library(sf) |> suppressPackageStartupMessages() par(mfrow = c(2,4)) par(mar = c(1,1,1.2,1)) # 1 p <- st_point(0:1) plot(p, pch = 16) title("point") box(col = 'grey') # 2 mp <- st_multipoint(rbind(c(1,1), c(2, 2), c(4, 1), c(2, 3), c(1,4))) plot(mp, pch = 16) title("multipoint") box(col = 'grey') # 3 ls <- st_linestring(rbind(c(1,1), c(5,5), c(5, 6), c(4, 6), c(3, 4), c(2, 3))) plot(ls, lwd = 2) title("linestring") box(col = 'grey') # 4 mls <- st_multilinestring(list( rbind(c(1,1), c(5,5), c(5, 6), c(4, 6), c(3, 4), c(2, 3)), rbind(c(3,0), c(4,1), c(2,1)))) plot(mls, lwd = 2) title("multilinestring") box(col = 'grey') # 5 polygon po <- st_polygon(list(rbind(c(2,1), c(3,1), c(5,2), c(6,3), c(5,3), c(4,4), c(3,4), c(1,3), c(2,1)), rbind(c(2,2), c(3,3), c(4,3), c(4,2), c(2,2)))) plot(po, border = 'black', col = '#ff8888', lwd = 2) title("polygon") box(col = 'grey') # 6 multipolygon mpo <- st_multipolygon(list( list(rbind(c(2,1), c(3,1), c(5,2), c(6,3), c(5,3), c(4,4), c(3,4), c(1,3), c(2,1)), rbind(c(2,2), c(3,3), c(4,3), c(4,2), c(2,2))), list(rbind(c(3,7), c(4,7), c(5,8), c(3,9), c(2,8), c(3,7))))) plot(mpo, border = 'black', col = '#ff8888', lwd = 2) title("multipolygon") box(col = 'grey') # 7 geometrycollection gc <- st_geometrycollection(list(po, ls + c(0,5), st_point(c(2,5)), st_point(c(5,4)))) plot(gc, border = 'black', col = '#ff6666', pch = 16, lwd = 2) title("geometrycollection") box(col = 'grey')

In this project, I analyze a dataset of pets to investigate which characteristics are associated with higher adoption likelihood. By identifying these patterns, this analysis can provide insights that may help improve adoption strategies in shelters.

Penelitian ini melakukan penerapan metode K-Means Clustering pada dataset perumahan di India untuk mengidentifikasi segmentasi pasar properti berdasarkan karakteristik rumah dan fasilitas yang tersedia. Analisis mencakup tahap preprocessing data, penentuan jumlah cluster optimal dengan metode Elbow, implementasi K-Means, serta visualisasi hasil menggunakan Principal Component Analysis (PCA).