Bawang merah merupakan salah satu jenis sayuran yang sangat dicari dan diusahakan oleh petani di Indonesia. Bawang merah adalah tanaman holtikultura yang sangat sensitif terhadap perubahan harga, yang dapat memengaruhi baik petani maupun konsumen. Penelitian ini bertujuan untuk melakukan perbandingan metode ARIMA outlier dengan metode Neural Network dalam meramalkan data harga bawang merah di Kota Samarinda

Land_Use Density Plot

Density plot of avg_pixct of Land_Average

Analisis komparasi antara model ARIMA dengan deteksi outlier dan Neural Network terhadap data produksi cabai besar di Jawa Timur periode Januari 2017 hingga Desember 2023

This is a table detailing the incorporated and unincorporated communities throughout Cameron County using data spanning from 2023; this represents the most up-to-date information available publicly, as far as I know. A Census-Designated Place (CDP), per the U.S. Census Bureau, is "...[a] statistical equivalent of [an] incorporated place and represent[s] unincorporated communities that do not have a legally defined boundary or an active, functioning governmental structure." Source: https://www.census.gov/programs-surveys/bas/information/cdp.html ... code: library(easypackages) libraries(c("readxl", "ggmap", "ggiraph", "ggforce", "ggcorrplot", "ggthemes", "ggsignif", "ggsflabel", "ggrepel", "ggpubr", "ggsci", "glue", "gt", "janitor", "maptools", "mapview", "magrittr", "plyr", "prettyunits", "progress", "progressr", "psych", "rgeos", "rio", "rms", "Hmisc", "robustbase", "rspat", "s2", "sfheaders", "sfweight", "snakecase", "smoothr", "sp", "spatial", "spatialEco", "spatstat", "spatstat.linnet", "spatstat.model", "rpart", "spatstat.explore", "nlme", "spatstat.random", "spatstat.geom", "spatstat.data", "spdep", "sf", "spData", "abind", "summarytools", "terra", "tidycensus", "tidylog", "tidyselect", "lubridate", "forcats", "stringr", "dplyr", "purrr", "readr", "tidyr", "tibble", "ggplot2", "tidyverse", "tigris", "tmap", "vctrs", "viridis", "viridisLite", "vroom", "waldo", "wk", "stats", "graphics", "grDevices", "utils", "datasets", "methods", "base", "haven", "foreign", "survey", "srvyr", "sitrep", "questionr", "srvyr", "stringr")) ##tigris package, pulls Census data TX_places<- places(state = "TX", cb = FALSE, year = 2023) cam_pop_tracts<- get_acs("tract", table = "B01001", state = "TX", county = "Cameron", year= 2022, survey = "acs5", geometry = TRUE) # colonias data available through the Texas OAG database via filtering # https://texasoag.maps.arcgis.com/apps/webappviewer/index.html?id=1bc9c4f7b1da47dd8fc535fbd17dc060 # the file I'm using comes from the Cameron County DOT though, I'm pretty sure, circa 05/2022 colonias_sf<- read_sf(dsn = "E:/COLONIAS/COLONIAS.shp") ## Step 1: (after loading everything in) make a "cookie cutter" to filter the places data cam_pop_tracts %>% st_as_sf(.) %>% st_union(.) -> cam_union ## Step 2: Cookie Cut! st_intersection(TX_places, cam_union) -> cam_places ## Step 3: Make the table of places cam_places %>% mutate(TYPE = str_remove(NAMELSAD, paste0(NAME)) %>% str_squish(.) %>% toupper(.)) %>% st_drop_geometry(.) %>% arrange(., desc(TYPE)) %>% gt(.) %>% cols_align("center") %>% opt_stylize("4")