Diagnosa Penyakit Kulit Menggunakan Case Based-Reasoning dan Self Organizing Maps

Fhatiah Adiba(1*), Nurul Mukhlisah Abdal(2), Andi Akram Nur Risal(3),

(1) Universitas Negeri Makassar
(2) Universitas Negeri Makassar
(3) Universitas Negeri Makassar
(*) Corresponding Author




DOI: https://doi.org/10.26858/ijfs.v6i1.13967

Abstract


This study aims to compare the results of the accuracy and speed of the system in diagnosing skin diseases using the case based reasoning (CBR) method with the indexing method and without using indexing. Self-organizing maps (SOM) are used as an indexing method and the process of finding similarity values uses the nearest neighbor method. Testing is done with two scenarios. The first scenario uses CBR without indexing self-organizing maps, the second scenario uses CBR with indexing self-organizing maps. The accuracy of the diagnosis of skin diseases at a threshold ≥80 for CBR without indexing self-organizing maps is 93.46% with an average retrieve time of 0.469 seconds while CBR testing using SOM indexing is 92.52% with an average retrieve time of 0.155 seconds. The results of comparison of CBR methods without using show higher results than using SOM indexing, but the process of retrieving CBR using SOM is faster than not using indexing

Keywords


case based reasoning (CBR), self-organizing maps (SOM), nearest neighbor, indexing

Full Text:

PDF

References


Deboeck, G., & Kohonen, T. (2013). Visual explorations in finance: with self-organizing maps. Springer Science & Business Media.

GIRI, G. S. T. A. Y. U. V. M., & Harjoko, A. (2015). Sistem Rekomendasi Musik Berdasarkan Konteks Menggunakan Case-Based Reasoning dan Self Organizing Map. Universitas Gadjah Mada.

Guessoum, S., Laskri, M. T., & Lieber, J. (2014). RespiDiag: a case-based reasoning system for the diagnosis of chronic obstructive pulmonary disease. Expert Systems with Applications, 41(2), 267–273.

Kolodner, J. (2014). Case-based reasoning. Morgan Kaufmann.

Mancasari, U. K. (2012). Sistem Pakar Menggunakan Penalaran Berbasis Kasus Untuk Mendiagnosa Penyakit Saraf Pada Anak. Skripsi. Prodi S1 Ilmu Komputer, Universitas Gadjah Mada, Yogyakarta.

Murphy, P. M. (1994). UCI repository of machine learning databases. Ftp:/Pub/Machine-Learning-Databaseonics. Uci. Edu.

Papuangan, M. (2019). Penerapan Case Based Reasoning Untuk Sistem Diagnosis Penyakit Hepatitis. JIKO (Jurnal Informatika Dan Komputer), 1(1), 7–12.

Prajarini, D. (2016). Perbandingan Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Kulit. INFORMAL: Informatics Journal, 1(3), 137–141.

Russell, S., & Norvig, P. (2002). Artificial intelligence: a modern approach.

SANTOSO, H. (2017). Metode Indexing pada Case Based Reasoning (CBR) menggunakan Density Based Spatial Clustering Application with Noise (DBSCAN). Universitas Gadjah Mada.

Tursina, T. (2014). Case Base Reasoning untuk Menentukan Daerah Menentukan Daerah Berpotensi Demam Berdarah (Studi Kasus Kota Pontianak). Seminar Nasional Bisnis Dan Teknologi 2014. Darmajaya Informatics and Business Institute.

Wahyudi, E., & Hartati, S. (2017). Case-Based Reasoning untuk Diagnosis Penyakit Jantung. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 11(1), 1–10.


Article Metrics

Abstract view : 736 times | PDF view : 152 times

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.



Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.