Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases


Dadang Priyanto(1*); Ahmad Robbiul Iman(2); Deny Jollyta(3);

(1) Universitas Bumigora
(2) Universitas Bumigora
(3) Institut Bisnis Dan Teknologi Pelita Indonesia
(*) Corresponding Author

  

Abstract


Indonesia, with its very large population, is a potential market for drugs trafficking. Hence, seriousness is needed in cracking down or preventing drug trafficking. Narcotics are substances or drugs that can cause dependence or addicted and other negative impacts on users. The problem is that drug users do not realize and even ignore diseases caused by drug addiction. The diseases can be life-threatening for users, such as inflammation of the liver, heart disease, hypertension, stroke, and others. The prevalence rate of drug abuse in West Nusa Tenggara (NTB) is included in the high category, reaching 292 cases or around 37.24% cases. This study aimed to create an application that can classify various diseases of drug users using the naïve bayes and KNN methods. The results of this study indicated that there was a very close relationship between drug users and various deadly diseases. The prediction results showed that the naive bayes method provided a prediction accuracy of 94.5% while the KNN showed a prediction accuracy of 92.5%. This shows that the naive bayes method provides better predictive performance than the KNN in the data set of drug addicts in NTB.

Keywords


Drug Addiction; Drug Addiction Disease; Naive Bayes; Narkoba; K-Nearest Neighbor

  
  

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doi  https://doi.org/10.33096/ilkom.v15i2.1544.262-270 		  

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