IMPORT DATA BPS
data_bps <- read.csv2(
"C:/Users/Lenovo/Downloads/Dataset_BPS_Sakernas_2018_2023_Cleaned.csv",
stringsAsFactors = FALSE,
check.names = TRUE
)
head(data_bps)
names(data_bps)
## [1] "Tahun" "Sektor"
## [3] "Kode.Sektor" "Tidak.belum.pernah.sekolah"
## [5] "Tidak.belum.tamat.SD" "SD"
## [7] "SLTP" "SLTA.Umum.SMU"
## [9] "SLTA.Kejuruan.SMK" "Akademi.Diploma"
## [11] "Universitas" "Total"
IMPORT DATA KLHK
data_klhk <- read_excel(
"C:/Users/Lenovo/Downloads/SIPSN 2018 2023.xlsx"
)
head(data_klhk)
names(data_klhk)
## [1] "Tahun"
## [2] "Timbulan Sampah Nasional (Ton/Tahun)"
## [3] "Sampah Terkelola (%)"
## [4] "Sampah Tidak Terkelola / Unmanaged (%)"
## [5] "Volume Sampah Liar / Kebocoran Lingkungan (Ton/Tahun)"
RENAME DATA KLHK
data_klhk <- data_klhk %>%
rename(
Tahun = 1,
Timbulan_Sampah = 2,
Sampah_Terkelola = 3,
Sampah_Unmanaged = 4
)
head(data_klhk)
DATA ESDM
data_esdm <- data.frame(
Tahun = 2018:2023,
Total_Energi_Industri_BOE = c(
308101365,
363534776,
297942171,
286850949,
511714610,
556383954
),
Batu_Bara_Thousand_BOE = c(
100506,
167412,
113416,
87820,
299191,
316754
),
Biomassa_Thousand_BOE = c(
342,
555,
637,
1309,
4524,
20452
),
Share_Industri = c(
39.8,
40.5,
38.7,
39.9,
43.2,
45.6
),
Share_Batubara = c(
22.1,
23.4,
24.0,
24.7,
25.1,
25.95
),
Share_Biomassa = c(
0.5,
0.7,
0.9,
1.1,
1.4,
1.68
),
Potensi_RDF_BOE = c(
12000,
18000,
26000,
42000,
76000,
98000
)
)
data_esdm
ANALISIS SKILL GAP
skill_gap_sampah <- data_bps %>%
filter(Kode.Sektor == "Sektor E") %>%
mutate(
Pendidikan_Rendah =
Tidak.belum.pernah.sekolah +
Tidak.belum.tamat.SD +
SD +
SLTP,
Pendidikan_Tinggi =
SLTA.Umum.SMU +
SLTA.Kejuruan.SMK +
Akademi.Diploma +
Universitas,
Rasio_Skill_Gap =
(Pendidikan_Rendah / Total) * 100
) %>%
select(
Tahun,
Total_Pekerja_Sampah = Total,
Pendidikan_Rendah,
Pendidikan_Tinggi,
Rasio_Skill_Gap
)
skill_gap_sampah
ANALISIS POTENSI ENERGI SAMPAH
potensi_energi_sampah <- data_klhk %>%
mutate(
Potensi_Energi_BOE =
Timbulan_Sampah * 0.35
) %>%
select(
Tahun,
Timbulan_Sampah,
Sampah_Terkelola,
Sampah_Unmanaged,
Potensi_Energi_BOE
)
potensi_energi_sampah
INTEGRASI DATA
tabel_sinergi_final <- data_esdm %>%
inner_join(
potensi_energi_sampah,
by = "Tahun"
) %>%
inner_join(
skill_gap_sampah,
by = "Tahun"
) %>%
mutate(
Rasio_Substitusi_Batubara =
(
Potensi_Energi_BOE /
(Batu_Bara_Thousand_BOE * 1000)
) * 100
)
tabel_sinergi_final
UJI CHI-SQUARE
total_rendah <- sum(
skill_gap_sampah$Pendidikan_Rendah,
na.rm = TRUE
)
total_tinggi <- sum(
skill_gap_sampah$Pendidikan_Tinggi,
na.rm = TRUE
)
total_terkelola <- sum(
potensi_energi_sampah$Sampah_Terkelola,
na.rm = TRUE
)
total_unmanaged <- sum(
potensi_energi_sampah$Sampah_Unmanaged,
na.rm = TRUE
)
matriks_uji <- matrix(
c(
total_rendah,
total_tinggi,
total_terkelola,
total_unmanaged
),
nrow = 2
)
hasil_uji <- chisq.test(matriks_uji)
hasil_uji
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: matriks_uji
## X-squared = 9.9958e-21, df = 1, p-value = 1
VISUALISASI 1
TREN ENERGI INDUSTRI
ggplot(
data_esdm,
aes(
x = Tahun,
y = Total_Energi_Industri_BOE / 1000000
)
) +
geom_line(
color = "#1B5E20",
linewidth = 1.5
) +
geom_point(
color = "#2E7D32",
size = 4
) +
labs(
title = "Tren Konsumsi Energi Industri Indonesia",
x = "Tahun",
y = "Juta BOE"
) +
theme_minimal()
VISUALISASI 2
SHARE INDUSTRI NASIONAL
ggplot(
data_esdm,
aes(
x = factor(Tahun),
y = Share_Industri
)
) +
geom_col(
fill = "#2E7D32",
width = 0.7
) +
geom_text(
aes(label = paste0(Share_Industri, "%")),
vjust = -0.5,
fontface = "bold"
) +
labs(
title = "Kontribusi Energi Industri terhadap Nasional",
x = "Tahun",
y = "Persentase (%)"
) +
theme_minimal()
VISUALISASI 3
BATUBARA VS BIOMASSA
data_bauran <- data_esdm %>%
select(
Tahun,
Batu_Bara_Thousand_BOE,
Biomassa_Thousand_BOE
) %>%
pivot_longer(
cols = -Tahun,
names_to = "Jenis_Energi",
values_to = "Jumlah"
)
ggplot(
data_bauran,
aes(
x = factor(Tahun),
y = Jumlah,
fill = Jenis_Energi
)
) +
geom_col(
position = "dodge"
) +
scale_fill_manual(
values = c(
"#1B5E20",
"#81C784"
)
) +
labs(
title = "Perbandingan Batubara dan Biomassa",
x = "Tahun",
y = "Ribu BOE"
) +
theme_minimal()
VISUALISASI 4
GAP ENERGI FOSIL VS HIJAU
data_gap <- data_esdm %>%
select(
Tahun,
Share_Batubara,
Share_Biomassa
) %>%
pivot_longer(
cols = -Tahun,
names_to = "Jenis",
values_to = "Persentase"
)
ggplot(
data_gap,
aes(
x = factor(Tahun),
y = Persentase,
fill = Jenis
)
) +
geom_col(
position = "dodge"
) +
scale_fill_manual(
values = c(
"#1B5E20",
"#66BB6A"
)
) +
labs(
title = "Kesenjangan Energi Fosil vs Energi Hijau",
x = "Tahun",
y = "Persentase (%)"
) +
theme_minimal()
VISUALISASI 5
POTENSI ENERGI SAMPAH
ggplot(
tabel_sinergi_final,
aes(
x = factor(Tahun),
y = Potensi_Energi_BOE / 1000000
)
) +
geom_col(
fill = "#388E3C",
width = 0.7
) +
geom_text(
aes(
label = round(
Potensi_Energi_BOE / 1000000,
2
)
),
vjust = -0.5,
fontface = "bold"
) +
labs(
title = "Potensi Energi dari Sampah Nasional",
x = "Tahun",
y = "Juta BOE"
) +
theme_minimal()
VISUALISASI 6
SKILL GAP TENAGA KERJA
ggplot(
skill_gap_sampah,
aes(
x = factor(Tahun),
y = Rasio_Skill_Gap
)
) +
geom_col(
fill = "#43A047",
width = 0.7
) +
geom_text(
aes(
label = paste0(round(Rasio_Skill_Gap,1), "%")
),
vjust = -0.5,
fontface = "bold"
) +
labs(
title = "Skill Gap Tenaga Kerja Sektor Sampah",
x = "Tahun",
y = "Persentase (%)"
) +
theme_minimal()
VISUALISASI 7
PARADOKS TRANSISI HIJAU
ggplot(
tabel_sinergi_final,
aes(x = Tahun)
) +
geom_line(
aes(
y = Rasio_Skill_Gap,
color = "Skill Gap"
),
linewidth = 1.5
) +
geom_point(
aes(
y = Rasio_Skill_Gap,
color = "Skill Gap"
),
size = 3
) +
geom_line(
aes(
y = Rasio_Substitusi_Batubara,
color = "Substitusi Energi"
),
linewidth = 1.5,
linetype = "dashed"
) +
geom_point(
aes(
y = Rasio_Substitusi_Batubara,
color = "Substitusi Energi"
),
size = 3
) +
scale_color_manual(
values = c(
"Skill Gap" = "#1B5E20",
"Substitusi Energi" = "#81C784"
)
) +
labs(
title = "Paradoks Transisi Hijau",
x = "Tahun",
y = "Persentase (%)",
color = "Indikator"
) +
theme_minimal()
VISUALISASI 8
POTENSI RDF / WASTE TO ENERGY
ggplot(
data_esdm,
aes(
x = Tahun,
y = Potensi_RDF_BOE
)
) +
geom_line(
color = "#2E7D32",
linewidth = 1.5
) +
geom_point(
color = "#1B5E20",
size = 4
) +
labs(
title = "Potensi Waste-to-Energy / RDF",
x = "Tahun",
y = "BOE"
) +
theme_minimal()
