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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Sains Matematika dan Statistika Jurnal Analisa Indonesian Journal of Global Health research Jurnal Manajemen Pendidikan Dasar, Menengah dan Tinggi [JMP-DMT] LINEAR: Journal of Mathematics Education Edupedia: Jurnal Studi Pendidikan dan Pedagogi Islam Jurnal Pembelajaran dan Pengembangan Matematika Jurnal Riset Mahasiswa Matematika
Sri Harini
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Religion Computer Science & IT Education Health Professions Languange, Linguistic, Communication & Media Mathematics Nursing Public Health Other

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Predicting Budget Absorption Categories Using Random Forest and Support Vector Machine Methods Novardy Novardy; Ririen Kusumawati; Muhammad Amin Hariyadi; Sri Harini; Muhammad Imamudin
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.37223

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.
Co-Authors Abdullah Shofi Abdussakir Abdussakir Ach. Nashichuddin Andini Endah Sri Mulyani Anisah Salsabila Asfira Zakiatun Nisa’ Atiqotur Royyani Dewi Rosikhoh Edy Prayoga Elly Susanti Endang Widhiyastuti Erni Anggraini Iktafi Muzayana Ina Maya Sabara Ira Wirdatus Solichah Marhayati Muhammad Amin Hariyadi Muhammad Imamudin Muhammad Imamul Khoiri Muhammad Irfan Afandi Nailul Munah Novardy Novardy Noviana Dewi Nuri Fatihatul Fariha Rifqoh Thoyyibah Ririen Kusumawati Sri Pujilestari Suci Hidayati Tri Harningsih Turmudi, Turmudi