Lung sounds provide essential information about the health of the lungs and respiratory tract. They have unique and distinguishable patterns associated with the abnormalities in these organs. Many studies attempted to develop various methods to classify lung sounds automatically. Wavelet transform is one of the approaches widely utilized for physiological signal analysis. Commonly, wavelet in feature extraction is used to break down the lung sounds into several sub-bands before calculating some parameters. This study used five lung sound classes obtained from various sources. Furthermore, the wavelet analysis process was carried out using Discrete Wavelet Transform (DWT) and Wavelet Package Decomposition (WPD) analysis and entropy calculation as feature extraction. In the DWT process, the highest accuracy obtained was 97.98% using Permutation Entropy (PE), Renyi Entropy (RE), and Spectral Entropy (SEN). In WPD, the best accuracy achieved is 98.99 % when 8 sub-bands and RE are used. These results are relatively competitive compared with previous studies using the wavelet method with the same datasets.

IntroductionAs a disaster-prone country, hospital preparedness in dealing with disasters in Indonesia is essential. This research, therefore, focuses specifically on hospital preparedness for COVID-19 in Indonesia, which is important given the indication that the pandemic will last for the foreseeable future.MethodsDuring March to September 2022, a cross-sectional approach and a quantitative study was conducted in accordance with the research objective to assess hospital preparedness for the COVID-19 pandemic. This research shows the level of readiness based on the 12 components of the rapid hospital readiness checklist for COVID-19 published by the World Health Organization (WHO). Evaluators from 11 hospitals in four provinces in Indonesia (Capital Special Region of Jakarta, West Java, Special Region of Yogyakarta, and North Sumatra) filled out the form in the COVID-19 Hospital Preparedness Information system, which was developed to assess the level of hospital readiness.ResultsThe results show that hospitals in Capital Special Region of Jakarta and Special Region of Yogyakarta have adequate level (≥ 80%). Meanwhile, the readiness level of hospitals in West Java and North Sumatra varies from adequate level (≥ 80%), moderate level (50% – 79%), to not ready level (≤ 50%).ConclusionThe findings and the methods adopted in this research are valuable for policymakers and health professionals to have a holistic view of hospital preparedness for COVID-19 in Indonesia so that resources can be allocated more effectively to improve readiness.