Perbandingan Keefektifan Metode Case-Based Reasoning dan Certainty Factor dalam Sistem Pakar Diagnosis Penyakit Multiple Sclerosis

 Hanifah Ekawati (STMIK Widya Cipta Dharma, Samarinda, Indonesia)
 (*)Ita Arfyanti Mail (STMIK Widya Cipta Dharma, Samarinda, Indonesia)
 Tommy Bustomi (Politeknik Negeri Samarinda, Samarinda, Indonesia)

(*) Corresponding Author

Submitted: July 23, 2023; Published: October 25, 2023


The management of complex neurological diseases such as Multiple Sclerosis (MS) requires accurate and efficient diagnostic approaches. To enhance diagnostic precision, a study has conducted a comparison between two approaches within the framework of an expert system, namely the Case-Based Reasoning (CBR) Method and the Certainty Factor (CF) Method. The primary objective of this study is to evaluate the effectiveness of these two methods in supporting the diagnosis process of Multiple Sclerosis. The Case-Based Reasoning Method is an approach that relies on past experiences to address new issues. Within an expert system, CBR utilizes knowledge from previous cases to identify diagnoses that align with the current situation. On the other hand, the Certainty Factor Method is an approach that measures the confidence level in a statement based on rules and associated confidence factors. This study makes use of a dataset containing information from previous cases related to the diagnosis of Multiple Sclerosis. By employing both of these methods, an expert system is developed to provide diagnostic recommendations based on inputted symptoms and data. The effectiveness of both approaches is evaluated through diagnostic accuracy, computational speed, and confidence levels in the generated results. Research findings indicate that both methods have their respective strengths and weaknesses. The CBR method tends to yield accurate results by referring to similar cases in the past, but it may encounter challenges in unique or rare cases. On the other hand, the Certainty Factor Method has the ability to handle uncertainty and can produce results with measurable confidence levels. However, dependence on predefined rules may limit adaptation to new cases. In conclusion, this study underscores that there is no singular perfect approach within expert systems for diagnosing Multiple Sclerosis. Both the CBR and Certainty Factor methods contribute in their own ways to improving accuracy and confidence in the diagnosis process. Therefore, integrating these two methods could be a promising direction for the development of expert systems in the future.


Expert System; Case Based Reasoning; Certainty Factor; Multiple Sclerosis

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D. APRIZA and S. Samsuryadi, “Perbandingan Metode Euclidean Distance Dengan Coefficient Correlation Pada Klasifikasi Penyakit Multiple Sclerosis Lesion …,” 2019, [Online]. Available:

Warna, “Implementasi Algoritma Certainty Factor untuk Mendiagnosa Penyakit yang Disertai Demam,” vol. IV, pp. 129–137, 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.

J. Coding et al., “Implementasi Metode Dempster Shafer Pada Sistem Pakar Diagnosa Infeksi Penyakit Tropis Berbasis Web,” J. Coding, vol. 06, no. 03, pp. 97–106, 2018.

M. R. Fadhillah, I. Ishak, and P. S. Ramadhan, “Implementasi Sistem Pakar Mendiagnosa Penyakit Penyakit Gastritis Dengan Menggunakan Metode Teorema Bayes,” J. Teknol. Sist. Inf. dan Sist. Komput. TGD, vol. 4, no. 1, pp. 1–9, 2021.

A. A. S. Nyoman Irvianti Windaputri, Sri Widowati, “Sistem Pakar Diagnosa Penyakit Mata Menggunakan Metode Forward Chaining dan Certainty Factor,” J. Teknol. Terap. Sains, vol. 1, no. 3, pp. 107–119, 2020.

N. Situmeang and S. Sulindawaty, “Sistem Pakar Mendiagnosa Penyakit Saraf Pusat Manusia Dengan Metode Certainty Factor,” REMIK (Riset dan E-Jurnal Manaj. Inform. Komputer), vol. 4, no. 1, p. 28, 2019, doi: 10.33395/remik.v4i1.10224.

G. W. Nyipto Wibowo, S. Widiastuti, M. Muratno, E. Lolang, and S. Soraya, “Penerapan Metode Teorema Bayes Dalam Mendiagnosa Penyakit Tubercolosis,” Build. Informatics, Technol. Sci., vol. 4, no. 4, pp. 254–263, 2023, doi: 10.47065/bits.v4i4.3035.

Sulindawaty and M. Lestari, “Sistem Pakar Mendiagnosa Penyakit Stroke Transient Ischaemic Attack(TIA) Dengan Menggunakan Metode Dempster Shafer,” J. Sist. Inf. dan Teknol. Jar., vol. 2, no. 2, pp. 25–30, 2021, [Online]. Available:

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.

R. Rachman, “Sistem Pakar Deteksi Penyakit Refraksi Mata Dengan Metode Teorema Bayes Berbasis Web,” J. Inform., vol. 7, no. 1, pp. 68–76, 2020.

Y. S. R. Nur, A. Burhanuddin, D. Aldo, and W. L. Army, “Sistem Pakar Deteksi Penyakit Bawang Merah dengan Metode Case Based Reasoning,” J. MEDIA Inform. BUDIDARMA, vol. 6, no. 3, pp. 1356–1366, 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.

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.

A. J. Sitorus, J. E. Hutagalung, and A. Dermawan, “Sistem Pakar Diagnosa Penyakit Pencernaan Menggunakan Metode Case Based Reasoning (CBR) Berbasis Web,” J. MEDIA Inform. BUDIDARMA, vol. 6, no. 4, pp. 2214–2221, 2022.

I. L. K. Panjaitan, E. Panggabean, and Sulindawaty, “Analisis Perbandingan Metode Dempster Shafer dengan Metode Certainty Factor Untuk Mendiagnosa Penyakit Stroke,” J. Inform. Pelita Nusant., vol. 3, no. 1, pp. 69–74, 2018, [Online]. Available:

L. F. Putri, “Perancangan Aplikasi Sistem Pakar Penyakit Roseola Dengan Menggunakan Metode Certainty Factor,” J. Sist. Komput. dan Inform., vol. 1, no. 2, p. 107, 2020, doi: 10.30865/json.v1i2.1956.

M. Aldjawad, S. Andryana, and A. Andrianingsih, “Penerapan Metode Perbandingan Dempster-Shafer dengan Certainty Factor pada Aplikasi Sistem Pakar Deteksi Dini Penyakit Alzheimer pada Lansia Berbasis Web,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 5, no. 2, p. 144, 2021, doi: 10.35870/jtik.v5i2.206.

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.

S. Batubara, S. Wahyuni, and E. Hariyanto, “Penerapan Metode Certainty Factor Pada Sistem Pakar Diagnosa Penyakit Dalam,” in Seminar Nasional Royal (SENAR), 2018, vol. 1, no. 1, pp. 81–86.

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.

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