Electronic Theses and Dissertation

Tuberkulosis (TB) merupakan tantangan kesehatan global yang signifikan, dengan Indonesia menjadi negara kedua dengan kasus TB terbanyak di dunia pada tahun 2023, mencapai lebih dari satu juta kasus dan 134.000 kematian. Deteksi dini TB melalui screening sangat penting untuk meningkatkan keberhasilan pengobatan dan mengurangi penyebaran penyakit. Namun, metode diagnostik konvensional seperti tes cepat molekuler dan kultur bakteri memiliki keterbatasan dalam ketersediaan alat, biaya, dan waktu. Dalam mendukung upaya World Health Organization (WHO) untuk pengembangan metode diagnostik non-sputum yang cepat dan mudah, tim PKM-KC telah mengembangkan prototipe detektor TB berbasis hembusan napas bernama SMELTUB (Smart Measurement of Early Lung Tuberculosis). SMELTUB menggunakan teknologi Electronic Nose (eNose), kecerdasan buatan (AI), dan Internet of Things (IoT) untuk mendeteksi volatile organic compounds (VOCs) dari napas pasien. Prototipe ini dirancang untuk menawarkan metode diagnostik yang portabel, cepat, dan akurat. Pengembangan SMELTUB melibatkan tiga tahapan utama: perancangan dan pengujian fungsional prototipe, pengumpulan dataset pelatihan dari 33 sampel VOCs pasien TB dan individu sehat, serta uji implementasi di fasilitas kesehatan primer. Uji coba menunjukkan akurasi 79%, sensitivitas 78%, dan spesifisitas 80%. Hasil ini menunjukkan potensi SMELTUB sebagai alat screening TB yang efektif dan efisien. Dengan kemampuan deteksi real-time dan integrasi IoT, SMELTUB diharapkan mendukung deteksi dini TB serta memperkuat pengendalian penyakit ini di Indonesia, sejalan dengan target eliminasi TB global pada tahun 2030.

Tuberculosis (TB) remains a significant global health challenge, with Indonesia ranking as the second-highest country for TB cases worldwide in 2023, reporting over one million cases and 134,000 deaths. Early TB detection through screening is crucial for improving treatment outcomes and reducing transmission. However, conventional diagnostic methods such as molecular rapid tests and bacterial culture face limitations in accessibility, cost, and time. To support the World Health Organization (WHO) initiative for developing rapid and non-sputum diagnostic methods, the PKM-KC team has developed a breath-based TB detector prototype called SMELTUB (Smart Measurement of Early Lung Tuberculosis). SMELTUB utilizes Electronic Nose (eNose) technology, artificial intelligence (AI), and the Internet of Things (IoT) to detect volatile organic compounds (VOCs) from patients’ breath. This prototype is designed to provide a portable, fast, and accurate diagnostic solution. The development of SMELTUB involved three main stages: designing and functional testing of the prototype, collecting training datasets from 33 VOC samples of TB patients and healthy individuals, and implementing the prototype in primary healthcare facilities. Trial results demonstrated an accuracy of 79%, sensitivity of 78%, and specificity of 80%, indicating the potential of SMELTUB as an effective and efficient TB screening tool. With its real-time detection capability and IoT integration, SMELTUB aims to facilitate early TB detection and strengthen TB control efforts in Indonesia, aligning with the global target to eliminate TB by 2030.