library(mongolite)Warning: package 'mongolite' was built under R version 4.4.3Agregasi dan Visualisasi Profil Perusahaan Tercatat
Project UAS MDS Kelompok 6:
M0501241031 Jefita Resti Sari
M0501241038 Sely Fitriatun
M0501241048 Claudian Tikulimbong Tangdiloban
M0501241063 Baiq Nina Febriati
Koneksi Data MongoDB
# KONEKSI
conn <- mongo(
collection = "Profil Perusahaan Tercatat",
db = "mds",
url = "mongodb+srv://jefitaa:Jefitars02!@cluster0.jbglyd0.mongodb.net/")Agregasi Data Profil Perusahaan Tercatat
Pemilik Saham 1
query_pemilik <- '[
{
"$group": {
"_id": "$Pemilik Saham 1",
"jumlah_perusahaan": { "$sum": 1 }
}
},
{
"$sort": { "jumlah_perusahaan": -1 }
}
]'
hasil_pemilik <- conn$aggregate(query_pemilik)
print(head(hasil_pemilik)) _id jumlah_perusahaan
1 Masyarakat Non Warkat 120
2 <NA> 55
3 Masyarakat 27
4 PT Biro Klasifikasi Indonesia 4
5 PT Semen Indonesia (Persero) Tbk 2
6 PT Barito Pacific Tbk 2Jumlah Emiten per Corporate Secretary
agg_corpsec <- conn$aggregate('[
{
"$group": {
"_id": "$Corporate Secretary",
"jumlah_emiten": { "$sum": 1 }
}
},
{
"$sort": { "jumlah_emiten": -1 }
}
]')
print(head(agg_corpsec)) _id jumlah_emiten
1 Yudhi Surjadjaja 2
2 Satrio Boediarto 1
3 Agianita Julinda 1
4 Febrina Kenya Savitri 1
5 Chadafy Maraden Sibarani 1
6 Asep Mulyana 1Jumlah Emiten berdasarkan Tahun IPO (Listing Date)
agg_tahun_listing <- conn$aggregate('[
{
"$project": {
"tahun": { "$substr": [ "$Listing Date", 6, 4 ] }
}
},
{
"$group": {
"_id": "$tahun",
"jumlah_emiten": { "$sum": 1 }
}
},
{
"$sort": { "_id": 1 }
}
]')
print(agg_tahun_listing) _id jumlah_emiten
1 1
2 1977 1
3 1979 1
4 1980 2
5 1981 2
6 1982 4
7 1983 2
8 1984 2
9 1989 24
10 1990 51
11 1991 12
12 1992 10
13 1993 12
14 1994 30
15 1995 15
16 1996 10
17 1997 22
18 1998 3
19 1999 4
20 2000 15
21 2001 22
22 2002 19
23 2003 8
24 2004 12
25 2005 8
26 2006 11
27 2007 21
28 2008 16
29 2009 11
30 2010 20
31 2011 25
32 2012 23
33 2013 28
34 2014 22
35 2015 17
36 2016 15
37 2017 36
38 2018 57
39 2019 55
40 2020 49
41 2021 54
42 2022 55
43 2023 75
44 2024 40
45 2025 13Jumlah Emitmen berdasarkan Komisaris Utama
agg_komut <- conn$aggregate('[
{
"$group": {
"_id": "$Komisaris Utama",
"jumlah_emiten": { "$sum": 1 }
}
},
{
"$sort": { "jumlah_emiten": -1 }
}
]')
print(head(agg_komut)) _id jumlah_emiten
1 <NA> 16
2 Hermanto Tanoko 5
3 Edwin Soeryadjaya 5
4 Franky Oesman Widjaja 3
5 Muktar Widjaja 3
6 Fendi Santoso 2Lokasi (Alamat)
query <- '[
{
"$addFields": {
"kota": {
"$switch": {
"branches": [
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "banda aceh", "options": "i" } }, "then": "Banda Aceh" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "medan", "options": "i" } }, "then": "Medan" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "padang", "options": "i" } }, "then": "Padang" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "pekanbaru", "options": "i" } }, "then": "Pekanbaru" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "tanjung pinang", "options": "i" } }, "then": "Tanjung Pinang" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "jambi", "options": "i" } }, "then": "Jambi" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "bengkulu", "options": "i" } }, "then": "Bengkulu" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "palembang", "options": "i" } }, "then": "Palembang" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "pangkalpinang", "options": "i" } }, "then": "Pangkalpinang" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "bandar lampung", "options": "i" } }, "then": "Bandar Lampung" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "jakarta", "options": "i" } }, "then": "Jakarta" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "bandung", "options": "i" } }, "then": "Bandung" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "serang", "options": "i" } }, "then": "Serang" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "semarang", "options": "i" } }, "then": "Semarang" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "yogyakarta", "options": "i" } }, "then": "Yogyakarta" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "surabaya", "options": "i" } }, "then": "Surabaya" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "denpasar", "options": "i" } }, "then": "Denpasar" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "mataram", "options": "i" } }, "then": "Mataram" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "kupang", "options": "i" } }, "then": "Kupang" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "pontianak", "options": "i" } }, "then": "Pontianak" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "palangka raya", "options": "i" } }, "then": "Palangka Raya" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "banjarmasin", "options": "i" } }, "then": "Banjarmasin" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "samarinda", "options": "i" } }, "then": "Samarinda" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "tanjung selor", "options": "i" } }, "then": "Tanjung Selor" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "manado", "options": "i" } }, "then": "Manado" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "palu", "options": "i" } }, "then": "Palu" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "makassar", "options": "i" } }, "then": "Makassar" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "kendari", "options": "i" } }, "then": "Kendari" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "gorontalo", "options": "i" } }, "then": "Gorontalo" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "mamuju", "options": "i" } }, "then": "Mamuju" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "ambon", "options": "i" } }, "then": "Ambon" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "sofifi", "options": "i" } }, "then": "Sofifi" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "jayapura", "options": "i" } }, "then": "Jayapura" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "manokwari", "options": "i" } }, "then": "Manokwari" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "wamena", "options": "i" } }, "then": "Wamena" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "merauke", "options": "i" } }, "then": "Merauke" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "nabire", "options": "i" } }, "then": "Nabire" },
{ "case": { "$regexMatch": { "input": "$Alamat", "regex": "sorong", "options": "i" } }, "then": "Sorong" }
],
"default": "Lainnya"
}
}
}
},
{
"$group": {
"_id": "$kota",
"jumlah_perusahaan": { "$sum": 1 }
}
},
{
"$sort": { "jumlah_perusahaan": -1 }
}
]'
hasil_agregasi <- conn$aggregate(query)
print(hasil_agregasi) _id jumlah_perusahaan
1 Jakarta 646
2 Lainnya 202
3 Surabaya 31
4 Medan 12
5 Bandung 10
6 Semarang 9
7 Yogyakarta 7
8 Serang 7
9 Palembang 4
10 Pekanbaru 2
11 Denpasar 1
12 Makassar 1
13 Padang 1
14 Samarinda 1
15 Bandar Lampung 1Jumlah Perusahaan per Sektor
agg_sektor <- conn$aggregate('[
{
"$group": {
"_id": "$Sektor",
"jumlah_perusahaan": { "$sum": 1 }
}
},
{
"$sort": { "jumlah_perusahaan": -1 }
}
]')
print(agg_sektor) _id jumlah_perusahaan
1 NA 935Direktur Utama
query_dirut <- '[
{
"$group": {
"_id": "$Direktur Utama",
"jumlah_perusahaan": { "$sum": 1 }
}
},
{
"$sort": { "jumlah_perusahaan": -1 }
}
]'
hasil_dirut <- conn$aggregate(query_dirut)
print(head(hasil_dirut)) _id jumlah_perusahaan
1 <NA> 8
2 Ardi Kusuma 2
3 Hary Tanoesoedibjo 2
4 Michael 2
5 Firdaus 2
6 Edward Halim 2Jumlah Jabatan (Komisaris + Direktur + Komite)
query_jumlah <- '[
{
"$project": {
"Nama": 1,
"jumlah_komisaris": {
"$size": {
"$filter": {
"input": { "$objectToArray": "$$ROOT" },
"as": "item",
"cond": { "$regexMatch": { "input": "$$item.k", "regex": "Komisaris", "options": "i" } }
}
}
},
"jumlah_direktur": {
"$size": {
"$filter": {
"input": { "$objectToArray": "$$ROOT" },
"as": "item",
"cond": { "$regexMatch": { "input": "$$item.k", "regex": "Direktur",
"options": "i" } }
}
}
},
"jumlah_komite": {
"$size": {
"$filter": {
"input": { "$objectToArray": "$$ROOT" },
"as": "item",
"cond": { "$regexMatch": { "input": "$$item.k", "regex": "Komite",
"options": "i" } }
}
}
}
}
},
{
"$project": { "_id": 0 }
}
]'
hasil_jumlah <- conn$aggregate(query_jumlah)
print(head(hasil_jumlah)) Nama jumlah_komisaris jumlah_direktur
1 PT Adaro Andalan Indonesia Tbk 2 4
2 Astra Agro Lestari Tbk 4 7
3 Mahaka Media Tbk 3 2
4 Asuransi Bina Dana Arta Tbk 4 4
5 ABM Investama Tbk 4 4
6 PT Aspirasi Hidup Indonesia Tbk 5 5
jumlah_komite
1 3
2 3
3 3
4 3
5 3
6 3Visualisasi Data
library(ggplot2)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(treemapify)Warning: package 'treemapify' was built under R version 4.4.3library(ggrepel)#Top 7 Pemilik Saham dengan Jumlah Emiten Terbanyak
library(ggplot2)
library(dplyr)
# Jika hasil dari Mongo masih: "_id" dan "jumlah_perusahaan"
df <- hasil_pemilik %>%
rename(Pemilik = `_id`, Jumlah = jumlah_perusahaan) %>%
arrange(desc(Jumlah)) %>%
slice(1:7) # Tampilkan hanya 7 teratas
# Plot horizontal bar
ggplot(df, aes(x = Jumlah, y = reorder(Pemilik, Jumlah))) +
geom_col(fill = "steelblue") +
labs(
title = "Top 7 Pemilik Saham dengan Jumlah Emiten Terbanyak",
x = "Jumlah Emiten", y = "Pemilik Saham"
) +
theme_minimal(base_size = 12) +
theme(axis.text.y = element_text(size = 10))
#Jumlah Emiten berdasarkan Tahun IPO
library(ggplot2)
library(dplyr)
# Rename kolom agar lebih mudah digunakan
df_tahun <- agg_tahun_listing %>%
rename(Tahun = `_id`, Jumlah = jumlah_emiten) %>%
mutate(Tahun = as.numeric(Tahun)) # ubah ke numerik untuk sumbu X
# Line plot untuk tren emiten
ggplot(df_tahun, aes(x = Tahun, y = Jumlah)) +
geom_line(color = "steelblue", size = 1) +
geom_point(color = "darkblue", size = 2) +
labs(title = "Tren Jumlah Emiten berdasarkan Tahun IPO",
x = "Tahun", y = "Jumlah Emiten") +
theme_minimal(base_size = 12)Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_line()`).Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).
#Top 10 Komisaris Utama dengan Emiten Terbanyak
library(dplyr)
df_komut <- agg_komut %>%
rename(Komisaris = `_id`, Jumlah = jumlah_emiten) %>%
mutate(Komisaris = ifelse(is.na(Komisaris) | Komisaris == "NA", "Tidak Diketahui", Komisaris)) %>%
slice_max(order_by = Jumlah, n = 10, with_ties = FALSE) # fix: ambil top 10 saja
ggplot(df_komut, aes(x = Jumlah, y = reorder(Komisaris, Jumlah))) +
geom_col(fill = "darkorange") +
labs(
title = "Top 10 Komisaris Utama dengan Emiten Terbanyak",
x = "Jumlah Emiten", y = "Komisaris Utama"
) +
theme_minimal(base_size = 12)
library(ggplot2)
library(dplyr)
df_kota <- hasil_agregasi %>%
rename(Kota = `_id`, Jumlah = jumlah_perusahaan) %>%
arrange(desc(Jumlah))
ggplot(df_kota, aes(x = Jumlah, y = reorder(Kota, Jumlah))) +
geom_col(fill = "green") +
labs(
title = "Jumlah Perusahaan per Kota",
x = "Jumlah Perusahaan",
y = "Kota"
) +
theme_minimal(base_size = 12)
#Total Jabatan Setiap Perusahaan
library(ggplot2)
library(tidyr)
library(dplyr)
# Data reshaping ke format long agar bisa plot bar side-by-side
df_long <- hasil_jumlah %>%
pivot_longer(cols = c(jumlah_komisaris, jumlah_direktur, jumlah_komite),
names_to = "Jabatan",
values_to = "Jumlah")
# Ambil Top 10 perusahaan berdasarkan total jabatan (komisaris + direktur)
df_top10 <- df_long %>%
group_by(Nama) %>%
summarise(Total = sum(Jumlah)) %>%
arrange(desc(Total)) %>%
slice(1:10) %>%
inner_join(df_long, by = "Nama") %>%
mutate(Nama = factor(Nama, levels = unique(Nama)))
# Plot
ggplot(df_top10, aes(x = Nama, y = Jumlah, fill = Jabatan)) +
geom_bar(stat = "identity", position = position_dodge()) +
coord_flip() +
labs(title = "Top 10 Jumlah Komisaris, Direktur dan Komite per Perusahaan",
x = "Nama Perusahaan",
y = "Jumlah") +
scale_fill_manual(values = c("jumlah_komisaris" = "orange", "jumlah_direktur" = "steelblue", "jumlah_komite" = "maroon"),
labels = c("Komisaris", "Direktur", "Komite")) +
theme_minimal(base_size = 12)
#Lokasi Perusahaan
library(dplyr)
library(tibble)
# Ambil data hasil agregasi Mongo
df_kota <- hasil_agregasi %>%
rename(Kota = `_id`, Jumlah = jumlah_perusahaan)
# Tambahkan koordinat untuk lebih banyak kota
koordinat_kota <- tibble(
Kota = c(
"Jakarta", "Surabaya", "Bandung", "Medan", "Semarang",
"Yogyakarta", "Serang", "Palembang", "Pekanbaru",
"Makassar", "Samarinda", "Lainnya"
),
lat = c(
-6.2088, -7.2504, -6.9175, 3.5952, -6.9667,
-7.8014, -6.1214, -2.9761, 0.5071,
-5.1477, -0.5022, NA
),
lon = c(
106.8456, 112.7688, 107.6191, 98.6722, 110.4381,
110.3647, 106.1572, 104.7754, 101.4478,
119.4327, 117.1537, NA
)
)
# Gabungkan koordinat ke data jumlah perusahaan
df_map <- left_join(df_kota, koordinat_kota, by = "Kota") %>%
filter(!is.na(lat))
library(dplyr)
library(tibble)
# Ambil data hasil agregasi Mongo
df_kota <- hasil_agregasi %>%
rename(Kota = `_id`, Jumlah = jumlah_perusahaan)
# Tambahkan koordinat untuk lebih banyak kota
koordinat_kota <- tibble(
Kota = c(
"Jakarta", "Surabaya", "Bandung", "Medan", "Semarang",
"Yogyakarta", "Serang", "Palembang", "Pekanbaru",
"Makassar", "Samarinda", "Lainnya"
),
lat = c(
-6.2088, -7.2504, -6.9175, 3.5952, -6.9667,
-7.8014, -6.1214, -2.9761, 0.5071,
-5.1477, -0.5022, NA
),
lon = c(
106.8456, 112.7688, 107.6191, 98.6722, 110.4381,
110.3647, 106.1572, 104.7754, 101.4478,
119.4327, 117.1537, NA
)
)
# Gabungkan koordinat ke data jumlah perusahaan
df_map <- left_join(df_kota, koordinat_kota, by = "Kota") %>%
filter(!is.na(lat))
library(leaflet)
library(leaflet)
# Buat palet warna unik berdasarkan nama kota
pal_kota <- colorFactor(palette = "Set1", domain = df_map$Kota)
leaflet(df_map) %>%
addTiles() %>%
addCircleMarkers(
~lon, ~lat,
radius = ~sqrt(Jumlah), # Ukuran berdasarkan jumlah perusahaan
color = ~pal_kota(Kota), # Warna berdasarkan kota
label = ~paste(Kota, ":", Jumlah, "perusahaan"),
fillOpacity = 0.8, stroke = TRUE
) %>%
addLegend("bottomright", pal = pal_kota, values = ~Kota,
title = "Kota", opacity = 1)Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette Set1 is 9
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette Set1 is 9
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette Set1 is 9
Returning the palette you asked for with that many colors