Showing 2 articles found for "Extreme"

Implementation Method High-Low, Scatterplot, And Least Squares In Cost Analysis

Susan Grace V Nainggolan, Yolanda Naomi Octavia Br. Simangunsong, Ester Simamora, Putri Herlina Munte, Marly Patricia Sihombing, Maria Cristina Rumapea
Abstract: This study aims to examine the application of the High-Low method, Scatterplot, and Least Squares in cost analysis, particularly in separating fixed and variable cost behavior. The research uses a library research approach… ch by collecting and reviewing literature from textbooks, journals, and official publications related to cost estimation and cost behavior modeling. The findings indicate that the High-Low method is simple because it uses only the highest and lowest activity levels, but its accuracy is limited due to reliance on extreme data points. The Scatterplot method provides a visual representation of the relationship between cost and activity, helping identify patterns and potential outliers; however, its results may vary due to subjective judgment in determining the cost line. Meanwhile, the Least Squares method is considered the most accurate because it utilizes all available data to produce the best-fit regression line, although it requires more complex calculations and statistical understanding. Overall, the selection of the most suitable method depends on the objective of the analysis, data availability, and the level of accuracy required

Analysis of Extreme Weather Causes of Flooding in Manado North Sulawesi

Ni Luh Made Anik Evaria, Rafa Zafirah Istiqomah
Abstract: On January 27 and 28 2023, Manado City was hit by heavy rain which caused several areas to experience significant flooding. The high intensity of rain during that period reached extreme levels, resulting in the inundation… n of several areas and submerging hundreds of residents' houses in 22 villages or sub-districts and 7 sub-districts in Manado. In studying this event, this research investigates the factors that influence extreme weather in Manado, observing sea surface temperature data, streamlines, air observations, the Southern Oscillation Index (SOI), and the Indian Ocean Dipole (IOD). The analysis shows that the positive SOI and negative IOD phenomena significantly impact extreme weather in the region. Cumulonimbus (Cb) clouds drove the atmospheric conditions formed on January 27 and 28 2023 and air flows from the Indian Ocean which brought wet air masses into Indonesian territory. High sea surface temperatures, reaching 28-29°C, also play a role in the intensive growth of rain clouds. Based on atmospheric conditions seen from radiosonde observation data, the stability index value shows that unstable conditions trigger heavy rain which causes flooding