Predicting budget absorption categories using random forest and support vector machine methods

Novardy, Novardy, Kusumawati, Ririen ORCID: https://orcid.org/0000-0001-6090-7219, Hariyadi, M. Amin ORCID: https://orcid.org/0000-0001-9327-7604, Harini, Sri ORCID: https://orcid.org/0000-0001-9664-027X and Imamudin, Mochamad ORCID: https://orcid.org/0009-0006-7522-3710 (2026) Predicting budget absorption categories using random forest and support vector machine methods. Department of Informatics Engineering, Faculty of Science and Technology, UIN Maulana Malik Ibrahim Malang, 18 (1). pp. 1-8. ISSN 1978-161X

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Abstract

Budget classification plays a crucial role in planning, management, and budgeting, from implementation to accountability. We create budgets by considering various types of expenditures and funding sources. Each type of expenditure, such as employee salaries, goods, capital, grants, social assistance, subsidies, interest, and non-tax revenue (PNBP) or public service agencies (BLU), has its own set of rules and methods for tracking money. This study aims to demonstrate how budget classification, based on expenditure types and funding sources, is applied in the implementation of the Revenue Budget. This study aims to assess the classification performance of two models, namely the Random Forest Classifier (RFC) and Support Vector Machine (SVM), based on historical data and evaluate the performance of each model. Tests show that the Random Forest model consistently outperforms the SVM model for each data proportion, with a ratio of 90:10 to 60:40. The Random Forest model achieved its best performance at the 80:20 data split, with an accuracy score of 94 percent, a precision score of 94 percent, a recall score of 94 percent, and an F1 score of 87 percent. The average accuracy score of the SVM test results was 80 percent.

Item Type:	Journal Article
Keywords:	budget absorption; random forest; support vector machine; machine learning; classification
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Divisions:	Graduate Schools > Magister Programme > Graduate School of Islamic Education Management
Depositing User:	Mochamad Imamudin
Date Deposited:	13 Jul 2026 10:35

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