PREDIKSI PENYAKIT TUBERCULOSIS (TBC) MENGGUNAKAN ALGORITMA C4.5

Deny Febriyanto, Yogiek Indra Kurniawan

Abstract


Tuberculosis (TB) is an infectious disease caused by mycobacteria, especially Mycobacterium tuberculosis. TB disease remains one of the threats, especially in a country with low and middle economic levels. According to the World Health Organization (WHO) TB disease became one of the biggest causes of death in the world. Indonesia ranks second with the largest number of TB cases in the world. In addition, there are some symptoms and factors that can cause a person affected by TB disease. For the treatment of TB disease can be done intensively, but it takes a long time. Research in the diagnosis of disease with classification techniques using C4.5 algorithm has been done by several previous researchers and get good results. Therefore, in this research will be predicted TB disease using C4.5 algorithm. The C4.5 algorithm is chosen because it is very easy to interpret, fast, and has high precision accuracy. The results of this study is an application that can help people to make the diagnosis of TB disease from an early age.


Keywords


Data Mining, C4.5, Prediction of Disease, Tuberculosis

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References


Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., Honrao, V. (2013). Predicting Students' Performance Using ID3 And C4.5 Classification Algorithms. International Journal of Data Mining & Knowledge Management Process, 3(5), 39-52. Doi: 10.5121/ijdkp.2013.3504

Amin, R. K., Indwiarti, Sibaroni, Y. (2015). Implementation of Decision Tree Using C4.5 Algorithm in Decision Making of Loan Application by Debtor (Case Study:Bank Pasar of Yogyakarta Special Region). Tersedia dari Information and Communication Technology (ICoICT ): 2015 3rd International Conference, Nusa Dua, Bali. IEEE. Doi: 10.1109/ICoICT.2015.7231400

Cheon, S. A., Cho, H. H., Kim, J., Lee, J., Kim, H., Park, T. J. (2016). Recent tuberculosis diagnosis toward the end TB strategy. Journal of Microbiological Methods, 123, 51-61. Doi:10.1016/j.mimet.2016.02.007

Deliu, I., Manea, E. (2014). The Incidence Of Mycobacterium Tuberculosis Infections And Resistance To AntiTuberculosis Drugs In Giurgiu Population. Current Trends in Natural Sciences, 3(6), 27-31.

Ekata., Tyagi, P. K., Gupta, N. K., Gupta, S. (2016). Diagnosis of Pulmonary Tuberculosis using Fuzzy Inference System. Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH-16), 3-7. Doi: 10.1109/CIPECH.2016.7918726

Eticha, B. M., Atomsa, A., Tsehaineh, B., Berheto, T. M. (2014). Patients’ perspectives of the quality of tuberculosis treatment services in South Ethiopia. American Journal of Nursing Science, 3(4), 48-55. Doi: 10.11648/j.ajns.20140304.12

Hanik, U. (2011). Fuzzy Decision Tree dengan Algoritma C4.5 pada Data Diabetes Indian Pima, Institut Teknologi Sepuluh November. Surabaya.

Han, J., Kamber, M., Pei, J. (2011). Data mining : concepts and techniques (3rd ed.). USA: Morgan Kaufmann.

Harryanto, F., Hansun, S. (2017). Penerapan Algoritma C4.5 untuk memprediksi Penerimaan Calon Pegawai Baru di PT WISE. Jatisi, 3(2), 95-103.

J. R. Quinlan, C4.5: Programs for Machine Learning, San Mateo: Morgan Kaufmann, 1993.

Kamagi, D. H., Hansun, S. (2014) Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa. ULTIMATICS, 6(1), 15-20.

Kementrian Kesehatan Republik Indonesia. (2014). Pedoman Nasional Pengendalian Tuberkulosis. Tulisan pada : http://spiritia.or.id/dokumen/pedoman-tbnasional2014.pdf [Diakses pada 17 oktober 2017]

Kementrian Kesehatan Republik Indonesia. (2016). Berapa Kasus TBC di Indonesia?. Tulisan pada : http://databoks.katadata.co.id/datapublish/2017/03/22/berapa-kasus-tbc-di-indonesia [Diakses pada 17 oktober 2017]

Kumar, P. S., Umatejaswi, V. (2017). Diagnosing Diabetes using Data Mining Techniques. International Journal of Scientific and Research Publications, 7(6), 705-709.

Kurniawan, Y. I., Dwiyatmika, W. (2017). Aplikasi Diagnosa Retardasi Mental Pada Anak. Tersedia dari Prosiding SEMNAS Penguatan Individu di Era Revolusi Informasi.

Marwana. (2014). Algoritma C4.5 untuk Simulasi Prediksi Kemenangan Dalam Pertandingan Sepakbola, STIMED, Nusa Palapa, Makassar.

Padila. (2013). Asuhan Keperawatan Penyakit Dalam. Nuha Medika. Yogyakarta.

Purushottam., Saxena, K., Sharma, R. (2016). Efficient Heart Disease Prediction System using Decision Tree. International Conference on Computing, Communication and Automation (ICCCA2015), 85, 962-969. Doi:10.1016/j.procs.2016.05.288

Sandi, G., Supangkat, S. H., Slamet C. (2016). Health risk prediction for treatment of hypertension. Tersedia dari Cyber and IT Service Management: International Conference, Bandung, Indonesia. IEEE. Doi : 10.1109/CITSM.2016.7577584

World Health Organization. (2015). TB comorbidities and risk factors. Tulisan pada : http://www.who.int/tb/areas-of-work/treatment/risk-factors/en/ [Diakses pada 17 oktober 2017]

World Health Organization. (2016). The top 10 causes of death. Tulisan pada : http://www.who.int/mediacentre/factsheets/fs310/en/ [Diakses pada 17 oktober 2017]

World Health Organization. (2016). Global tuberculosis report 2016. Tulisan pada : http://www.who.int/tb/publications/global_report/en/ [Diakses pada 17 oktober 2017]

Zulvia, F., Kuo, R., Roflin, E. (2017). Efficient Heart Disease Prediction System using Decision Tree. IEEE, 204-209. Doi:10.1109/COMPSAC.2017.57.




DOI: http://dx.doi.org/10.30646/sinus.v16i2.366

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