Emas adalah salah satu jenis investasi yang sering mengalami perubahan harga, umumnya setiap hari. Karena faktor fluktuasi inilah diperlukannya peramalan harga emas untuk membantu para investor dalam pengambilan keputusan. Namun selama pandemi Coronavirus Disease 2019 (Covid-19) ini
harga emas mengalami fluktuasi yang lebih ekstrim dibanding 4 tahun terakhir sehingga diperlukannya pendekatan metode peramalan dan analisis yang tepat dalam kasus ini. Metode peramalan yang dipilih adalah Double Exponential Smoothing. Selain itu, ditemukannya banyak sebaran missing values sehingga diperlukannya imputasi, maka dipilih metode Last Observation Carried Forward (LOCF) dan linear interpolation. Dalam penelitian ini dataset utama dibagi ke dalam 3 (jenis) dataset, yaitu; Pracovid-19 (sebelum terjadinya Covid-19,
digunakan hanya untuk visualisasi fluktuasi ekstrem dari pandemi Covid-19), Incovid-19 (selama terjadinya Covid-19 berdasarkan informasi tanggal kasus Covid-19 pertama di Indonesia), dan Kombinasi (gabungan dataset Pracovid-19
dan Incovid-19). Dalam peramalan, Incovid-19 memiliki MAPE lebih besar dibanding Pracovid-19 dan Kombinasi, namun hasil evaluasi menunjukkan peramalan Incovid-19 memiliki nilai MAPE terendah sehingga Incovid-19 dengan imputasi LOCF dipilih menjadi dataset dengan peramalan paling adaptif di
lapangan dan digunakan untuk meramalkan harga harian emas sampai rekam data paling akhir dari dataset utama

Gold is another kind of investment which often experiences price change, mostly everyday. Because of its price fluctuation, forecasting is needed to help investor in investment decision making. But during this Coronavirus Disease 2019 (Covid-19), gold price is fluctuating extremely than the past 4 years so better forecasting method approachment and analysis technique is needed due to this
case. Double Exponential Smoothing method is chosen to forecast this daily gold price. On the other hand, there are so many missing values spreading around the main dataset so imputation method is needed too, Last Observation Carried
Forward (LOCF) and linear interpolation are chosen for imputing the missing values. In this research, main dataset was splitted into 3 (three) datasets, which are Precovid-19 (before Covid-19, used only for visualizing the actual fluctuation condition during this pandemic), Incovid-19 (during Covid-19 based on the date where first Covid-19 case occured in Indonesia), and Combination (a binding dataset of Pracovid-19 and Incovid-19). Although Incovid-19’s MAPE value is higher than Pracovid-19 and Combination’s MAPE values, but in evaluation session showed that Incovid-19’s MAPE of forecast results has the lowest value rather than Combination’s MAPE of forecast results, so the conclusion of this research is Incovid-19 dataset with LOCF imputation is the most adaptive with the actual condition and it is used to forecast the daily gold price until the last period of the main dataset then.