Ensemble Klasifikasi Penyakit Tuberculosis Pada Hasil Pengobatan Menggunakan Metode Hybrid K-Nearest Neighbor (K-NN), Decision Tree dan Support Vector Machine (SVM)

 (*)Alya Nurfaiza Azzahra Mail (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Junta Zeniarja (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Ardytha Luthfiarta (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Mufida Rahayu (Universitas Dian Nuswantoro, Semarang, Indonesia)

(*) Corresponding Author

Submitted: November 20, 2023; Published: January 18, 2024


Tuberculosis (TB) is an infectious disease with the highest cause of death in the world. This disease can be transmitted through the air and attacks the pulmonary respiratory system. The increase in TB cases from year to year is due to little information about the treatment of this disease. This requires the process of diagnosing and treating TB requiring accurate data analysis. From these problems, classification of tuberculosis disease is needed to improve better treatment results. In this study, experiments were used with the Hybrid model classification algorithm with a method that combines three approaches, namely K-Nearest Neighbor (K-NN), Decision Tree, Support Vector Machine (SVM) to classify treatment results using the Ensemble classification method and aims to combine each method in order to create a stronger Ensemble model and increase accuracy in treatment results, using data from the Semarang City Health Service or what is called Tuberculosis Information System (SITB) data in 2020-2023 with 80% training data and test data 20%. Based on the results of testing and analysis using the confusion matrix, the highest accuracy value was obtained at 78.55% using K-Fold Cross validation, namely k equals 7 and the Ensemble model obtained high results for treatment outcomes.


Treatment Results; K-Nearest Neighbor; Decision Tree; Support Vector Machine; Tuberculosis; Ensemble

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Lina Yunita, Rasi Rahagia, Fauziah H. Tambuala, A. Suyatni Musrah, Andi Asliana Sainal, and Suprapto, “Efektif Pengetahuan dan Sikap Masyarakat Dalam Upaya Pencegahan Tuberkulosis: Effective Knowledge and Community Attitudes in Tuberculosis Prevention Efforts,” J. Health JoH, vol. 10, no. 2, pp. 186–193, Jul. 2023, doi: 10.30590/joh.v10n2.619.

K. Tilwani, A. Patel, M. Patel, P. Sojitra, and G. Dave, “Asiaticoside A for the modulation of 1-TbAd- a potential target and ligand for extensive drug resistance Mycobacterium tuberculosis,” AMB Express, vol. 13, no. 1, p. 111, Oct. 2023, doi: 10.1186/s13568-023-01616-w.

K. Mar’iyah and Z. Zulkarnain, “Patofisiologi penyakit infeksi tuberkulosis,” Pros. Semin. Nas. Biol., vol. 7, no. 1, Art. no. 1, Nov. 2021, doi: 10.24252/psb.v7i1.23169.

V. C. Osamor and A. F. Okezie, “Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis,” Sci. Rep., vol. 11, no. 1, p. 14806, Jul. 2021, doi: 10.1038/s41598-021-94347-6.

O. Hrizi et al., “Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model,” J. Healthc. Eng., vol. 2022, pp. 1–13, Mar. 2022, doi: 10.1155/2022/8950243.

C. Prasitpuriprecha et al., “Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System,” Pharmaceuticals, vol. 16, no. 1, p. 13, Dec. 2022, doi: 10.3390/ph16010013.

W. Xing and Y. Bei, “Medical Health Big Data Classification Based on KNN Classification Algorithm,” IEEE Access, vol. 8, pp. 28808–28819, 2020, doi: 10.1109/ACCESS.2019.2955754.

L. M. Ferreira, T. Sáfadi, and J. L. Ferreira, “K-mer applied in Mycobacterium tuberculosis genome cluster analysis,” Braz. J. Biol., vol. 84, p. e258258, 2024, doi: 10.1590/1519-6984.258258.

M. A. Elashmawy, I. Elamvazuthi, L. I. Izhar, S. Paramasivam, and S. Su, “Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 8, 2023, doi: 10.14569/IJACSA.2023.0140808.

R. Kadry and O. Ismael, “A New Hybrid KNN Classification Approach based on Particle Swarm Optimization,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 11, 2020, doi: 10.14569/IJACSA.2020.0111137.

R. Ruswanto, M. Mardhiah, R. Mardianingrum, and K. Novitriani, “SINTESIS DAN STUDI IN SILICO SENYAWA 3-NITRO-N’-[(PYRIDIN-4-YL) CARBONYL]BENZOHYDRAZIDE SEBAGAI KANDIDAT ANTITUBERKULOSIS,” Chim. Nat. Acta, vol. 3, no. 2, Aug. 2015, doi: 10.24198/cna.v3.n2.9183.

A. H. Husen, A. S. Nur Afiah, S. Soesanti, and F. Tempola, “Deteksi Dini Resiko Tuberkulosis di Kota Ternate: Pelacakan dan Implementasi Algoritma Klasifikasi,” J. CoSciTech Comput. Sci. Inf. Technol., vol. 3, no. 2, pp. 217–225, Aug. 2022, doi: 10.37859/coscitech.v3i2.3986.

V. M. Santi, L. Nafisah, and Q. Meidianingsih, “Penerapan Metode SMOTE CHAID dalam Klasifikasi Tuberkulosis Relapse,” J. Stat. Dan Apl., vol. 6, no. 1, pp. 26–36, Jun. 2022, doi: 10.21009/JSA.06103.

Yanti Apriyani, I. D. I. Iskandar, Mira Kusmira, Melisa Winda Pertiwi, Imam Amirulloh, and Taufik Wibisono, “Implementasi Sistem Pakar dengan Algortima Naïve Bayes dengan Laplace Correction untuk Diagnosis Tuberkulosis Paru,” Inf. J. Inform. Dan Sist. Inf., vol. 13, no. 1, pp. 24–46, May 2021, doi: 10.37424/informasi.v13i1.72.

Y.-D. Zhang, W. Wang, X. Zhang, and S.-H. Wang, “Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM,” Mob. Netw. Appl., Feb. 2022, doi: 10.1007/s11036-021-01901-7.

A. Kumar, A. Verma, G. Shinde, Y. Sukhdeve, and N. Lal, “Crime Prediction Using K-Nearest Neighboring Algorithm,” in 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India: IEEE, Feb. 2020, pp. 1–4. doi: 10.1109/ic-ETITE47903.2020.155.

M. Park et al., “Distinguishing nontuberculous mycobacterial lung disease and Mycobacterium tuberculosis lung disease on X-ray images using deep transfer learning,” BMC Infect. Dis., vol. 23, no. 1, p. 32, Jan. 2023, doi: 10.1186/s12879-023-07996-5.

S. C. Gupta and N. Goel, “Enhancement of Performance of K-Nearest Neighbors Classifiers for the Prediction of Diabetes Using Feature Selection Method,” in 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India: IEEE, Oct. 2020, pp. 681–686. doi: 10.1109/ICCCA49541.2020.9250887.

B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 2, no. 01, pp. 20–28, Mar. 2021, doi: 10.38094/jastt20165.

S. Rajaraman, F. Yang, G. Zamzmi, Z. Xue, and S. K. Antani, “A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs,” Bioengineering, vol. 9, no. 9, p. 413, Aug. 2022, doi: 10.3390/bioengineering9090413.

A. Mahajan et al., “A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction,” Mathematics, vol. 10, no. 10, p. 1714, May 2022, doi: 10.3390/math10101714.

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