Analisa Perbandingan Metode Teorema Bayes dan Case Based Reasoning dalam Mendeteksi Penyakit Polymyalgia Rheumatica
DOI:
https://doi.org/10.30865/klik.v4i4.1568Keywords:
Expert System; Bayes Toerema Method; Case Based Reasoning Method; Polymyalgia RheumaticaAbstract
This research aims to conduct a comparative analysis between two diagnostic methods, namely Bayes' Theorem and Case Based Reasoning, in detecting Polymyalgia Rheumatica. This disease is known for its symptoms of muscle pain and stiffness in certain parts of the body, so correct diagnosis is very important for effective disease management. The problem at hand is increasing the accuracy and effectiveness of diagnosis, and this research details how both methods are applied. The Bayes Theorem method is used to calculate the probability of the existence of a disease based on the symptoms that appear. Meanwhile, Case Based Reasoning utilizes knowledge from previous cases to determine the diagnosis in new cases. Data collection involves information on Polymyalgia Rheumatica symptoms from a number of patients. The comparative analysis includes evaluation of the accuracy, sensitivity, specificity, and computational time of both methods. It is hoped that the results of this research will provide a deeper understanding of the effectiveness of each method in supporting the diagnosis of Polymyalgia Rheumatica. These findings are expected to make an important contribution to the development of better diagnostic systems, with the potential to improve medical practitioners' ability to identify and manage this disease more efficiently. The results of the analysis using two approaches, namely Bayes' Theorem and Case-Based Reasoning, to assess Polymyalgia Rheumatica, revealed quite striking differences in the level of prediction certainty. Bayes' theorem sets the probability at around 22.1%, while the Case-Based Reasoning approach gives a probability level of up to 52%.
Downloads
References
P. S. Ramadhan, “Sistem Pakar Pendiagnosaan Dermatitis Imun Menggunakan Teorema Bayes,” InfoTekJar J. Nas. Inform. dan Teknol. Jar., vol. 3, no. 1, pp. 43–48, 2018.
Warna, “Implementasi Algoritma Certainty Factor untuk Mendiagnosa Penyakit yang Disertai Demam,” vol. IV, pp. 129–137, 2023.
R. Rachman, “Sistem Pakar Deteksi Penyakit Refraksi Mata Dengan Metode Teorema Bayes Berbasis Web,” J. Inform., vol. 7, no. 1, pp. 68–76, 2020.
N. Hidayat, R. Saragih, and H. Khair, “Diagnosa Penyakit Turunan (Heraditas) Imbisil Pada Manusia Dengan Metode Dempster Shafer,” J. Ilm. Multidisiplin, vol. 1, no. 10, pp. 3780–3788, 2022.
N. Veldasari, A. Fadli, A. W. Wardhana, and M. S. Aliim, “Analisis Perbandingan Metode Certainty Factor, Dempster Shafer dan Teorema Bayes dalam Deteksi Dini Gangguan Kesehatan Mental,” J. Pendidik. dan Teknol. Indones., vol. 2, no. 7, pp. 329–339, 2022, doi: 10.52436/1.jpti.191.
N. J. Telambanua, N. Nofriadi, and A. Dermawan, “Sistem Pakar Untuk Mendeteksi Penyakit Mata Menerapkan Metode Case Based Reasoning,” Build. Informatics, Technol. Sci., vol. 4, no. 2, pp. 570–580, 2022, doi: 10.47065/bits.v4i2.2116.
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, 2018.
A. U. Bani and F. Nugroho, “Sistem Pakar Dalam Diagnosa Penyakit Tuberkulosis Otak Menggunakan Metode Certainty Factor,” J. Media Inform. Budidarma, vol. 4, no. 4, pp. 1170–1174, 2020, doi: 10.30865/mib.v4i4.2507.
H. Marfalino, T. Novita, and D. Djesmedi, “Sistem Pakar Diagnosa Penyakit Saluran Pencernaan Pada Manusia Dengan Metode Cased Based Reasoning,” J. Sains Inform. Terap., vol. I, no. 2, pp. 83–88, 2022, [Online]. Available: https://rcf-indonesia.org/jurnal/index.php/jsit.
S. A. Rahman and S. Sumijan, “Sistem Pakar Menggunakan Metode Case Based Reasoning dalam Akurasi Penyakit Disebabkan oleh Bakteri Staphylococcus Aureus,” J. Sistim Inf. dan Teknol., vol. 3, pp. 13–19, 2021, doi: 10.37034/jsisfotek.v3i1.38.
S. Nainggolan and F. A. Sianturi, “Sistem Pakar Mendiagnosa Penyakit Kulit Menggunakan Metode Teorema Bayes,” JIKOMSI [Jurnal Ilmu Komput. dan Sist. Informasi], vol. 3, no. 3, pp. 192–196, 2021.
A. Wijayanti, F. N. Arifah, D. E. Putri, M. D. Satriyanto, and S. Sallu, “Sistem Pakar Dalam Mendiagnosa Penyakit Tubercolosis dengan mengimplementasikan Metode Case Based Reasoning,” J. Comput. Syst. Informatics, vol. 4, no. 3, 2023.
N. Budiana Informatika, “Implementasi Metode Dempster-Shafer Untuk Diagnosa Penyakit Ikan Kerapu Macan,” Teknologipintar.org, vol. 3, no. 5, pp. 2023–2024, 2023.
E. Oktarina et al., “PENERAPAN METODE CERTAINTY FACTOR DALAM MENDIAGNOSIS Pengaruh dan peran teknologi informasi pada saat ini benar-benar dibutuhkan di segala aspek kehidupan dan bidang , salah satunya merupakan aplikasi perangkat lunak yang menggunakan basis pengetahuan ( k,” vol. 7, no. 2, pp. 129–136, 2022.
I. Susilawati and R. Y. Simanullang, “Sistem Pakar untuk Mengidentifikasi Penyakit ITP (Idiopathic Thrombocytopenic Purpura) melalui Pendekatan Dempster Shafer,” JIKTEKS J. Ilmu Komput. dan Teknol. Inf., vol. 1, no. 3, pp. 17–24, 2023, [Online]. Available: https://jurnal.faatuatua.com/index.php/JIKTEKS/article/view/10.
M. F. Azmi, P. Purwadi, and G. Syahputra, “Sistem Pakar Mendeteksi Gizi Buruk Pada Balita Menggunakan Metode Case Based Reasoning,” J. Cyber Tech, vol. 1, no. 3, 2022.
R. R. Girsang and H. Fahmi, “Sistem Pakar Mendiagnosa Penyakit Mata Katarak Dengan Metode Certainty Factor Berbasis Web,” MATICS J. Ilmu Komput. dan Teknol. Inf. (Journal Comput. Sci. Inf. Technol., vol. 11, no. 1, pp. 27–31, 2019.
A. H. Nasyuha, Y. Syahra, M. I. Perangin-Angin, D. R. Habibie, and A. A. Subagyo, “Sistem Pakar Dalam Mendiagnosis Penyakit Leishmaniasis Menerapkan Metode Case-Based Reasoning (CBR),” J. MEDIA Inform. BUDIDARMA, vol. 7, no. 2, pp. 747–755, 2023.
M. R. Fadillah, B. Andika, and D. Saripurna, “Sistem Pakar Mendiagnosa Penyakit Dan Hama Penyerang Tanaman Bougenville Dengan Metode Teorema Bayes,” J. SAINTIKOM (Jurnal Sains Manaj. Inform. Dan Komputer), vol. 19, no. 1, pp. 88–99, 2020.
H. Hafizah, “Sistem Pakar Untuk Pendiagnosaan Karies Gigi Menggunakan Teorema Bayes,” J. Teknol. Sist. Inf. dan Sist. Komput. TGD, vol. 4, no. 1, pp. 103–111, 2021.
D. APRIZA and S. Samsuryadi, “Perbandingan Metode Euclidean Distance Dengan Coefficient Correlation Pada Klasifikasi Penyakit Multiple Sclerosis Lesion …,” 2019, [Online]. Available: https://repository.unsri.ac.id/23207/%0Ahttps://repository.unsri.ac.id/23207/52/RAMA_55201_09121402019_0004027101_01_front_ref.pdf.
I. Istiadi, Emma Budi Sulistiarini, Rudy Joegijantoro, and Affi Nizar Suksmawati, “Perbandingan Metode CBR dan Dempster-Shafer pada Sistem Pakar Terintegrasi Layanan Kesehatan,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1143–1152, 2021, doi: 10.29207/resti.v5i6.3612.
C. Nugroho, A. D. Wiranata, and R. T. Aldisa, “Sistem Pakar untuk Mendeteksi Gejala Awal Penyakit Apendisitis dengan Metode Case Based Reasoning (CBR) Berbasis Mobile Android,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 6, no. 4, pp. 543–547, 2022.
I. Dinata and S. Sofiana, “Implementasi Diagnosa Penyakit Pada Tanaman Anggur Menggunakan Metode CBR (Case Based Reasoning) Berbasis Website,” OKTAL J. Ilmu Komput. dan Sains, vol. 1, no. 06, pp. 630–640, 2022.
I. Nugraha and M. Siddik, “Penerapan Metode Case Based Reasoning (CBR) Dalam Sistem Pakar Untuk Menentukan Diagnosa Penyakit Pada Tanaman Hidroponik,” J. Mhs. Apl. Teknol. Komput. dan Inf., vol. 2, no. 2, pp. 91–96, 2021.
D. Dona, H. Maradona, and M. Masdewi, “Sistem Pakar Diagnosa Penyakit Jantung Dengan Metode Case Based Reasoning (Cbr),” Zo. J. Sist. Inf., vol. 3, no. 1, pp. 1–12, 2021.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisa Perbandingan Metode Teorema Bayes dan Case Based Reasoning dalam Mendeteksi Penyakit Polymyalgia Rheumatica
ARTICLE HISTORY
Issue
Section
Copyright (c) 2024 Intan Putri F, Siti Emalia, Agus Iskandar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
