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Penerapan Metode Earned Value Management Berbasis Microsoft Project Dengan Integrasi Artificial Neural Network Untuk Pengendalian Waktu Dan Biaya Proyek (Studi Kasus: Pembangunan Gedung Direktorat Reserse Kriminal Khusus Polda Jawa Timur)

Author


Hamdal Afridho(1Mail), Mas Suryanto HS(2),
(1) Program Studi S1 Teknik Sipil, Fakultas Teknik, Universitas Negeri Surabaya,
(2) Program Studi S1 Teknik Sipil, Fakultas Teknik, Universitas Negeri Surabaya,

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Available online: 2026-10-24  |  Published : 2026-10-24
Copyright (c) 2026 Hamdal Afridho, Mas Suryanto H.S
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Abstract


This study aims to predict the Estimate at Completion (EAC) and Estimate Completion Date (ECD) using conventional EVM and an integrated EVM–Artificial Neural Network (ANN) model, and to compare their levels of accuracy. This quantitative comparative-experimental study was conducted on the Construction Project of the DITRESKRIMSUS Building of the East Java Regional Police. Secondary data were obtained from the Bill of Quantities (RAB), S-Curve, weekly reports, and financial reports. EVM analysis was performed using Microsoft Project, while the ANN model was processed using RapidMiner through five review scenarios with hyperparameter optimization. The accuracy level was validated using Root Mean Square Error (RMSE) against the actual conditions. The results show that the EVM-ANN prediction of ECD produced an RMSE of 1.673 weeks, with the most optimal prediction of 40 weeks obtained in scenarios D and E, more accurate than conventional EVM 3.493 weeks, whose most optimal prediction was 42 weeks in scenario A. Conversely, the conventional EVM prediction of EAC produced an RMSE of IDR 10,842,909.21, with the most optimal prediction of IDR 41,071,398,678.62 in scenario E, more accurate than EVM-ANN's RMSE of IDR 48,727,488.73, whose most optimal prediction was IDR 41,059,371,369.22 in scenario D.

Keywords


Earned Value Management, Artificial Neural Network, Microsoft Project, Project Control, RMSE.

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