Comparative analysis of Naïve Bayes, K Nearest Neighbor and C.45 method in weather forecast

Findawati, Yulian; Astutik, Ika Ratna Indra; Fitroni, A S; Indrawati, Iwiq; Yuniasih, Nury

doi:10.1088/1742-6596/1402/6/066046

Cited by 8 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [24] they proposed that the DM solution which applies the Naive Bayes algorithm is the basis for the current weather forecast and the C45 prediction method. when guessing a situation Although the results of comparisons between the Naïve Bayes, K-Nearest Neighbor, and C45 classifications of weather forecasting have shown that KNN classification is the most accurate, Naïve Bayes got 68.77% in the calculations.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

Kareem

Abdulazeez

Hasan

2021

AJRCoS

View full text Add to dashboard Cite

Weather forecasting is the process of predicting the status of the atmosphere for certain regions or locations by utilizing recent technology. Thousands of years ago, humans tried to foretell the weather state in some civilizations by studying the science of stars and astronomy. Realizing the weather conditions has a direct impact on many fields, such as commercial, agricultural, airlines, etc. With the recent development in technology, especially in the DM and machine learning techniques, many researchers proposed weather forecasting prediction systems based on data mining classification techniques. In this paper, we utilized neural networks, Naïve Bayes, random forest, and K-nearest neighbor algorithms to build weather forecasting prediction models. These models classify the unseen data instances to multiple class rain, fog, partly-cloudy day, clear-day and cloudy. These model performance for each algorithm has been trained and tested using synoptic data from the Kaggle website. This dataset contains (1796) instances and (8) attributes in our possession. Comparing with other algorithms, the Random forest algorithm achieved the best performance accuracy of 89%. These results indicate the ability of data mining classification algorithms to present optimal tools to predict weather forecasting.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

Kareem

Abdulazeez

Hasan

2021

AJRCoS

View full text Add to dashboard Cite

show abstract

“…Many other methods in machine learning which is used to process classifications include K-Nearest Neighbor and Naïve Bayes Classifier. Classification is the grouping of an object into classes based on the characteristics similarities and differences (Safri, et al, 2018;Bayhaqy, et al, 2018;Yang, et al, 2018;Findawati, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Analysis K-Nearest Neighbor Method in Classification of Vegetable Quality Based on Color

Putra¹,

Novelan²,

Rizki³

2022

JAETS

View full text Add to dashboard Cite

In this research, the process of applying the K-Nearst Neighbor (KNN) method will be carried out, which is a classification method for a collection of data based on the majority of categories and the goal is to classify new objects based on attributes and sample samples from training data. So that the desired output target is close to the accuracy in conducting learning testing. The results of the test of the K-Nearest Neighbor method. It can be seen that from the K values ??of 1 to 10, the percentage of the results of the analysis of the K-NN method is higher than the results of the analysis of the K-NN method. And from the K value that has been tested, the K 2 value and the K 9 value have the largest percentage so that the accuracy is also more precise. As for the results of testing the K-Nearest Neighbor method in data classification. As for the author's test using a variation of the K value of K-Nearest Neighbor 3,4,5,6,7,8,9. Has a very good percentage of accuracy compared to only K-NN. The test results show the K-Nearest Neighbor method in data classification has a good percentage accuracy when using random data. The percentage of variation in the value of K K-Nearest Neighbor 3,4,5,6,7,8,9 has a percentage of 100%.

show abstract

“…Besides the C45 algorithm can also be combined with the naïve Bayes algorithm to analyze a social, academic problem, [5,13,14]. Many researchers combine and compare the c45 algorithm with other algorithms such as the comparative analysis of Naïve Bayes, K Nearest Neighbor and C.45 methods in weather forecasting that provide decision support [15]. In addition, c45 datamining can also be used to measure the level of customer satisfaction in an institution or organization [16].…”

Section: Introductionmentioning

confidence: 99%

Analysis of sales levels of pharmaceutical products by using data mining algorithm C45

Sovia

Muhammad

Arlis

et al. 2021

IJEECS

View full text Add to dashboard Cite

<p>This research was conducted to analyze the level of sales of pharmaceutical products at a Pharmacy. This is done to find out the types of products that have high and low sales levels. This study uses the C45 Data Mining Algorithm concept that will produce a conclusion on the prediction of sales of pharmaceutical products through data processing obtained from sales transactions at pharmacies. This C45 algorithm will form a decision tree that provides users with knowledge about products that are in great demand by consumers based on sales data and predetermined variables. The final result of the C45 algorithm produces a number of rules that can identify the inheritance of a type of medicinal product. C45 algorithm is able to produce 20 types of categories that will be labeled goals based on the number of pharmaceutical products, since it can be concluded that C45 successfully defines 55% of the existing objective categories.</p>

show abstract

Comparative analysis of Naïve Bayes, K Nearest Neighbor and C.45 method in weather forecast

Cited by 8 publications

References 4 publications

Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

Analysis K-Nearest Neighbor Method in Classification of Vegetable Quality Based on Color

Analysis of sales levels of pharmaceutical products by using data mining algorithm C45

Contact Info

Product

Resources

About