Analysis K-Nearest Neighbors (KNN) in Identifying Tuberculosis Disease (Tb) By Utilizing Hog Feature Extraction

Muhathir Muhathir, Theofil Tri Saputra Sibarani, Al-Khowarizmi Al-Khowarizmi

Abstract


Pulmonary tuberculosis is an infectious disease caused by Microbacterium tuberculosis, which is one of the lower respiratory tract disease, which is largely in the pulmonary tissue of the lung infection and then undergoes a process known as the primary focus of Ghon. Because the disease is difficult and takes a long time to decide the patient is affected by the disease Tuberkolusis, then the detection of the patient affects Tuberkolusis by utilizing the K-NN method as a classification and HOG as feature extraction. Results of the classification of positive diagnosis with a total of 234 samples from 330 samples or successfully recognizable Sebasar 70.90%, while the classification result is a negative diagnosis with the amount of 240 samples from 330 samples or successfully identified by 72.72%. The results of the study showed the image classification of the X-ray Set Tuberculosis using the method K-NN and HOG feature with cross-validation 5 folds with 71.81% accuracy.
Keyword : tuberculosis, K-NN, HOG.

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References


Wicaksono, B. S., Romadhony, A., & Sulistiyo, M. D. (2014). Analisis dan Implementasi Sistem Pendiagnosis Penyakit Tuberculosis Menggunakan Metode Case-Based Reasoning. Seminar Nasional Aplikasi Teknologi Informasi (SNATI). Yogyakarta.

Sarwani, D., Nurlaela, & Isnani, Z. A. (2012). Risk Factors of Multidrug Resistant Tuberculosis (MDR-TB). Jurnal Kesehatan Masyarakat, 8(1), 60-66.

Muniroh, N., Aisah, S., & Mifbakhuddin. (2013). Faktor-Faktor Yang Berhubunga Dengan Kesembuhan Penyakit Tuberkulosis (TBC) Paru di Wilayah Kerja Puskesmas Mangkang Semarang Barat. jurnal Keperawatan Komunitas.

Hiswani. (2008). Tuberkulosis Merupakan Penyakit Infeksi yang Masih Menjadi Masalah Kesehatan Masyarakat. Skripsi, Universitas Sumatera Utara.

Shofia, E. N., Putri, R. R., & Arwan, A. (2017). Sistem Pakar Diagnosis Penyakit Demam: DBD, Malaria dan Tifoid Menggunakan Metode K-Nearest Neighbor. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer.

Fibrianto, A., Magdalena, R., & Fuadah, Y. N. (2018). KLASIFIKASI KONDISI PARU-PARU NORMAL, PENYAKIT TUBERKULOSIS (TBC) DAN EFUSI PLEURA PADA MANUSIA MENGGUNAKAN JARINGAN SYARAF TIRUAN PROPAGASI BALIK. e-Proceeding of Engineering.

Wardani, R. S., & Purwanto. (2015). MODEL DIAGNOSIS TUBERKULOSIS MENGGUNAKAN k-NEAREST NEIGHBOR BERBASIS SELEKSI ATRIBUT. The 2nd University Research Coloquium.

Nabilla, N. N., Hidayat, B., & Suhardjo. (n.d.). DETEKSI CITRA GRANULOMA MELALUI RADIOGRAF PERIAPIKAL DENGAN METODE HISTOGRAM OF ORIENTED GRADIENTS DAN KLASIFIKASI K-NEAREST NEIGHBOR. Seminar Nasional Inovasi Dan Aplikasi Teknologi Di Industri (p. 2018). Malang: ITN Malang.

Prayudani, S., Hizriadi, A., Lase, Y. Y., & Fatmi, Y. (2019, November). Analysis Accuracy Of Forecasting Measurement Technique On Random K-Nearest Neighbor (RKNN) Using MAPE And MSE. In Journal of Physics: Conference Series(Vol. 1361, No. 1, p. 012089). IOP Publishing.

Puspitawuri, A., Santoso, E., & Dewi, C. (2019). Diagnosis Tingkat Risiko Penyakit Stroke Menggunakan Metode K-Nearest Neighbor dan Naïve Bayes. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(4).

Lubis, A. R., Lubis, M., & Al-Khowarizmi (2020). Optimization of distance formula in K-Nearest Neighbor method. Bulletin of Electrical Engineering and Informatics, 9(1), 326-338.

Pranoto, M. B., & Ramadhani, K. N. (2017). Face Detection System Menggunakan Metode Histogram of Oriented Gradients ( HOG ) dan Support Vector Machine ( SVM ). e-Proceeding Eng.

Cai, Z., Yu, P., Liang, Y., Lin, B., & Huang, H. (2016). SVM-KNN Algorithm for Image Classification Based on Enhanced HOG Feature. Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing.

Shofia, E. N., Putri, R. R., & Arwan, A. (2017). Sistem Pakar Diagnosis Penyakit Demam: DBD, Malaria dan Tifoid Menggunakan Metode K-Nearest Neighbor. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer.

Aini, N., Ramadiani, & Hatta, H. R. (2017). SISTEM PAKAR PENDIAGNOSA PENYAKIT TUBERKULOSIS. Jurnal Informatika Mulawarman, 12(1).

Alfaris, S. (2014). Sistem Pakar untuk Mendiagnosa Penyakit Polip Nasi (Polip Hidung) Menggunakan Metode Certainty Factor. Pelita Informatika Budi Darma, 7(2).

Tanjung, J. P., & Muhathir. (2020). Classification of facial expressions using SVM and HOG. JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING), 3(2), 210-215.

Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), (pp. 886-893). San Diego, Calif, USA.

SIDDIK, M. A., NOVAMIZANTI, L., & RAMATRYANA, I. N. (n.d.). Deteksi Level Kolesterol melalui Citra Mata Berbasis HOG dan ANN. ELKOMIKA, 7(2).

Alamsyah, D. (2017). Pengenalan Mobil pada Citra Digital Menggunakan HOG-SVM. Jatisi, 2.

Liantoni , F. (2015). Klasifikasi Daun Dengan Perbaikan Fitur Citra Menggunakan Metode K-Nearest Neighbor. ULTIMATICS, 2.

Lubis, A. R., Lubis, M., Al-Khowarizmi & Listriani, D. (2019, August). Big Data Forecasting Applied Nearest Neighbor Method. In 2019 International Conference on Sustainable Engineering and Creative Computing (ICSECC) (pp. 116-120). IEEE.

Al-Khowarizmi, Sitompul, O. S., Suherman & Nababan, E. B. (2017, December). Measuring the Accuracy of Simple Evolving Connectionist System with Varying Distance Formulas. In Journal of Physics: Conference Series (Vol. 930, No. 1, p. 012004). IOP Publishing.

Wu, X., & Kumar, V. (2009). The Top Ten Algorithms in Data Mining. New York: CRC Press.

Larose, D. (2005). Discovering Knowledge in Data. USA: John Wiley’s and Son.

Han, J., & Kamber, M. (2006). Data Mining:Concept and Techniques. New York: Morgan Kaufmann Publisher.

Sandi, B., Siahaan, J. K., Permana, P., & Muhathir. (2019). Klasifikasi Citra Wayang Dengan Menggunakan Metode k-NN & GLCM. Semantika (Seminar Nasional Teknik Informatika). 2(1), 71-77.

Pariyandani, A., Larasati, D. A., Wanti, E. P., & Muhathir. (2019). Klasifikasi Citra Ikan Berformalin Menggunakan Metode k-NN dan GLCM. Semantika (Seminar Nasional Teknik Informatika). 2(1), 42-47.

Al-Khowarizmi, A. K., Nasution, I. R., Lubis, M., & Lubis, A. R. (2020). The effect of a SECoS in crude palm oil forecasting to improve business intelligence. Bulletin of Electrical Engineering and Informatics, 9(4), 1604-1611.

Jaeger, S., Candemir, S., Antani, S., Wáng, Y.-X. J., Lu, P.-X., & Thoma, G. (2014). Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. AME Publishing Company.

Muhathir, Mawengkang, H., & Ramli, M. (2017). KOMBINASI Z-FISHER TRANSFORM DAN BRAY CURTIS DISTANCE UNTUK PENGENALAN POLA HURUF JAR PADA CITRA AL-QURAN. Jurnal Bisman Info, 4(1).

Muhathir, M. (2018). KLASIFIKASI EKSPRESI WAJAH MENGGUNAKAN BAG OF VISUAL WORDS. JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING), 1(2), 73-82.

Rizal, Fadlisyah, Muhathir, Akfal A.M. (2015). Detection System Tajwid Al Quran on Image Using Bray Curtis Distance. IJCAT, 2(8), 293-300.




DOI: https://doi.org/10.55311/aiocsit.v1i1.11

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