Coronavirus Disease 19 (COVID-19) merupakan virus baru yang menyebabkan infeksi saluran pernapasan. Virus ini berasal dari hewan yang dapat menular pada manusia dengan percikan air liur. Menurut data epidemiologi rata-rata pasien terjangkit virus ini berusia 15-80 tahun. Virus ini memiliki masa inkubasi 2-14 hari yang mempunyai gejala awal yaitu deman tinggi, sesak nafas, batuk pilek. Indonesia memiliki 2 kasus pertama pada 2 maret 2020. Permasalahan yang diangkat dalam penelitian ini adalah bagaimana mengklasifikasi resiko terjangkit virus covid-19 dari gejala yang ditimbulkan. Tujuan penelitian ini untuk mengetahui tingkat resiko terjangkit virus covid-19 berdasarkan instrumen yang digunakan dari metode Knowledge Discovery in Database yang terdiri dari 5 tahapan yaitu selection, pre-processing, transformation, data mining, dan evaluation. Dataset yang digunakan peneliti diambil dari web resmi kaggle.com. Penelitian ini menggunakan 4 (empat) algoritma yaitu K-Nearest Neighbor (K-NN), Neural Network (NN), Random Forest (RF), dan Naive Bayes dengan bantuan tool rapidminer. Values dataset antara lain tingkat rendah 25,98%, tingkat sedang 54,33%, dan tingkat tinggi 19,69%. Nilai akurasi pada dataset dengan 127 data pasien terjangkit covid-19 menggunakan algoritma k-nearest neighbor memperoleh 57,89%, neural network memperoleh 73,68%, random forest memperoleh 68,42%, naive bayes memperoleh 65,38%. Pada penelitian ini algoritma klasifikasi Neural Network memberikan nilai akurasi yang tertinggi.
Earthquake Early Warning System (EEWS) is a warning system that provides information about the estimated S wave arrival time, which can cause significant and destructive seismic energy using the information carried by the P wave. Technological advances in analyzing data supported by big data, the interconnection between networks, and high-performance computing systems in the era of the 4.0 industrial revolution have posed challenges to process and analyze earthquake early warning using modern seismological techniques. Early identification of earthquake events is the key to time efficiency to accelerate the dissemination of information. Here, we implement deep learning for early detection and classification of the earthquake P wave and noise signals using raw historical data from 3 component BMKG single station (2014 -2020) in the subduction zone of West Sumatra. The feature selection of the waveform is only selected for earthquakes distance in the cluster close to the station centroid. Statistically, the results of training and testing show good and convergent performance. This result is a preliminary study of deep learning, which is targeted at the classification of earthquakes p wave and noise signals and its association to estimate early earthquake location using 3 component record channels.
This study investigates the speech act of promising found in the first five episodes of the TV series Gilmore Girls (2000). It categorizes utterances containing promises based on the directness strategies. The direct promising strategy is identified using the IFID of the speech act of promising, that is the performative verb promise, while the indirect promising strategy is identified and categorized into 10 types of indirect promising strategy proposed by Ariff and Mugableh (2013): pure promise, discourse conditional, tautological-like expression, body-part expression, self-aggrandizing expression, time expression, courtesy-like expression, swearing expression, adjacency pair, and false promise. The results show that the most commonly used strategy was the indirect promising strategy (94.3%) as the characters in the series tend to make promises casually by not using the performative verb promise. Then, pure promise strategy is the most frequently used type of indirect promising strategy (31.3%). In addition, there are two distinctive types of indirect promising strategies found in the TV series, i.e., hidden promise and sarcastic promise strategies. This finding suggests that there are many other ways to make promises besides using the performative verb, promise and the modal verb will since the context of the conversations sometimes indicates future acts that a speaker commits to doing.
Objective: This study aimed to test the hypothesis that Hibiscus sabdariffa Linn. Extract (HSE) would increase arcuate nucleus Lep-R, NPY, and white adipose tissue β3AR mRNA expression in DIO rats. This study also analyzed the potency of H. sabdariffa bioactive compounds as an activator of Lep-R and β3AR. Methods: Twenty-four male Sprague-Dawley rats were separated into four groups: Control (standard chow), DIO (HFD), DIO-Hib200 (HFD+HSE 200 mg/kg BW), and DIO-Hib400 (HFD+HSE400 mg/kg BW). HSE administration was administered orally for five weeks, once a day. Result: The administration of HSE significantly (P<0,05) increased the arcuate nucleus Lep-R expression, but not for the ARC NPY and WAT β3AR. The Lee index of DIO rats also significantly decrease (p<0,001 for a dose of 200 mg/kg BW and p<0,01 for a dose of 400 mg/kg BW) into the normal range (≤ 310). Among 39 bioactive compounds, 5-O-caffeoylshikimic acid has high free binding scores (-8,63) for Lep-R, and myricetin_3_arabinogalactoside has high free binding scores (-9,39) for β3AR. These binding predictions can activate Lep-R and β3AR. Conclusion: HSE increases leptin sensitivity and reduces obesity, and its bioactive compounds can activate the Lep-R and β3AR to regulate energy balance. HSE could be a potential therapeutic target for obesity.
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