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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References


B. Priambodo, Y. Jumaryadi, and U. Salamah, “Penggunaan Aplikasi Deteksi Pecandu Narkoba Di Meruya Utara,” J. Pasopati Pengabdi. Masy. dan Inov. Pengemb. Teknol., vol. 4, no. 3, 2022, doi: 10.14710/pasopati.2022.14468.

M. Mustaqim, H. Dafitri, and D. Dharmawati, “Edukasi Digital Pengenalan Bahaya Narkoba Bagi Anak Usia Dini Berbasis 3D Dan Augmented Reality,” Djtechno J. Teknol. Inf., vol. 2, no. 2, pp. 170–176, 2021, doi: 10.46576/djtechno.v2i2.1623.

B. C. Laksono and N. W. K. Projo, “Pemodelan Analisis Rantai Markov untuk Mengestimasi Potensi Kasus Narkoba di Indonesia,” Semin. Nas. Off. Stat., vol. 2021, no. 1, pp. 715–722, 2021, doi: 10.34123/semnasoffstat.v2021i1.1016.

A. Sinjar and T. Sahuri, “Bahaya Narkoba Terhadap Masa Depan Generasi Muda,” vol. 2, no. 2, pp. 154–160, 2021.

D. Priyanto, M. Zarlis, H. Mawengkang, and S. Efendi, “Analysis of earthquake hazards prediction with multivariate adaptive regression splines,” Int. J. Electr. Comput. Eng., vol. 12, no. 3, pp. 2885–2893, 2022, doi: 10.11591/ijece.v12i3.pp2885-2893.

H. Hairani, G. S. Nugraha, M. N. Abdillah, and M. Innuddin, “Komparasi Akurasi Metode Correlated Naive Bayes Classifier dan Naive Bayes Classifier untuk Diagnosis Penyakit Diabetes,” InfoTekJar (Jurnal Nas. Inform. dan Teknol. Jaringan), vol. 3, no. 1, pp. 6–11, 2018, doi: 10.30743/infotekjar.v3i1.558.

Y. F. Safri, R. Arifudin, and M. A. Muslim, “K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor,” Sci. J. Informatics, vol. 5, no. 1, p. 18, 2018, doi: 10.15294/sji.v5i1.12057.

R. M. Abarca, “Sistem Pakar Diagnosis Penyakit Mata,” Nuevos Sist. Comun. e Inf., no. sistem pakar penyakit mata, pp. 2013–2015, 2021, [Online]. Available: https://repository.dinamika.ac.id/id/eprint/982/6/BAB_II.pdf.

A. H. Bisri Merluarini, Diah Safitri, “Perbandingan Analisis Klasifikasi menggunakan Metode K-Nearest Neighbor (K-Nn) dan Multivariate Adaptive Regression Spline (Mars) pada Data Akreditasi Sekolah Dasar Negeri di Kota Semarang,” vol. 3, pp. 313–322, 2014.

P. I. Lestari, D. E. Ratnawati, and L. Muflikhah, “Implementasi Algoritme K-Means Clustering Dan Naive Bayes Classifier Untuk Klasifikasi Diagnosa Penyakit Pada Kucing,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 3, no. 1, pp. 968–973, 2019.

A. Rajkumar, “Comparison of Fuzzy Diagnosis with K-Nearest Neighbor and Naïve Bayes-2010.pdf,” Glob. J. Comput. Sci. Technol., vol. 10, no. 10, pp. 38–43, 2010.

A. Desiani, “Perbandingan Implementasi Algoritma Naïve Bayes dan K-Nearest Neighbor Pada Klasifikasi Penyakit Hati,” Simkom, vol. 7, no. 2, pp. 104–110, 2022, doi: 10.51717/simkom.v7i2.96.

A. B. Wibisono and A. Fahrurozi, “Perbandingan Algoritma Klasifikasi Dalam Pengklasifikasian Data Penyakit Jantung Koroner,” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 3, pp. 161–170, 2019, doi: 10.35760/tr.2019.v24i3.2393.

S. Sulastri, K. Hadiono, and M. T. Anwar, “Analisis Perbandingan Klasifikasi Prediksi Penyakit Hepatitis Dengan Menggunakan Algoritma K-Nearest Neighbor, Naïve Bayes Dan Neural Network,” Dinamik, vol. 24, no. 2, pp. 82–91, 2020, doi: 10.35315/dinamik.v24i2.7867.

A. Suragala, P. Venkateswarlu, and M. China Raju, “A Comparative Study of Performance Metrics of Data Mining Algorithms on Medical Data,” Lect. Notes Electr. Eng., vol. 698, no. February 2021, pp. 1549–1556, 2021, doi: 10.1007/978-981-15-7961-5_139.

V. Biksham, V. Srujana, I. Meghana, B. Harshath, and G. Tarun, “Heart Disease Prediction Using Machine Learning,” Ymer, vol. 21, no. 4, pp. 489–494, 2022, doi: 10.37896/YMER21.04/48.

E. Wijaya, “Implementation Analysis of GLCM and Naive Bayes Methods in Conducting Extractions on Dental Image,” IOP Conf. Ser. Mater. Sci. Eng., vol. 407, no. 1, 2018, doi: 10.1088/1757-899X/407/1/012146.

J. N. Peksi, B. Yuwono, and Y. M. Florestiyanto, “Classification of Anemia with Digital Images of Nails and Palms using the Naive Bayes Method,” J. Inform. dan Teknol. Inf., vol. 18, no. 1, pp. 118–130, 2021, doi: 10.31515/telematika.v18i1.4587.

P. Chalekar, S. Shroff, S. Pise, and S. Panicker, “Use of K-Nearest Neighbor in Thyroid Disease Classification,” Int. J. Curr. Eng. Sci. Res., vol. 1, no. 2, pp. 2394–0697, 2014.

J. M. Martínez-Otzeta and B. Sierra, “Analysis of the iterated probabilistic weighted k nearest neighbor method, a new distance-based algorithm,” ICEIS 2004 - Proc. Sixth Int. Conf. Enterp. Inf. Syst., no. January, pp. 233–240, 2004, doi: 10.5220/0002605402330240.

A. K. Saputro, K. A. Wibisono, and F. P. Pratiwi, “Identification of Disease Types on Tea - Plant Varieties Based Image Processing with K-Nearest Neighbor Method,” J. Phys. Conf. Ser., vol. 1569, no. 3, 2020, doi: 10.1088/1742-6596/1569/3/032078.


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