Prediction of Credit Card Using the Naïve Bayes Method and C4.5 Algorithm

Ketjie,; Mawardi, Viny Christanti; Perdana, Novario Jaya

doi:10.1088/1757-899x/1007/1/012161

Cited by 2 publications

(2 citation statements)

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“…It operates on the principle of probability theory and statistical methods. Bayes theorem establish independent values based on what precedes them[25]. The NB algorithm is considered "naive" because it assumption of independence among all variables concerning class values, which may not hold in realworld scenarios.…”

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confidence: 99%

Machine Learning Algorithms Fusion Based on DGA Data for Improving Fault Diagnosis of Electrical Power Transformer

Hechifa,

Lakehal,

Nanfak

et al. 2023

The Scientific Bulletin of Electrical Engineering Faculty

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Dissolved Gas Analysis (DGA) continues to be widely recognized as a valuable method in recent times for the early identification of issues in oil-filled power transformers. It has gained extensive adoption as a primary approach for the early discovery of these issues, relying on the analysis of dissolved gases. This contributes to enhancing the dependability of electrical systems. This paper proposes an efficient fusion method based on DGA data using the two best Machine Learning algorithms, the neural network (MLP), the naïve Bayes (NB) throughdata input vector ppm, a percentage input vector, and an Logarithmic input vector. The fusion method predictively combined the two classifiers and obtained a statistical evaluation: accuracy, recall, precision, and F-measure higher than both classifiers separately. The proposed fusion method was evaluated for performance using a test database and compared with conventional and smart methods. Results showed that the proposed model outperformed both traditional and intelligent methods in terms of diagnostic accuracy when using percentage and logarithmic input vectors. The Prediction Based Fusion (PBF) vector Percentages achieved an accuracy rate of 97.22%, while PBF vector Logarithmic achieved an accuracy rate of 95.83%. These rates were higher than those achieved by traditional methods, such as the Modified RRM/CEGB method 91.67% and Modified RRM/IEC method 90.28%. Additionally, the proposed model surpassed the accuracy rates of intelligent methods, such as CSUS ANN 88.89% and Conditional Probability 93.06%.

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confidence: 99%

Machine Learning Algorithms Fusion Based on DGA Data for Improving Fault Diagnosis of Electrical Power Transformer

Hechifa,

Lakehal,

Nanfak

et al. 2023

The Scientific Bulletin of Electrical Engineering Faculty

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“…Berdasarkan penelitian yang dilakukan oleh Perdana dan Purnomo (2020) mengenai sistem pakar mendiagnosa penyakit hipertensi menggunakan teorema bayes, Perdana dan Purnomo menggunakan metode teorema bayes untuk mendapatkan hasil diagnosa penyakit hipertensi. [3] Berdasarkan penelitian yang dilakukan oleh Hafizah (2021) mengenai sistem pakar untuk pendiagnosaan karies gigi menggunakan teorema bayes, Hafizah menggunakan metode teorema bayes untuk mendapatkan hasil diagnosa mengenai karies gigi. [4] Dari beberapa penelitian terdahulu yang menggunakan metode teorema bayes dalam mengatasi berbagai masalah diagnosa penyakit maka peneliti menggunakan metode teorema bayes untuk mendiagnosa penyakit Irritable Bowel Syndrome.…”

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Sistem Pakar Diagnosa Penyakit Irritable Bowel Syndrome (IBS) Menggunakan Metode Teorema Bayes

Atmaja

2022

RJOCS

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Irritable Bowel Syndrome merupakan penyakit dengan kumpulan gejala akibat iritasi pada saluran pencernaan dan memiliki gejala pada umumnya yaitu kram perut, diare, kembung dan sembelit. Penyakit ini rentan terhadap seseorang yang berusia dibawah 50 tahun. Seseorang yang mengalami beberapa gejala juga tidak jarang untuk mencari informasi mengenai penyakit yang dia derita kepada orang-orang sekitar ataupun media internet. Ketika seseorang tersebut curiga terhadap dirinya yang mengidap penyakit meningitis, maka seseorang tersebut perlu untuk memastikan penyakit yang diderita. Sehingga seseorang yang ingin memastikan penyakit yang diderita cenderung pergi ke dokter. Masalah yang terjadi yaitu biaya yang mahal dan tempat yang jauh dari rumah. Penelitian ini menggunakan sistem pakar dan metode teorema bayes untuk memastikan penyakit yang diderita seseorang adalah penyakit Irritable Bowel Syndrome tanpa harus menemui dokter.

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Prediction of Credit Card Using the Naïve Bayes Method and C4.5 Algorithm

Cited by 2 publications

References 0 publications

Machine Learning Algorithms Fusion Based on DGA Data for Improving Fault Diagnosis of Electrical Power Transformer

Machine Learning Algorithms Fusion Based on DGA Data for Improving Fault Diagnosis of Electrical Power Transformer

Sistem Pakar Diagnosa Penyakit Irritable Bowel Syndrome (IBS) Menggunakan Metode Teorema Bayes

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