Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases
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...
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| Format: | Article |
| Language: | English |
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Fakultas Ilmu Komputer UMI
2023-08-01
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| Series: | Ilkom Jurnal Ilmiah |
| Subjects: | |
| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1544 |
| _version_ | 1797261735229390848 |
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| author | Dadang Priyanto Ahmad Robbiul Iman Deny Jollyta |
| author_facet | Dadang Priyanto Ahmad Robbiul Iman Deny Jollyta |
| author_sort | Dadang Priyanto |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T23:45:56Z |
| format | Article |
| id | doaj.art-c655d0b3d08d470ebb06f8b8b88ee5a5 |
| institution | Directory Open Access Journal |
| issn | 2087-1716 2548-7779 |
| language | English |
| last_indexed | 2024-04-24T23:45:56Z |
| publishDate | 2023-08-01 |
| publisher | Fakultas Ilmu Komputer UMI |
| record_format | Article |
| series | Ilkom Jurnal Ilmiah |
| spelling | doaj.art-c655d0b3d08d470ebb06f8b8b88ee5a52024-03-15T07:10:06ZengFakultas Ilmu Komputer UMIIlkom Jurnal Ilmiah2087-17162548-77792023-08-0115226227010.33096/ilkom.v15i2.1544.262-270533Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive DiseasesDadang Priyanto0Ahmad Robbiul Iman1Deny Jollyta2Universitas BumigoraUniversitas BumigoraInstitut Bisnis Dan Teknologi Pelita IndonesiaIndonesia, 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.https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1544drug addictiondrug addiction diseasenaive bayesnarkobak-nearest neighbor |
| spellingShingle | Dadang Priyanto Ahmad Robbiul Iman Deny Jollyta Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases Ilkom Jurnal Ilmiah drug addiction drug addiction disease naive bayes narkoba k-nearest neighbor |
| title | Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases |
| title_full | Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases |
| title_fullStr | Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases |
| title_full_unstemmed | Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases |
| title_short | Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases |
| title_sort | naive bayes and k nearest neighbor algorithm approach in data mining classification of drugs addictive diseases |
| topic | drug addiction drug addiction disease naive bayes narkoba k-nearest neighbor |
| url | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1544 |
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