Perbandingan Algoritme Klasifikasi Untuk Prediksi Cuaca

Siregar, Amril Mutoi; Faisal, Sutan; Cahyana, Yana; Priyatna, Bayu

doi:10.32627/aims.v3i1.280

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Since time series has the potential of identifying significant chronological expression profiles and the genes associated with this profile, it can enable the comparison of cancer infected genes behavior across multiple conditions over short time duration. Specifically, the response of gastric epithelial cells infected with the vacAmutant strain of the pathogen Helicobacter pylori was investigated [11].…”

Section: Introductionmentioning

confidence: 99%

Retraction: Time Series Based Gene Expression Profiling Algorithm for Stomach Cancer Diagnosis

Abuya

Priyatna

2022

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Retraction to: Teresa Abuya. & B Priyatna. (20202). Time Series Based Gene Expression Profiling Algorithm for Stomach Cancer Diagnosis. Into Another Journal This article has been withdrawn by the publisher for the following reasons: This is based on the author's acknowledgment to the editor that the author has committed a violation of publication ethics. The author canceled the publication after the article was published, arguing that the template from the publisher was not standard even though the publisher had made a standard template that refers to IEEE. As such this article constitutes a grave abuse of the scientific publishing system.

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Section: Introductionmentioning

confidence: 99%

Retraction: Time Series Based Gene Expression Profiling Algorithm for Stomach Cancer Diagnosis

Abuya

Priyatna

2022

bit-cs

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“…Hasil temuan sebelumnya juga menunjukkan bahwa prediksi cuaca menggunakan Gaussian Naive Bayes banyak digunakan (Siregar et al, 2020 ;Yani et al, 2022), dan memiliki kinerja terbaik dengan prediksi akurasi sebesar 84,153% pada penelitian (Oshodi, 2022). Namun, metode Hyperparameter Tuning dan Feature Selection ANOVA-f test dalam model Gaussian Naive Bayes belum banyak digunakan dalam penelitian sebelumnya, denga melakukan pendekatan ini, diharapkan dapat mengatasi kelemahan dan kekurangan dari penelitian sebelumnya dan menghasilkan prediksi cuaca yang lebih akurat dan efisien.…”

Section: Pendahuluanunclassified

Optimasi Gaussian Naïve Bayes dengan Hyperparameter Tuning dan Univariate Feature Selection dalam Prediksi Cuaca

Lindawati,

Fadhli,

Wardana

2023

Edumatic

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The importance of conducting weather prediction research is due to the significant influence of weather changes on daily life. The purpose of this study is to apply an optimal machine-learning classification method for weather prediction. The method used is the Gaussian Naïve Bayes model, which has been optimized using Univariate Feature Selection ANOVA-f test and Hyperparameter Tuning GridsearchCV techniques. The data used consists of 6454 daily weather data in Palembang City. There are 5 tests on the Gaussian Naïve Bayes model before and after optimization. The research results show that the optimization of the model successfully improves the performance in weather prediction. The highest accuracy result after optimization reaches 98.33% with 644 test data, an improvement from the pre-optimization accuracy of only 96.95%. Before optimization, the predictions for weather conditions such as sunny, cloudy/rainy, light rain, and heavy rain match the actual data. However, there were 20 prediction errors when dealing with data that should represent very heavy rain conditions. After optimization, the number of prediction errors for the very heavy rain data was reduced to seven. The optimization approach used in this research helps find the most suitable parameter combinations and eliminates irrelevant features, allowing the model to consider only significant features in weather p

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Comparative Accuracy of Data Mining Models for Predicting Extreme Weather Events in West Sumatra

Kurniawan,

Kurniawan

2024

intij

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Weather factors have a vital role in human activities, especially extreme weather phenomena. Extreme weather can result in potential hydrometeorological disasters that cause loss of life and property. Climate change is also contributing to the higher frequency of extreme weather events. For this reason, research related to predicting extreme weather, especially very heavy rain, is needed to anticipate its impact. Research related to the prediction of extreme weather events is currently still being carried out using various models. By utilizing aerial observation data from Radiosonde (RASON) and daily rainfall data at the Minangkabau Meteorological Station Padang Pariaman, West Sumatra, extreme weather prediction modeling was carried out with the criteria of heavy rain events having rainfall intensity above 50 mm/day or 50 mm/day. 24 hours. From the data mining prediction model that has been carried out using the Support Vector Machine (SVM) Model, in this case, the Support Vector Regression (SVR), the Mean Squared Error (MSE) value is 502.88, and the R2 (Coefficient of Determination) score is 0.09. For the Artificial Neural Network (ANN) model, the Mean Squared Error (MSE) value was 590.03, and the R2 (Coefficient of Determination) score was -0.73 with an accuracy value of only 0.11 and a loss model value of 590. Meanwhile, for the data mining classification model using the Decision Tree Model, the value obtained The model accuracy was 0.47, and the Naïve Bayes (NB) model obtained a model accuracy value of 0.34. From the results of this comparison, it was found that the prediction model using the Decision Tree Model was more accurate in predicting extreme rain events in the West Sumatra region.

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Perbandingan Algoritme Klasifikasi Untuk Prediksi Cuaca

Cited by 3 publications

References 4 publications

Retraction: Time Series Based Gene Expression Profiling Algorithm for Stomach Cancer Diagnosis

Retraction: Time Series Based Gene Expression Profiling Algorithm for Stomach Cancer Diagnosis

Optimasi Gaussian Naïve Bayes dengan Hyperparameter Tuning dan Univariate Feature Selection dalam Prediksi Cuaca

Comparative Accuracy of Data Mining Models for Predicting Extreme Weather Events in West Sumatra

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