Analisis Big Data UMKM Menggunakan Apache Hadoop-Hive dengan Metode Forecasting Time Series - Perteka, Putu Denanta Bayuguna; Gunantara, Nyoman; Swamardika, Ida Bagus Alit (Majalah Ilmiah Teknologi Elektro; Vol 23 No 2 (2024): (Juli - Desember) Majalah Ilmiah Teknologi Elektro, 2024)
MSMEs (Micro, Small, and Medium Enterprises) play a vital role in economic growth, creating jobs, and improving people's welfare. However, MSMEs often face challenges in managing sales data and performance analysis, resulting in many businesses closing down every year. The Covid-19 pandemic has worsened this situation, with many MSMEs experiencing a decline in revenue and product demand. In the past five years, the application of big data analysis has increased rapidly, helping with fast and accurate decision-making. This technology, especially the time series forecasting method, allows predictions of future values based on historical data, thus supporting MSMEs in increasing productivity and competitiveness. The industrial revolution 4.0 brings big data and blockchain-based business intelligence that accelerates the supply chain and trade. This helps the government in setting regulations and making specific decisions for MSMEs. Although there are challenges in building infrastructure and deploying this technology, the benefits are enormous, such as understanding community trends through sentiment analysis on social media, improving company image, planning businesses based on consumer behavior, and making fast and accurate decisions. Apache Hadoop and Hive are efficient tools for managing and analyzing big data. In this study, MSME data in Bali Province was analyzed using the simple moving average forecasting time series method. The results of this analysis are used as a basis for providing recommendations and improving MSME management, and are expected to be a reference for better decision making.