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1) Program Studi Magister Ilmu Fisika, Fakultas MIPA, Universitas Brawijaya, Malang 2) Jurusan Fisika, Fakultas MIPA, Universitas Brawijaya, Malang Diterima 05 Agustus 2015, direvisi 31 Agustus 2015 ABSTRAKRadiasi gamma dapat menyebabkan terjadinya kerusakan sel karena interaksi antara radiasi dengan sel menghasilkan radikal bebas. Suatu senyawa yang dapat menetralkan dan menangkap radikal bebas disebut antioksidan. Penelitian ini bertujuan untuk menganalisis pengaruh radiasi gamma dan ekstrak temulawak (Curcuma xanthorrhiza) terhadap kadar SGPT hepar mencit. Penelitian ini menggunakan 80 ekor mencit jantan dan dibagi menjadi 3 kelompok, yaitu kontrol negatif (K-), radiasi non ekstrak (R-) dan ekstrak plus radiasi (R+). Pemberian ekstrak temulawak diberikan secara oral dengan dosis 1,4 gr/kg BB; 2,0 gr/kg BB; 2,6 gr/kg BB; 3,2 gr/kg BB dan 3,8 gr/kg BB, sedangkan paparan radiasi gamma diberikan selama 10 menit, 20 menit, 30 menit, 40 menit dan 50 menit. Pengukuran kadar SGPT pada serum mencit dilakukan dengan menggunakan alat blood analyzer. Hasil penelitian menunjukkan bahwa radikal bebas dapat merusak sel hepar dan memecah dinding sel, sehingga kadar SGPT dalam darah menjadi tinggi. Seiring dengan penambahan dosis ekstrak temulawak, maka kadar SGPT menjadi berkurang. ABSTRACT Gamma radiation can causes cells damage, because the interaction between radiations with cells canproduce free radical. The compound that can neutralize and catch free radical is called antioxidant. The objectives of this research were to analyze influence of gamma radiation and Curcuma xanthorrhiza extract to the SGPT level to the mice's liver. This research uses eighty mices and they were divided into three group, negative control (K-), radiation non extract (R-) and extract plus radiation (R+). The doses of treatment of Curcuma xanthorrhiza extract are 1,4 gr/kg body weight; 2,0 gr/kg body weight; 2,6 gr/kg body weight; 3,2 gr/kg body weight and 3,8 gr/kg body weight, while the exposure of gamma radiation are given during 10 minutes, 20 minutes, 30 minutes, 40 minutes and 50 minutes. The measuring of SGPT level in mice's serum is done by using blood analyzer. The results showed that free radical can damages liver cells and breaks cell wall, so SGPT level in blood to be high. In a row of increasing of Curcuma xanthorrhiza extract dose, then SGPT level to be low.

Bioelectric impedance spectroscopy (BIS) has been widely used to study the electrical properties of biological tissue based on the characteristics of the complex electrical impedance dispersions. One of the problems in using the BIS method is the length of time required for the data acquisition process and possibly data analysis as well. In this research, a compact and work rapidly BIS instrumentation system has been developed at a low cost. It is designed to work in the frequency range of 100 Hz to 100 kHz, which is generally used in the fields of biophysics and medical physics. The BIS instrumentation system is built using several integrated modules. The modules are an AC current source to produce a selectable injection current; a data acquisition system to measure voltage, current, and phase difference rapidly and simultaneously; and software to calculate and display measurement results in the form of Bode and Nyquist plots in real time. The developed BIS system has been validated using a simple RC circuit as the sample being tested. The average time needed in the process of data acquisition and analysis until the formation of impedance dispersion curves in the form of Bode and Nyquist plots, for 54 sample frequencies, is less than one minute. The system is able to identify R and C values of the sample with a maximum error of 1.5%. In addition, some simple application examples are also presented in this paper.

BACKGROUND: Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved. OBJECTIVE: To develop a new deep learning system of chest X-ray images and evaluate whether it can quickly and accurately detect pneumonia and COVID-19 patients. METHODS: The developed deep learning system (UBNet v3) uses three architectural hierarchies, namely first, to build an architecture containing 7 convolution layers and 3 ANN layers (UBNet v1) to classify between normal images and pneumonia images. Second, using 4 layers of convolution and 3 layers of ANN (UBNet v2) to classify between bacterial and viral pneumonia images. Third, using UBNet v1 to classify between pneumonia virus images and COVID-19 virus infected images. An open-source database with 9,250 chest X-ray images including 3,592 COVID-19 images were used in this study to train and test the developed deep learning models. RESULTS: CNN architecture with a hierarchical scheme developed in UBNet v3 using a simple architecture yielded following performance indices to detect chest X-ray images of COVID-19 patients namely, 99.6%accuracy, 99.7%precision, 99.7%sensitivity, 99.1%specificity, and F1 score of 99.74%. A desktop GUI-based monitoring and classification system supported by a simple CNN architecture can process each chest X-ray image to detect and classify COVID-19 image with an average time of 1.21 seconds. CONCLUSION: Using three hierarchical architectures in UBNet v3 improves system performance in classifying chest X-ray images of pneumonia and COVID-19 patients. A simple architecture also speeds up image processing time.