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All Journal Andalasian International Journal of Applied Science, Engineering, and Technology
Budiman, Refki
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Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Mechanical Engineering

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Development of an Energy-Efficient Electrical Load Model for Optimizing Electricity Consumption in the Headquarters Building using the PDCA Approach Budiman, Refki; Faizal, Jhonny
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 5 No. 1 (2025): March 2025
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v5i1.207

Abstract

The increasing electricity consumption in the public building sector has a significant impact on greenhouse gas (CO2) emissions, which is proportional to the volume of energy used. This study aims to develop an energy-efficient electrical load model to optimize electricity consumption in the headquarters building of PT Semen Padang using the Plan-Do-Check-Action (PDCA) approach. In the Plan stage, problem identification was conducted through a Pareto diagram and root cause analysis using a fishbone diagram. The dominant issue identified was energy waste in the lighting system. The solution implemented included the installation of motion sensor-based switch control devices to regulate lighting use automatically. In the Do phase, the devices were installed and tested, while in the Check phase, the evaluation showed a reduction in electricity consumption from over 85,000 kWh per month to below 80,000 kWh per month, resulting in an electricity cost savings of IDR 31 million per month. The Action phase established standards for inputs, processes, and outputs to ensure the sustainability of the improvements made. This study not only provides financial benefits but also supports the company's Energy Management System policy. With the PDCA approach, improvement processes can be carried out systematically and continuously, making a tangible contribution to energy efficiency in the public building sector.
The Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means Algorithm Baik Budi; Ilhamdi Rusydi, Muhammad; Arya Witama, Reivan; Hesti Ramadhamy, Queen; Budiman, Refki
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 5 No. 2 (2025): July 2025
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v5i02.223

Abstract

This study explores the use of Naive Bayes and k-means algorithms to predict and analyzed stability of the electrical grid. Data set for this research is public dataset from Kaggle. The main goal of the research is to develop an accurate and efficient predictive model. Naive Bayes was chosen it has ability to handle independent features and also have a compatibility with highdimensional data. The implementation was carried out using Python in Google Colab, with data preprocessing that included feature normalization and an 80:20 train-test split. The Gaussian Naive Bayes model was used for system stability classification. The results demonstrate excellent model performance, with an accuracy of 97.35%, precision of 98.91%, recall of 97.02%, and an F1-score of 97.95%. The confusion matrix reveals the model's ability to classify "stable" and "unstable" conditions with minimal prediction errors.
Co-Authors Arya Witama, Reivan Baik Budi Faizal, Jhonny Ilhamdi Rusydi, Muhammad Ramadhamy, Queen Hesti