Increasing electrical grid stability classification performance using ensemble bagging of C4.5 and classification and regression trees

Aziz, Firman; Lawi, Armin

doi:10.11591/ijece.v12i3.pp2955-2962

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Where is the Research Result Method C4.5 To measure the age classification based on the results of the dominant age decision tree algorithm and the accuracy of the 2017-2021 data. As in previous research shows that the decision tree algorithm provides new knowledge [6] and the experimental results C4.5 also confirms that the most influential instant data attribute [7].…”

Section: Introductionsupporting

confidence: 76%

Naive Bayes Method and C4.5 in Classification of Birth Data

Afandi¹,

Noviana²,

Nurdianah³

2022

Indonesian J. Comput. Cybern. Syst.

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Data on the birth and productive age of a mother to get pregnant in Lampung is still high. to find out the comparison of the productive age of pregnant women and whether they have met the minimum and maximum requirements for a mother to become pregnant, and the criteria for babies born. Where the results of data processing will be used as a source of data for counseling mothers, especially for residents of Banjar Kertahayu village. The data processing requires a special method so that the results become a benchmark for a decision later, such as Data Mining. The method used for data processing used is Naive Bayes and C4.5 Algorithm. The data used is birth data in 2017-2021, the source of data from the Banjar Village Midwife-Central Lampung Regency. Research Results Method C 4.5 Middle age has a dominant age category value of 0.3324138. where the highest value is in 2017, and accuracy is 100 percent from the 2017-2021 data. The baby weight criterion using the Naïve Bayes Class Method has a dominant Middle-aged category value of 0.09675, the highest value in 2017, The results of accuracy for 5 years have accuracy of 92.84% based on 2017-2021 birth data

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Section: Introductionsupporting

confidence: 76%

Naive Bayes Method and C4.5 in Classification of Birth Data

Afandi¹,

Noviana²,

Nurdianah³

2022

Indonesian J. Comput. Cybern. Syst.

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“…Metode Classification and Regression Tree (CART) merupakan sebuah algoritma yang digunakan untuk memproduksi model klasifikasi atau regresi dengan mengategorikan data ke dalam kelas atau nilai yang sesuai dengan pertanyaan-pertanyaan yang diajukan secara bertahap. CART memecah data menjadi beberapa bagian yang semakin kecil dengan cara mengambil kondisi yang paling bermanfaat untuk memisahkan data ke dalam kelas atau nilai yang sesuai [12], [13]. Kriteria standar yang digunakan CART pada scikit adalah kriteria Gini Index.…”

Section: B Decision Treeunclassified

Komparasi Algoritma Decision Tree Dan KNN Dalam Mengklasifikasi Daerah Berdasarkan Produksi Listrik

Harriz,

Setiyowati

2023

J. Inf. dan Komp.

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Pilihan negara yang sedang tumbuh dan berkembang, memiliki jumlah penduduk yang besar dan luas wilayah yang luas yaitu India sebagai contoh untuk diteliti. Listrik komponen vital dan berperan penting. Peneliti melakukan perbandingan akurasi antara dua algoritma pembelajaran mesin yang populer, yaitu Decision Tree dan KNN (K-Nearest Neighbor). Dataset yang berisi sampel sebanyak 345273 digunakan dan validasi dengan metode StratifiedKFold sebanyak 33 bagian dilakukan untuk mengevaluasi hasil klasifikasi dari kedua algoritma tersebut. Hasil dari penelitian ini menunjukkan bahwa algoritma Decision Tree yaitu 85.78% dan memiliki akurasi yang lebih tinggi dibandingkan dengan KNN yang memiliki akurasi sebesar 80.34% dalam mengklasifikasi daerah di India berdasarkan produksi listrik yang dihasilkan. Selain itu, temuan lainnya yaitu Decision Tree memiliki waktu komputasi yang lebih cepat yaitu 51.66 detik dibandingkan dengan KNN yang memiliki waktu komputasi sebesar 56.27 detik. Kesimpulan dari penelitian ini adalah bahwa algoritma Decision Tree memiliki akurasi yang lebih tinggi dibandingkan dengan algoritma KNN. Selain itu, Decision Tree juga memiliki waktu komputasi yang lebih cepat. Dengan demikian, algoritma Decision Tree dapat menjadi pilihan yang lebih baik dalam melakukan klasifikasi.

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“…The authors observed that the battery is acting as a source while the PLN and solar cells are providing the energy, or vice versa, and just one bus at the load is in an unstable or dangerous state. Aziz and Lawi [24] proposed ensemble ML methods for electrical grid stability and achieved good accuracy. The authors experimented with C 4.5 and CART algorithms and reported good results.…”

Section: Introductionmentioning

confidence: 99%

An efficient smart grid stability prediction system based on machine learning and deep learning fusion model

Lakshmanarao,

Srisaila,

Srinivasa Ravi Kiran

et al. 2024

IJEECS

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<span>A smart grid is a modern power system that allows for bidirectional communication, driven mostly by the idea of demand responsiveness. Predicting the stability of the smart grid is necessary for improving its dependability and maximizing the efficacy and regularity of electricity delivery. Predicting smart grid stability is difficult owing to the various elements that impact it, including consumer and producer engagement, which may contribute to smart grid stability. This research work proposes machine learning (ML) and deep learning (DL) approaches for predicting smart grid sustainability. Five ML algorithms, namely support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and logistic regression (LR), were applied for the prediction of smart grid stability. Later, the stacking ensemble and voting ensemble of ML algorithms were also applied for prediction. To further increase accuracy, a novel fusion model with DL artifical neural networks (ANN) and ML SVM was applied and achieved an accuracy of 98.92%. The experiment results show that the proposed model outperformed existing models for smart grid stability prediction.</span>

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Increasing electrical grid stability classification performance using ensemble bagging of C4.5 and classification and regression trees

Cited by 8 publications

References 17 publications

Naive Bayes Method and C4.5 in Classification of Birth Data

Naive Bayes Method and C4.5 in Classification of Birth Data

Komparasi Algoritma Decision Tree Dan KNN Dalam Mengklasifikasi Daerah Berdasarkan Produksi Listrik

An efficient smart grid stability prediction system based on machine learning and deep learning fusion model

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