Analisis performa algoritma naive bayes pada deteksi otomatis citra mri

Analisis performa algoritma naive bayes pada deteksi otomatis citra mri

• By Akbar, Fajar; Rais, Amin Nur; Sobari, Irwan Agus; Zuama, Robi Aziz, Rudiarto, Biktra;
• Jurnal Nasional Terakreditasi
• 156
• 37-42

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.2012.651299 5 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.2011.1349.1 352 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.2016.05 03043 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.2014.6804 439 Rai, S., & Chakrabarty, N. (2019, May 15). Brain MRI Images for Brain Tumor Detection. Retrieved from kaggle.com: https://www.kaggle.com/navoneel/brain  Otak pada manusia menjadi bagian paling penting dari sistem saraf pusat tubuh manusia. Penggunaan pencitraan dengan MRI salah satunya dapat digunakan sebagai langkah awal untuk mendeteksi bagian otak manusia. Langkah pencitraan medis digunakan sebagai langkah awal pendiagnosisan terhadap penyakit tumor otak. Dengan melakukan ekstraksi fitur, yang bertujuan untuk melakukan proses klasifikasi citra tumor otak, antara citra otak normal dengan abnormal dengan menggunakan metode naive bayes. Didapatkan 41 citra yang kemudian menjadi 39 dataset hasil ekstraksi fitur dengan 2 class, normal sebanyak 20 data dan abnormal 19 data. Hasil penghitungan didapatkan nilai class normal sebesar 0.513 dan class abnormal sebesar 0.487 nilai akurasi penghitungan sebesar 84.17%. 

• Katakunci
• URL http://Analisis performa algoritma naive bayes pada deteksi otomatis citra mri
• Penerbit Jurnal JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), Jakarta DKI Jakarta

REFERENSI

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.2012.651299 5 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.2011.1349.1 352 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.2016.05 03043 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.2014.6804 439 Rai, S., & Chakrabarty, N. (2019, May 15).  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.20150401 03 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.ph p/infotek/article/view/98