Article Per Year (5 Year)
p-Index From 2021 - 2026
1.363
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Manajemen Teknologi Journal of Economic, Bussines and Accounting (COSTING) EKONOMIS : Journal of Economics and Business Dinasti International Journal of Education Management and Social Science Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences International Journal of Social Service and Research Eduvest - Journal of Universal Studies
Manahan Siallagan
Unknown Affiliation
Religion Aerospace Engineering Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Development of a Predictive Analytics Model for Cement Compressive Strength: A Case Study at PT Semen Pertama Syahputra, Debi; Siallagan, Manahan
Eduvest - Journal of Universal Studies Vol. 5 No. 12 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i12.51633

Abstract

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.
Co-Authors Arlinda Wibiayu Dhanan Sarwo Utomo Hendrawan Ibrahim, Maulana Malik Malanita Sontya Mira Kania Sabariah Octafian, Luthfi Pinandhita, Ifan Pri Hermawan Rahman, Muhammad Ali Santi Novani Sarah, Shanty Syahputra, Debi Tomasaputra, Diarista Utomo Sarjono Putro