.ac.id
Thesis advisor	Sihananto, Andreas Nugroho	NIDN0012049005	andreas.nugroho.jarkom@upnjatim.ac.id

Subjects:

Q Science > Q Science (General)
Q Science > QA Mathematics
Q Science > QA Mathematics > QA76.6 Computer Programming
T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Farrel Mohammad

Date Deposited:

19 Jul 2024 07:28

Last Modified:

19 Jul 2024 07:28

URI:

https://repository.upnjatim.ac.id/id/eprint/26665