Abstract: Time-domain observations with ground-based telescopes are often affected by the day-night cycle and weather conditions, leading to data gaps and a relatively low duty cycle (typically around 0.30), which significantly impacts time-domain astronomical studies. To compare the performance of frequency-domain and time-domain analysis methods in handling time-domain data with gaps and their applicability in asteroseismology, the Lomb-Scargle algorithm and the Inpainting interpolation method were employed as frequency-domain approaches, while the Gaussian Process (GP) method was used as a time-domain approach. These methods were applied to simulate light curves exhibiting solar-like oscillations with duty cycles ranging from 0.20 to 0.50. The results indicate that the Gaussian Process method outperforms both the Lomb-Scargle and Inpainting methods in terms of accuracy and stability in recovering the true values. The Inpainting method, in particular, tends to introduce significant false signals when applied to low-duty-cycle data, leading to potential measurement distortions. Therefore, the Gaussian Process method is the preferred choice for analyzing low-duty-cycle data from ground-based telescopes, followed by the Lomb-Scargle method, while the Inpainting method is not recommended.