This study attempts to examine the short-term and long-term relationship among selected global anddomestic macroeconomic variables fromeach country (Fed rate, crude oil price, Dow Jones Index, interest rate, exchange rate and inflation) for Indonesia and Malaysia Islamic capital market (Jakarta Islamic Index (JII) and FTSE Bursa Malaysia Hijrah Shariah Index (FHSI). The methodology used in this study is vector error correction model (VECM) for the monthly data starting from January 2006 to December 2010. The result shows that in the long-term, all selectedmacroeconomic variables except Dow Jones Index variable have significantly affect in both Islamic stock market FHSI and JII, while in the short-term there is no any selected macroeconomic variables that significantly affect FHSI and only inflation, exchange rate and crude oil price variables seem to significantly affect JII. Keywords : Islamic Stock Market, Jakarta Islamic Index, FTSE Hijrah Shariah Index, VAR/VECMJEL Classification: E52, E44

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Objective Visual inspection of cervix after acetic acid application (VIA) has been considered an alternative to Pap smear in resource-limited settings, like Indonesia. However, VIA results mainly depend on examiner’s experience and with the lack of comprehensive training of healthcare workers, VIA accuracy keeps declining. We aimed to develop an artificial intelligence (AI)-based Android application that can automatically determine VIA results in real time and may be further developed as a health care support system in cervical cancer screening. Result A total of 199 women who underwent VIA test was studied. Images of cervix before and after VIA test were taken with smartphone, then evaluated and labelled by experienced oncologist as VIA positive or negative. Our AI model training pipeline consists of 3 steps: image pre-processing, feature extraction, and classifier development. Out of the 199 data, 134 were used as train-validation data and the remaining 65 data were used as test data. The trained AI model generated a sensitivity of 80%, specificity of 96.4%, accuracy of 93.8%, precision of 80%, and ROC/AUC of 0.85 (95% CI 0.66–1.0). The developed AI-based Android application may potentially aid cervical cancer screening, especially in low resource settings.

Channel selection is an improvement technique to optimize EEG-based BCI performance.In previous studies, many channel selection methods-mostly based on spatial information of signals-have been introduced. One of these channel selection techniques is the energy calculation method. In this paper, we introduce an energy optimization calculation method, called the energy extraction method. Energy extraction is an extension of the energy calculation method, and is divided into two steps. The first step is energy calculation and the second is energy selection. In the energy calculation step, l2-norm is used to calculate channel energy, while in the energy selection method we propose three techniques: "high value" (HV), "close to mean" (CM), and "automatic". All proposed framework schemes for energy extraction are applied in two types of datasets. Two classes of datasets i.e. motor movement (hand and foot movement) and motor imagery (imagination of left-and right-hand movement) were used. The system used a Common Spatial Pattern (CSP) method to extract EEG signal features and k-NN as a classification method to classify the signal features with k=3. Based on the test results, all schemes for the proposed energy extraction method yielded improved BCI performance of up to 58%. In summary, the energy extraction approach using the CM energy selection method was found to be the best channel selection technique.

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