<![CDATA[Digitization of construction using technologies such as AI, Big Data, Machine Learning, and Internet of Things (IoT) can help improve the productivity and efficiency of the construction industry. Machine learning methods such as Artificial Neural Network (ANN) are used to solve complex problems, including in construction projects. In addition, Earned Value Management (EVM) is used as a method to analyze and control project performance and estimate project completion time. Although EVM has the disadvantage of predicting estimated project completion times that are linear in nature, the use of non-linear methods such as Artificial Neural Network can help improve the accuracy of estimating project completion times that are complex and have a high degree of variation. This research aims to analyze the physical achievements of the work and predict estimates of work completion using Earned Value Management which is integrated with the Artificial Neural Network on the Advanced Development Project for Facilities and Infrastructure of the Sanggabuana Karawang Training Area Maintenance Detachment (Sarpras Denharrahlat) belonging to the Indonesian Ministry of Defense. By using EVM and ANN, project management can be improved in terms of cost control, scheduling, and better estimation of project completion time. The data analysis method used is EVM. The data analyzed is Week, Planned Value and Earned Value. The three data are then plotted and translated in graphical form to calculate the Schedule Variance and Schedule Performance Index to evaluate the project. After conducting an analysis using the EVM, then a predictive model is formed to calculate the estimated project completion time using the Artificial Neural Network (ANN) method. Next, optimization of the predictive model is carried out to reduce the level of prediction error. The Software used to build predictive models is RapidMiner. At week 26, work progress was 100% ahead of plan, with an Earned Schedule value of 31.00. This indicates that the project has been completed in accordance with the target time. Certain parameter modifications to the optimal model include training cycles of 100, learning rate of 0.316, and momentum of 0.316. With these parameter settings, the model produces the most accurate predictions with a low prediction error rate.]]>
