The Consumer Price Index (CPI) is an indicator that is often used to measure the inflation rate in an area, or can be interpreted as a comparison between the prices of a commodity package from a group of goods or services consumed by households over a certain period time. The spread of COVID-19 throughout the world affects the economy in Indonesia, especially Yogyakarta. Forecasting CPI data during the COVID-19 pandemic has the benefit of being an illustration of data collection in the CPI of D.I Yogyakarta Province in the predicted period. This is useful as a comparison with the original data at the time of data collection and publication, as well as a consideration in making policies and improving the economy. Researchers use the Double Exponential Smoothing (DES) method to predict the CPI of Yogyakarta D.I Province, which aims to determine the best forecasting model and forecasting results. This method is rarely used in research on CPI data forecasting in Yogyakarta. The data in this study are monthly data from March 2020 to August 2021. The highest CPI in Yogyakarta occurred in August 2021 at 107.21 or 107.2, while the lowest CPI in Yogyakarta occurred in April 2020 at 105.15 or 105.2. The average CPI in Yogyakarta per month is 106.1. The Mean Absolute Percentage Error (MAPE) value obtained from the DES method is 0.1308443%, so that the accuracy of the model is 99.869%. Forecasting with the DES method is quite well used in forecasting the CPI data of Yogyakarta in September 2020 - November 2021. The results of CPI forecasting in Yogyakarta using the DES method were 107.2602, 107.3104, and 107.3606 from September-November.

Adanya pandemi Covid’19 di Indonesia memberikan dampak besar ke berbagai sektor kehidupan. Hal ini menyebabkan pemerintah memberikan respon cepat untuk mengatasi kasus ini dengan pembuatan berbagai kebijakan, salah satunya adalah kebijakan baru mengenai vaksinasi yang diwajibkan untuk semua masyarakat di Indonesia. Kebijakan tersebut menimbulkan berbagai macam respon dari masyarakat yang kebanyakan disalurkan melalui media sosial. Adanya respon dari masyarakat tersebut dapat menjadi salah satu acuan untuk melakukan evaluasi terhadap pelaksanaan vaksinasi di Indonesia. Maka dari itu, dengan memanfaatkan data dari media sosial twitter, penelitian ini bertujuan untuk menganalisis respon masyarakat terhadap pelaksanaan vaksinasi dengan cara mengklasifikasikan respon tersebut ke dalam respon positif, negatif, dan netral. Hasil analisis diperoleh bahwa respon masyarakat selama 3 bulan, yaitu pada bulan Mei dan Juni masih terdapat respon netral sedangkan pada bulan Juli keseluruhan kata memiliki respon negatif seiring dengan kenaikan kasus Covid’19. Kata Kunci: covid’19, vaksinasi, pelaksanaan vaksin, kartu/sertifikat vaksin, sentimen analisis

Indonesia is a country with a large population. Based on the results of the 2020 census, Indonesia's population ranks fourth in the world. The Indonesian government has made a policy to reduce population growth, namely the Family Planning Program or Keluarga Berencana (KB). One of the areas that did not escape the target was the DIY. Based on BKKBN DIY data, there is a significant difference between the number of active KB participants and the number of couples of childbearing ages, the number of KB equipment and the number of KB health facilities that exist between sub-districts in Sleman Regency. Then the sub-district classification is carried out based on the 2020 KB data in Sleman Regency using the K-Medoids Clustering method. This study aims to see the sub-district grouping used as a reference by the government in increasing active KB participants in the community to overcome the population in Yogyakarta, primarily focusing on Sleman. The categories in each cluster, namely Cluster 1, which consists of 6 sub-districts, have a high level of KB active participants, couples of reproductive ages, KB equipment, and KB health facilities. Then Cluster 2, which consists of 6 sub-districts, has a medium level of KB active participants, couples of reproductive ages, KB equipment, and KB health facilities. While Cluster 3 consists of 5 sub-districts, where KB active participants, teams of reproductive age, KB equipment, and KB health facilities are low level.