Penerapan Teorema Bayes untuk Mendiagnosa Penyakit Telinga Hidung Tenggorokan (THT)
DOI:
https://doi.org/10.30812/matrik.v18i2.398Keywords:
Ear, Nose and Throat disease, Bayes theorem, expert systems, computational timeAbstract
Ear, Nose and Throat Disease (ENT) is a common disease that is often considered a harmless disease by people, so they assume there is no need to see a doctor. But in fact, ENT disease can also provide serious disorders if not treated early and right, so the expert system of ENT is needed for early detection before the patient decided to see a doctor or not. Based on these problems, this study proposed the application of the Bayes theorem for early detection of ENT disease. The types of diseases used in this study were six types and had 22 symptoms. User inputs the symptoms of a disease, then the system will provide a diagnosis. This diagnosis can be used as an initial reference for sufferers and can be used as a reference for young doctors who are taking medical education with an ENT specialist. In this initial research, testing was carried out to calculate the computational time needed by the system to diagnose ENT disease. Based on this research, we found that the average computing time needed by the system to diagnose is 00:09:54 or Nine minutes, fifty-four seconds.
Downloads
Download data is not yet available.
References
[1] S. V., “Karakteristik penderita tonsilitis kronis yang diindikasikan tonsilektomi Di RSUD Raden Mattaher Jambi,†Skripsi, 2013
[2] N. K. Pebriyanti and A. W. Andika, “Sistem Pakar Penentuan Tanaman Obat pada Penyakit THT berbasis Web,†SINTECH (Science Inf. Technol. J., vol. 1, no. 1, pp. 34–40, 2018
[3] Y. R. Nasution and Khairuna, “Sistem pakar deteksi awal penyakit tuberkulosis dengan metode bayes,†Klorofil, vol. 1, no. 1, pp. 17–23, 2017
[4] R. Ramadhan, “Pemodelan Sistem Pakar Diagnosa Penyakit Tanaman Cabai Merah Dengan Metode Fuzzy-Ahp.,†Repos. J. Mhs. PTIIK UB., vol. 6, no. 7, 2015
[5] Hamdani, “Sistem Pakar Untuk Diagnosa Penyakit Pada Manusia,†J. Inform. Mulawarman, vol. 5, no. 2, pp. 13-21., 2010
[6] M. A. Fahmy, I. P. Ningrum, and J. Y. Sari, “Sistem pakar diagnosis penyakit hewan sapi dengan metode forward chaining,†no. December, 2018
[7] Y. Hendriana, “PROGRAM BANTU IDENTIFIKASI PENYAKIT THT,†in Simposium Nasional Teknologi Terapan (SNTT), 2013, pp. 58–63
[8] S. Wahyu, P., Muhammad A.W. dan Bagus, “Sistem pakar berbasis web untuk diagnosa awal penyakit THT,†in Prosiding SNATI Yogyakarta, 2008
[9] H. T. Sihotang, E. Panggabean, and H. Zebua, “Sistem Pakar Mendiagnosa Penyakit Herpes Zoster Dengan Menggunakan Metode Teorema Bayes,†J. Inform. Pelita Nusant., vol. 3, no. 1, pp. 33–40, 2018
[10] C. Vikasari., “Modernisasi Teknologi Realtime pada Pelelangan Ikan dalam Menumbuhkan Perekonomian Berbasis Kemaritiman,†JUITA J. Inform., 2018.
[2] N. K. Pebriyanti and A. W. Andika, “Sistem Pakar Penentuan Tanaman Obat pada Penyakit THT berbasis Web,†SINTECH (Science Inf. Technol. J., vol. 1, no. 1, pp. 34–40, 2018
[3] Y. R. Nasution and Khairuna, “Sistem pakar deteksi awal penyakit tuberkulosis dengan metode bayes,†Klorofil, vol. 1, no. 1, pp. 17–23, 2017
[4] R. Ramadhan, “Pemodelan Sistem Pakar Diagnosa Penyakit Tanaman Cabai Merah Dengan Metode Fuzzy-Ahp.,†Repos. J. Mhs. PTIIK UB., vol. 6, no. 7, 2015
[5] Hamdani, “Sistem Pakar Untuk Diagnosa Penyakit Pada Manusia,†J. Inform. Mulawarman, vol. 5, no. 2, pp. 13-21., 2010
[6] M. A. Fahmy, I. P. Ningrum, and J. Y. Sari, “Sistem pakar diagnosis penyakit hewan sapi dengan metode forward chaining,†no. December, 2018
[7] Y. Hendriana, “PROGRAM BANTU IDENTIFIKASI PENYAKIT THT,†in Simposium Nasional Teknologi Terapan (SNTT), 2013, pp. 58–63
[8] S. Wahyu, P., Muhammad A.W. dan Bagus, “Sistem pakar berbasis web untuk diagnosa awal penyakit THT,†in Prosiding SNATI Yogyakarta, 2008
[9] H. T. Sihotang, E. Panggabean, and H. Zebua, “Sistem Pakar Mendiagnosa Penyakit Herpes Zoster Dengan Menggunakan Metode Teorema Bayes,†J. Inform. Pelita Nusant., vol. 3, no. 1, pp. 33–40, 2018
[10] C. Vikasari., “Modernisasi Teknologi Realtime pada Pelelangan Ikan dalam Menumbuhkan Perekonomian Berbasis Kemaritiman,†JUITA J. Inform., 2018.
Downloads
Published
2019-05-29
Issue
Section
Articles
How to Cite
[1]
I. Imamah and A. Siddiqi, “Penerapan Teorema Bayes untuk Mendiagnosa Penyakit Telinga Hidung Tenggorokan (THT)”, MATRIK, vol. 18, no. 2, pp. 268–275, May 2019, doi: 10.30812/matrik.v18i2.398.
Similar Articles
- Moh.Erwin Indrawan, Ahmat Adil, IMPLEMENTASI RESTFUL WEB SERVICE ONE CHIP MULTI-CLIENT UNTUK MENGOPTIMALKAN PENJUALAN PULSA ALL OPERATOR , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 15 No. 2 (2016)
- Lusiana Efrizoni, Sarjon Defit, Muhammad Tajuddin, Anthony Anggrawan, Komparasi Ekstraksi Fitur dalam Klasifikasi Teks Multilabel Menggunakan Algoritma Machine Learning , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 3 (2022)
- M. Khairul Anam, Bunga Nanti Pikir, Muhammad Bambang Firdaus, Susi Erlinda, Agustin Agustin, Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen dan Pemeritah , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 1 (2021)
- Christofer Satria, Peter Wijaya Sugijanto, Anthony Anggrawan, I Nyoman Yoga Sumadewa, Aprilia Dwi Dayani, Rini Anggriani, Multi-Algorithm Approach to Enhancing Social Assistance Efficiency Through Accurate Poverty Classification , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 1 (2024)
- Yarza Aprizal, Rabin Ibnu Zainal, Afriyudi Afriyudi, Perbandingan Metode Backpropagation dan Learning Vector Quantization (LVQ) Dalam Menggali Potensi Mahasiswa Baru di STMIK PalComTech , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 18 No. 2 (2019)
- Hermila A., Rahmat Taufik R. L Bau, Sitti Suhada, Abdulaziz Ahmed siyad, Predicting Gen Z’s Sentiments on Gorontalo’s CulturalWisdom UsingSentiment Analysis Models , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 2 (2025)
- Anthony Anggrawan, Mayadi Mayadi, Application of KNN Machine Learning and Fuzzy C-Means to Diagnose Diabetes , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 2 (2023)
- Deni Marta, M. Angga Eka Putra, Guntoro Barovih, Analisis Perbandingan Performa Virtualisasi Server Sebagai Basis Layanan Infrastructure As A Service Pada Jaringan Cloud , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 19 No. 1 (2019)
- Magdalena Ariance Ineke Pakereng, Alz Danny Wowor, Yos Richard Beeh, Felix David, Erwien Christianto, Vincent Exelcio Susanto, Claudio Canavaro, Square Transposition Method with Adaptive Key Flexibility and Strong Diffusion Performance , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 3 (2025)
- Abd Mizwar A Rahim, Andi Sunyoto, Muhammad Rudyanto Arief, Stroke Prediction Using Machine Learning Method with Extreme Gradient Boosting Algorithm , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 3 (2022)
You may also start an advanced similarity search for this article.
.png)
