Optimasi Software Effort Estimation Menggunakan Random Forest

Abstract
Software development effort estimation is crucial as it is one of the key factors for successful software development. This research employs Random Forest to estimate software development effort. To achieve better results, the study combines the Random Forest method with Genetic Algorithm. The results show that the China dataset provides more accurate estimation compared to the Desharnais dataset, because the China dataset uses relevant feature selection for estimation.
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How to Cite

Maria Rosario Borroek, et al. (2025). Optimasi Software Effort Estimation Menggunakan Random Forest. Prosiding Seminar Nasional Ilmu Teknik, 2(2). https://doi.org/10.61132/prosemnasproit.v2i2.156

Maria Rosario Borroek; Jasmir Jasmir; Fachruddin Fachruddin; Marrylinteri Istoningtyas; Yosefina Venus, "Optimasi Software Effort Estimation Menggunakan Random Forest," Prosiding Seminar Nasional Ilmu Teknik, vol. 2, no. 2, 2025.

Maria Rosario Borroek; Jasmir Jasmir; Fachruddin Fachruddin; Marrylinteri Istoningtyas; Yosefina Venus. "Optimasi Software Effort Estimation Menggunakan Random Forest." Prosiding Seminar Nasional Ilmu Teknik, vol. 2, no. 2, 2025.

Maria Rosario Borroek; Jasmir Jasmir; Fachruddin Fachruddin; Marrylinteri Istoningtyas; Yosefina Venus. "Optimasi Software Effort Estimation Menggunakan Random Forest." Prosiding Seminar Nasional Ilmu Teknik 2, no. 2 (2025).

Maria Rosario Borroek, et al. (2025) 'Optimasi Software Effort Estimation Menggunakan Random Forest', Prosiding Seminar Nasional Ilmu Teknik, 2(2). doi: 10.61132/prosemnasproit.v2i2.156.

Maria Rosario Borroek; Jasmir Jasmir; Fachruddin Fachruddin; Marrylinteri Istoningtyas; Yosefina Venus. Optimasi Software Effort Estimation Menggunakan Random Forest. Prosiding Seminar Nasional Ilmu Teknik. 2025;2(2).

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