Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means Algorithm
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Universitas Andalas
2025-07-01
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| Series: | Andalasian International Journal of Applied Science, Engineering, and Technology |
| Online Access: | https://aijaset.lppm.unand.ac.id/index.php/aijaset/article/view/223 |
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| _version_ | 1849397755019001856 |
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| author | Baik Budi Muhammad Ilhamdi Rusydi Reivan Arya Witama Queen Hesti Ramadhamy Refki Budiman |
| author_facet | Baik Budi Muhammad Ilhamdi Rusydi Reivan Arya Witama Queen Hesti Ramadhamy Refki Budiman |
| author_sort | Baik Budi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2d52cdb42cab4c1389a45f7a48554873 |
| institution | Kabale University |
| issn | 2797-0442 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Universitas Andalas |
| record_format | Article |
| series | Andalasian International Journal of Applied Science, Engineering, and Technology |
| spelling | doaj-art-2d52cdb42cab4c1389a45f7a485548732025-08-20T03:38:54ZengLembaga Penelitian dan Pengabdian Masyarakat (LPPM), Universitas AndalasAndalasian International Journal of Applied Science, Engineering, and Technology2797-04422025-07-015212112910.25077/aijaset.v5i02.223223Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means AlgorithmBaik Budi0Muhammad Ilhamdi Rusydi1Reivan Arya Witama2Queen Hesti Ramadhamy3Refki Budiman4Universitas Andalas, IndonesiaUniversitas Andalas, IndonesiaUniversitas Andalas, IndonesiaUniversitas Andalas, IndonesiaUniversitas Andalas, IndonesiaThis 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.https://aijaset.lppm.unand.ac.id/index.php/aijaset/article/view/223 |
| spellingShingle | Baik Budi Muhammad Ilhamdi Rusydi Reivan Arya Witama Queen Hesti Ramadhamy Refki Budiman Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means Algorithm Andalasian International Journal of Applied Science, Engineering, and Technology |
| title | Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means Algorithm |
| title_full | Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means Algorithm |
| title_fullStr | Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means Algorithm |
| title_full_unstemmed | Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means Algorithm |
| title_short | Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means Algorithm |
| title_sort | prediction of electrical grid stability using naive bayes and k means algorithm |
| url | https://aijaset.lppm.unand.ac.id/index.php/aijaset/article/view/223 |
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