DIAGNOSIS PENYAKIT PARU MENGGUNAKAN CASE BASED REASONING

SIARA, MUHAMMAD RIFQI DZAKY and Rini, Dian Palupi and Kurniati, Rizki (2022) DIAGNOSIS PENYAKIT PARU MENGGUNAKAN CASE BASED REASONING. Undergraduate thesis, Sriwijaya University.

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Abstract

Decreasing the quality of clean air causes reduced lung response, while the lungs play an important role in the human respiratory system. In general, the availability of health facilities and personnel (especially pulmonary specialists) is still not evenly distributed in every district/city in Indonesia. Because of this, we need a system that is able to implement expert knowledge into the system, to carry out early treatment in diagnosing lung health conditions for people who feel general symptoms of lung disease. In this study, an approach that focuses on problem solving based on knowledge from previous cases is used, namely the Case Based Reasoning method. The application of CBR (Case Based Reasoning) is carried out to find the value of old cases and new cases. If the similarities are the same then the diagnostic results have been found. If the similarities are not the same, the results will be searched using the Sorgenfrei similarity, then these results can be used as diagnostic results and the diagnostic results can be saved as old cases. The research was conducted by conducting direct interviews with doctors or an expert to develop the knowledge process in building knowledge. Based on the data that has been tested in this study, it shows that this expert system has a fairly good accuracy rate of 82%. Menurunnya kualitas udara yang bersih menyebabkan berkurangnya respon paru, sementara paru-paru memiliki peran penting dalam sistem pernapasan manusia. Secara umum jumlah penyebaran ketersediaan fasilitas dan tenaga kesehatan (khususnya dokter spesialis paru) masih belum merata di setiap wilayah kabupaten/kota di Indonesia. Karena hal itu maka diperlukan sistem pakar yang yang mampu mengimplementasi pengetahuan pakar ke dalam sistem, untuk melakukan penanganan dini dalam mendiagnosa kondisi kesehatan paru bagi masyarakat yang merasakan gejala umum dari penyakit paru. Pada penelitian ini digunakan pendekatan yang menitik beratkan pemecahan masalah dengan didasarkan pada basis ilmu pengetahuan dari kasus-kasus sebelumnya yaitu menggunakan metode Case Based Reasoning. Penerapan CBR (Case Based Reasoning) dilakukan mencari kesamaan nilai kemiripan dari kasus lama dan kasus baru. Jika similaritas sama maka hasil diagnosa berhasil ditemukan. Jika similaritas tidak sama maka hasilnya akan dicari menggunakan similaritas Sorgenfrei, lalu hasil tersebut dapat dijadikan hasil diagnosa dan hasil diagnosa tersebut dapat disimpan menjadi kasus lama. Penelitian dilakukan dengan melakukan wawancara langsung dengan dokter atau seorang pakar untuk mengevaluasi proses akuisisi pengetahuan dalam membangun basis pengetahuan. Berdasarkan data yang sudah diuji pada penelitian ini menunjukan bahwa sistem pakar ini memiliki tingkat akurasi yang cukup baik yaitu sebesar 82%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Case Based Reasoning, Penyakit Paru, Similaritas Sorgenfrei, Sistem Pakar
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Rifqi Dzaky Siara
Date Deposited:	15 Jul 2022 02:13
Last Modified:	15 Jul 2022 02:13
URI:	http://repository.unsri.ac.id/id/eprint/73965

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