Aplikasi Diagnosa Penyakit Tuberkulosis Menggunakan Metode Logika Fuzzy

Abstract

Tuberculosis (TB) is a severe lung disease that often goes undetected because its symptoms are similar to those of a common cold. In 2022, Indonesia recorded over 700,000 TB cases, with one of the primary challenges being long hospital queues that delayed services. Advances in information technology, particularly in TB diagnosis, have the potential to speed up patient handling. With cases reaching 969,000 and 93,000 deaths annually, an early diagnosis system is essential to minimize delays. Fuzzy logic, an artificial intelligence approach, effectively manages uncertainty and complexity through "IF...THEN" rules and a defuzzification process that produces concrete outcomes. Implementing fuzzy logic is expected to enhance the effectiveness and efficiency of TB diagnosis and treatment. Through the analysis, design, and implementation of a fuzzy logic-based TB diagnostic application, the system has delivered results that meet expectations, as verified through black box testing. While the current system rules are effective, adding additional rules could further improve system accuracy and performance, making fuzzy logic a promising solution for enhancing TB diagnosis and management.