DIAGNOSIS GEJALA PENYAKIT TUBERKULOSIS MENGGUNAKAN FUZZY EXPERT SYSTEM BERBASIS WEB

Tuberkulosis (TB) adalah salah satu penyakit yang menyebabkan kematian tinggi pada manusia. Pencegahan penyakit ini telah dicari oleh para profesional medis dan peneliti. Sayangnya, penanganan TB masih dilakukan secara manual dan sangat tergantung pada ahli medis yang jumlahnya terbatas, sehingga dalam penelitian ini dilakukan pengembangan sistem informasi alternatif untuk mengatasi masalah tersebut. Sistem diagnosis gejala TB ini dikembangkan menggunakan metode sistem pakar fuzzy. Data masukan pada sistem ini adalah gejala yang diderita penderita, yang terdiri dari batuk, penurunan berat badan, sesak napas, kehilangan nafsu makan, demam, berkeringat di malam hari, dan malaise. Prosesnya dimulai dari memasukkan data gejala, kemudian diproses menggunakanfuzzy yang terdiri dari proses fuzifikasi, inferensi dan defuzifikasi.Aturan penyakit diberikan oleh para ahli yang ahli di bidangnya dan dari sumber jurnal. Keluaran dari sistem menampilkan antarmuka diagnosis penyakit di web. Hasil penelitian ini adalah sistem informasi yang dapat memberikan hasil diagnosis penyakit kepada pengguna. Perhitungan nilai akurasi juga dilakukan untuk mengetahui seberapa akurat fuzzy dalam sistem ini, dan dari hasil perhitungan ditemukan bahwa nilai akurasi yang didapat adalah sebesar 82% yang menunjukkan bahwa logika fuzzy baik untuk proses diagnosis. Kata kunci — TB, pakar, sistem pakar fuzzy, logika fuzzy, diagnosis Tuberculosis (TB) is one of the diseases that causes high mortality in humans. The prevention of this disease has been sought by medical professionals and researchers. Unfortunately, the handling of TB is still manual and very dependent on medical experts who are very limited in number. In this study we propose an alternative information technology to overcome this problem. To overcome this problem a TB diagnostic system is developed using a fuzzy expert system. Input data on this system are the symptoms suffered by the sufferer, which consists of cough, weight loss, breathless, loss of appetite, fever, sweat at night, and malaise. The input data is then processed using fuzzy logic which consists of a process of fuzification, inference and defuzification. The output of the system displays the disease diagnosis interface on the web. Disease rules are given by experts who are experts in their fields and from journal sources. The results of the study are information systems that can provide the results of disease diagnosis to the user. The calculation of the accuracy value is also done to find out how accurate the fuzzy logic is in this system, and from the results of these calculations it is found that the accuracy value is 82% which shows that fuzzy logic is good for the diagnostic process. Keywords—tuberculosis, expert, fuzzy expert system, fuzzy logic, diagnosi

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