ANALISIS PERFORMA ALGORITMA NAIVE BAYES PADA DETEKSI OTOMATIS CITRA MRI

  • Fajar Akbar (1*) STMIK Nusa Mandiri Jakarta
  • Amin Nur Rais (2) Teknik Informatika STMIK Nusa Mandiri Jakarta
  • Irwan Agus Sobari (3) Teknik Informatika STMIK Nusa Mandiri Jakarta
  • Robi Aziz Zuama (4) Sistem Informasi Universitas Bina Sarana Informatika
  • Biktra Rudiarto (5) Teknik Informatika STMIK Nusa Mandiri Jakarta

  • (*) Corresponding Author
Keywords: Feature Extraction, Brain Tumor, Naive Bayes

Abstract

The brain in humans becomes part of the central nervous system of the human body. The use of imaging with MRI is one that can be used as a first step to detect parts of the human brain. The imaging step is the first step in diagnosing brain tumor. By performing feature extraction, which aims to process the classification of brain tumors, between normal and abnormal brain images using the naive Bayes method. Obtained 41 images which then became 39 datasets. Feature extraction results with 2 classes, normal as many as 20 data and abnormal data 19. The calculation results obtained the value of the normal class of 0.513 and the abnormal class of 0.487 the value of the calculation accuracy of 84.17%.

Downloads

Download data is not yet available.

References

Adinegoro, A., Atmaja, R. D., & Purnamasari, R. (2015). Deteksi Tumor Otak dengan Ektrasi Ciri & Feature Selection mengunakan Linear Discriminant Analysis ( LDA ) dan Support Vector Machine ( SVM ) Brain Tumor ’ s Detection With Feature Extraction & Feature Selection Using Linear Discriminant Analysis ( LDA ). E-Proceeding of Engineering, 2(2), 2532–2539.

Akbar, F., Rais, N. A., Sobari, I. A., Zuama, R. A., & Rudiarto, B. (2019). Laporan Akhir Penelitian Performa Naive Bayes pada Deteksi Citra MRI. Jakarta.

Ananda, R. S., & Thomas, T. (2012). Automatic segmentation framework for primary tumors from brain MRIs using morphological filtering techniques. 2012 5th International Conference on Biomedical Engineering and Informatics, BMEI 2012, (March 2015), 238–242. https://doi.org/10.1109/BMEI.2012.6512995

Caraka, B., Sumbodo, B. A. A., & Candradewi, I. (2017). Klasifikasi Sel Darah Putih Menggunakan Metode Support Vector Machine (SVM) Berbasis Pengolahan Citra Digital. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 7(1), 25. https://doi.org/10.22146/ijeis.15420

Cholis, M. N., & Fuad, Y. (2014). APLIKASI DETEKSI TEPI SOBEL UNTUK IDENTIFIKASI TEPI CITRA MEDIS. MATHunesa, 3(2), 15–19.

Christ, M. C. J., & Parvathi, R. M. S. (2011). Segmentation of Medical Image using Clustering and Watershed Algorithms. American Journal of Applied Sciences, 8(12), 1349–1352. https://doi.org/10.3844/ajassp.2011.1349.1352

Irawan, C., Udayanti, E. D., & Nugroho, F. A. (2013). Visualisasi dan Rekonstruksi 3D Citra Medis : Review. SEMANTIK 2013, 2013(November), 61–64.

Joseph, R. P., & Singh, C. S. (2014). Brain Tumor Mri Image Segmentation and Detection in Image Processing. International Journal of Research in Engineering and Technology, 3(1), 1–5.

Kaushik, D., Utkarsha, S., Singhal, P., & Singh, V. (2014). Brain Tumor Segmentation using Genetic Algorithm. International Journal of Computer Applications, ICACEA (5), 13–15. https://doi.org/10.15662/IJAREEIE.2016.0503043

Muhamad, H., Prasojo, C. A., Sugianto, N. A., Surtiningsih, L., Cholissodin, I., Ilmu, F., … Optimization, P. S. (2017). OPTIMASI NAÏVE BAYES CLASSIFIER DENGAN MENGGUNAKAN PARTICLE, 4(3), 180–184.

Nandpuru, H. B., Salankar, S. S., & Bora, V. R. (2014). MRI Brain Cancer Classification Using Support Vector Machine. Electrical, Electronics and Computer Science (SCEECS), 2014 IEEE Students’ Conference. IEEE., 1–6. https://doi.org/10.1109/SCEECS.2014.6804439

Rai, S., & Chakrabarty, N. (2019, May 15). Brain MRI Images for Brain Tumor Detection. Retrieved from kaggle.com: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection.

Rais, A. N., & Riana, D. (2018). Segmentasi Citra Tumor Otak Mengunakan Support Vector Machine Classifier. Seminar Nasional Inovasi Dan Tren (SNIT) 2018, 152–155.

Riana, D., Plissiti, M. E., Nikou, C., Widyantoro, D. H., & Mengko, T. L. R. (2015). Inflammatory cell extraction and nuclei detection in Pap smear images. International Journal of E-Health and Medical Communications, 6(2), 27–43. https://doi.org/10.4018/IJEHMC.2015040103

Sharma, P., Diwakar, M., & Choudhary, S. (2012). Application of Edge Detection for Brain Tumor Detection. International Journal of Computer Applications, Volume 58, 21–25.

Tearani, N. P. (2014). Peningkatan Kompresi Citra Digital Menggunakan Discrete Cosine Transform – 2 Dimension ( DCT – 2D ), 1–5.

Vasuda, P., & Satheesh, S. (2010). Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation. International Journal on Computer Science and Engineering, 2(5), 1713–1715.

Yeni Lestari Nasution, Mesran, M., Suginam, S., & Fadlina, F. (2017). Sistem Pakar Untuk Mendiagnosis Penyakit Tumor Otak Menggunakan Metode Certainty Factor (Cf). Jurnal INFOTEK, 2(1), 0–4. Retrieved from http://ejurnal.amikstiekomsu.ac.id/index.php/infotek/article/view/98

Published
2019-08-01
How to Cite
[1]
F. Akbar, A. Rais, I. Sobari, R. Zuama, and B. Rudiarto, “ANALISIS PERFORMA ALGORITMA NAIVE BAYES PADA DETEKSI OTOMATIS CITRA MRI”, jitk, vol. 5, no. 1, pp. 37-42, Aug. 2019.
Article Metrics

Abstract viewed = 1278 times
PDF downloaded = 1090 times