Analisis Perbandingan Algoritma SVM Dan KNN Untuk Klasifikasi Anime Bergenre Drama

Pramansah, None Vika Vitaloka

doi:10.33059/j-icom.v3i1.4950

Cited by 1 publication

(1 citation statement)

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“…The final classification of the query will be based on the class that appears the most [25]. In the KNN algorithm, a formula is needed to calculate the shortest distance, the distance value in the KNN method can be calculated using the Euclidean distance formula [26]. The following is the Euclidean distance formula in Equation 2, Figure 7 shows the testing process on the KNN algorithm.…”

Section: K-nearest Neighbormentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Classification of Ultraviolet Index

Syahab

Widiyanto

Anuggilarso

et al. 2023

J. teknol. inf. pendidik.

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Indonesia is a tropical climate country which has the potential for high intensity of sunlight exposure. Several types of exposure that receives are ultraviolet rays. According to the National Agency for Meteorology, Climatology and Geophysics, it provides information regarding the impact on human activities in an ultraviolet index which has a risk scale. The aim of this research was creating a recommender system based on the ultraviolet index category in providing daily activity advice to users. The methods used the K-nearest Neighbor algorithm and Support Vector Machine with a Collaborative Filtering Model-Based approach that could recommend items based on the results of a model trained to identify input data patterns. The stages carried out in this study included data collection, data pre-processing, data division into test data and train data, dataset testing, analysis of the results of models that had been trained in the accuracy values using the algorithm tested. The results of the confusion matrix calculation produced test evaluations in accuracy values, precision values, and recall values. The comparison of result had the highest performance in K-nearest Neighbor with an accuracy value of 99.69%, a precision value of 99.00%, and a recall value of 96.20%. In research used the Support Vector Machine showed the lowest performance with an accuracy value of 97.91%, a precision value of 93.20%, and a recall value of 86.40%.

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Section: K-nearest Neighbormentioning

confidence: 99%