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All Journal EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis Eduvest - Journal of Universal Studies
Manahan Siallagan
Institut Teknologi Bandung
Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Health Professions Neuroscience Social Sciences Other

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Advance Meter Infrastructure (AMI) Users Behaviour And Electricity Performance Predictive Model: An Implementation Of AMI Program Review In Indonesia Fitri Fitri; Manahan Siallagan
EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 4 (2025): Oktober
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/ekombis.v13i4.8328

Abstract

The advancements of electricity metering in Indonesia have remained the focus by PT Perusahaan Listrik Negara (PLN) in distributing electricity. The Advanced Metering Infrastructure (AMI) initiative started in 2020 aimed to enhance service quality, reduce non-technical losses, and support the integration of renewable energy sources. The AMI implementation still considered to have many challenges considering its high implementation cost, uneven distribution of communication networks, and customer acceptance to the technology in Indonesia. The research aimed to develop the predictive model to identify critical parameters for AMI implementation success based on customer behaviour analysis. The success presented by the customer satisfaction level were found to have low correlation with the electricity and technical performance variables. Identified critical parameters were AMI implementation period, comfort features, AMI ease of use, and troubleshooting responses, which directly related to the customers. An internal performance monitoring is recommended to enhance services provided by PLN with predictive and active troubleshooting responses. The stage of socialization and education in both before and after AMI implementation are found to be critical to get the customer experience and satisfaction review to achieve its targeted impact.
Development of a Predictive Analytics Model for Cement Compressive Strength: A Case Study at PT Semen Pertama Debi Syahputra; Manahan Siallagan
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 Debi Syahputra Fitri Fitri