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A solar flare occurs due to a magnetic field reconnection above the active region. The active region magnetic complexity observed in the magnetogram could be used as proxies for solar flare forecasting. It is also known that solar flares that occur from emerging active regions located near the solar disk eastern limb can still have an impact on the Earth. Therefore, magnetic observation of active regions in the solar farside is important to forecast east limb flares occurrences. This study utilizes the conditional Generative Adversarial Network (cGAN) model to generate Artificial Intelligence (AI) solar farside magnetogram. Our model was trained using the solar frontside observations dataset from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) 304 Angstrom as input images and SDO/Helioseismic and Magnetic Imager (HMI) magnetogram as output images. Our model generates solar farside magnetogram using solar farside observation from Solar Terrestrial Relations Observatory (STEREO)/Extreme Ultraviolet Imager (EUVI) 304 Angstrom. We also conducted validation on the similarity of our AI-generated magnetogram with the magnetogram observation from SDO/HMI using the Structural Similarity Index (SSIM) method. SSIM obtained an average similarity value of 0.61±0.06 for training data and 0.47±0.02 for validation data which contain active regions producing flares.

Ludendorff coronal flattening index of the Total Solar Eclipse (TSE) on March 9, 2016, was calculated at various distances in solar radius. As a result, we obtained the coronal flattening index ( = a + b) at a distance of 2 solar radii is 0.16. The 24 th solar cycle phase based on the 2016 TSE event obtained -0.64 which showed the corona is pre-minimum type. Resulted coronal flattening index value gives a predicted maximum amplitude of the monthly sunspot number (Wmax) for the 25 th solar cycle to be 70 ± 65. Therefore, the solar activity for 25 th solar cycle predicted to be lower than the current solar cycle, which has a maximum sunspot number value of 146 in

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Photospheric vector magnetic field data which have several Space-weather HMI Active Region Patches (SHARP) parameters are used to study active regions that produced major solar flares. SHARP parameter data obtained from the Helioseismic Magnetic Imager (HMI) instruments onboard Solar Dynamics Observatory (SDO) have a good spatial and temporal sampling. We consider three SHARP parameters with high F-scores, namely total unsigned vertical current, total photospheric magnetic free energy, and total unsigned current helicity as a useful predictor for major solar flares. In this paper, we present the data analysis procedure and sample results focusing only on major solar flares (M and X class flares). The preliminary results showed in some cases, one of SHARP parameters for the M-class flare almost has the same value as X-class flare in which ideally should be higher.