@thesis{thesis,
		author={Kusuma  Dine Tiara and Manullang  Sergius Sarmose Manggara Putra and Siregar  Riki Ruli Affandi},
		title ={Perbandingan Akurasi Double Exponential Smoothing Berimputasi LOCF dan Linear Interpolation dalam Peramalan Harga Harian Emas},
		year={2020},
		url={http://156.67.221.169/4105/},
		abstract={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.}
		}