This study aims to classify COVID-19 patients based on the results of their hematology tests. Hematology test results have been shown to be useful in identifying the severity and risk of COVID-19 patients. Specifically, this study focuses on classifying COVID-19 patients based on their vital status, namely Deceased and Alive. The dataset used in this study contains four variables: white blood cells (WBC), neutrophils (NEU), lymphocytes (LYM), and Neutrophil Lymphocyte Ratio (NLR). Logistic Regression algorithm was used to solve the problem, and hyperparameter optimization was implemented to obtain the best model performance. The objective of this study was to build the best parameter in classifying the patients’ vital status. The proposed model achieved an accuracy score of 78%, which is the best performance among the tested models. The results of this study provide a key component for decision making in hospitals, as it provides a way to quickly and accurately identify the vital status of COVID-19 patients. This study has important implications for managing the COVID-19 pandemic and should be of interest to researchers and practitioners in the field.
COVID-19 has become a threat to the world because it has spread throughout the world. The fight against this pandemic is becoming an unavoidable reality for many countries. The government has set policies on various transmission prevention efforts. One of these efforts is for everyone to wear masks in order to break the transmission chain. With such conditions, the government must continue to monitor so that people can apply the appeal in their daily lives when participating in outdoor activities. The present time involves new problems in so many fields of information technology research, especially those related to artificial intelligence. The purpose of this study is to discuss the classification of face image detection in people who wear masks and do not wear masks. designed using the Convolutional Neural Network (CNN) model and built using the transfer learning method with the DenseNet169 model. The model used is also combined with the DenseNet169 transfer learning method and the fully connected layer model architecture, so as to optimize the performance test in the evaluation. These models were trained under similar conditions and evaluated on benchmarks with the same training and validation images. The result of this research is to get an accuracy value of 96% by combining the two datasets. This dataset is the same as previous research; the number of datasets is 8929 images
Mengamati perubahan yang dialami tubuh selama menstruasi akan memberikan petunjuk penting tentang status kesuburan. Salah satu metode yang dianggap paling efektif dan mudah untuk diimplementasikan adalah dengan pemantauan suhu basal tubuh. Pada dasarnya, suhu basal tubuh (BBT) adalah kondisi dimana suhu berada pada level terendah. Masuknya masa subur ditandai oleh sedikit penurunan suhu, diikuti oleh kenaikan secara tiba-tiba setidaknya 0,2 °C (0,4 °F) selama 48 jam berikutnya. Tentunya siklus masa subur tesebut termasuk dalam data deret waktu dikarenakan datanya saling berhubungan satu sama lain yang berurutan, disusun dari satu objek yang terdiri dari beberapa waktu periode. Salah satu metode prediksi data deret waktu adalah state-space. Teknik yang digunakan di dalam state-space adalah memisah waktu masa lalu dengan masa sekarang. Oleh karena itu pada penelitian di prediksi masa subur wanita berbasis suhu basal tubuh menggunakan pemodelan state-space, hasilnya sangat baik dengan akurasi rata-rata kesalahan prediksi dibawah 5%
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