<![CDATA[In cement manufacturing, ensuring consistent product quality remains a challenge due to variations in raw materials, operational conditions, and delays in laboratory testing, particularly compressive strength tests, which are only available after 3, 7, and 28 days. This study aims to address this issue by developing a predictive analytics model that estimates compressive strength using machine learning, based on early-available laboratory parameters. The research is conducted at PT Semen Pertama and uses the CRISP-DM framework to structure the analytical process, from business understanding to model deployment. Historical laboratory data—comprising chemical compositions (e.g., SiO₂, Al₂O₃, Fe₂O₃, CaO), physical properties (e.g., fineness, residue), and strength test results—were used to train two supervised learning models: Linear Regression and Random Forest Regressor. Several feature selection methods were applied to improve model accuracy and interpretability. Model performance was assessed using standard regression metrics and validated with cross-validation. The results show that Random Forest consistently achieved higher predictive accuracy than Linear Regression. Feature importance analysis highlighted key variables influencing compressive strength, providing practical insights for quality monitoring. This study supports earlier quality estimation and proactive decision-making in production.]]>
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