Pengguna melakukan pembuatan database dan pengimputan data yang dibutuhkan untuk mengelola dan menganalisis perilaku investor. Mulai dari data diri hingga transaksi yang dilakukan para investor.
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(DT)
## Warning: package 'DT' was built under R version 4.5.2
library(ggplot2)
library(tidyr)
Investor <- data.frame(
ID_Investor = c("INV_0001", "INV_0002", "INV_0003", "INV_0004", "INV_0005", "INV_0006", "INV_0007"),
Nama = c("Budi Santoso", "Ani Wijaya", "Citra Dewi", "Firman Teno", "Fitri Sinta", "Syifa Nurrisya", "Aslan Dikawan"),
Email = c("budi.santoso@gmail.com", "ani.wijaya@gmail.com", "citra.dewi@gmail.com", "firman@gmail.com", "fitri@gmail.com", "syifa@gmail.com", "Aslan@gmail.com"),
Tanggal_Registrasi = as.Date(c("2023-01-15", "2023-02-20", "2023-03-10", "2023-07-29", "2023-11-03", "2023-01-21", "2023-08-28")),
Risk_Profile = factor(c("Moderat", "Konservatif", "Agresif", "Agresif", "Moderat", "Konservatif", "Agresif"),
levels = c("Konservatif", "Moderat", "Agresif")))
Investasi <- data.frame(
ID_Investasi = c('V001', 'V002', 'V003', 'V004', 'V005', 'V006', 'V007', 'V008', 'V009', 'V010', 'V011', 'V012', 'V013', 'V014', 'V015'),
ID_Investor = c('INV_0001', 'INV_0001', 'INV_0002', 'INV_0003', 'INV_0003', 'INV_0004', 'INV_0005', 'INV_0006', 'INV_0006', 'INV_0007', 'INV_0002', 'INV_0004', 'INV_0005', 'INV_0007', 'INV_0007'),
Jenis_Investasi = factor(c('Saham', 'Obligasi', 'Properti', 'Crypto', 'Saham', 'Obligasi', 'Crypto', 'Properti', 'Saham', 'Obligasi', 'Crypto', 'Saham', 'Properti', 'Saham', 'Crypto'),
levels = c('Saham', 'Obligasi', 'Properti', 'Crypto')),
Nama_Investasi = c('PT Telkom Indonesia (TLKM)', 'Obligasi Negara Seri FR0075', 'Apartemen Green Park Jakarta', 'Bitcoin (BTC)', 'PT Bank Central Asia (BBCA)',
'Obligasi Korporasi PT Astra', 'Ethereum (ETH)', 'Ruko BSD City', 'PT Unilever Indonesia (UNVR)', 'Obligasi Negara Seri FR0080',
'Cardano (ADA)', 'PT Bank Rakyat Indonesia (BBRI)', 'Tanah Kavling Bandung', 'PT GoTo Gojek Tokopedia (GOTO)', 'Bitcoin (BTC)'),
Jumlah_Awal = c(50000000.00, 75000000.00, 350000000.00, 12000000.00, 45000000.00, 60000000.00, 8500000.00, 275000000.00, 30000000.00, 90000000.00, 15000000.00, 40000000.00, 180000000.00, 35000000.00, 10000000.00)
)
Portofolio <- data.frame(
ID_Portofolio = c('P001', 'P002', 'P003', 'P004', 'P005', 'P006', 'P007', 'P008', 'P009', 'P010', 'P011', 'P012'),
ID_Investor = c('INV_0001', 'INV_0001', 'INV_0002', 'INV_0003', 'INV_0004', 'INV_0005', 'INV_0006', 'INV_0007', 'INV_0002', 'INV_0003', 'INV_0005', 'INV_0007'),
Nama_Portofolio = c('Portofolio Konservatif', 'Portofolio Pertumbuhan', 'Portofolio Pendapatan Tetap', 'Portofolio Agresif', 'Portofolio Balanced',
'Portofolio Saham Blue Chip', 'Portofolio Reksadana', 'Portofolio Internasional', 'Portofolio Emas', 'Portofolio Properti',
'Portofolio Teknologi', 'Portofolio Campuran'),
Tanggal_Dibuat = as.Date(c('2023-01-15', '2023-03-22', '2023-02-10', '2023-04-05', '2023-01-30', '2023-05-12', '2023-06-18', '2023-03-01', '2023-05-25', '2023-02-14', '2023-04-30', '2023-01-10'))
)
Transaksi <- data.frame(
ID_Transaksi = c('TRX001', 'TRX002', 'TRX003', 'TRX004', 'TRX005', 'TRX006', 'TRX007', 'TRX008', 'TRX009', 'TRX010'),
ID_Investasi = c('V001', 'V005', 'V010', 'V003', 'V007', 'V012', 'V002', 'V008', 'V015', 'V004'),
Jenis_Transaksi = factor(c('Pembelian', 'Penjualan', 'Dividen', 'Capital Gain', 'Pembelian', 'Penjualan', 'Dividen', 'Capital Gain', 'Pembelian', 'Penjualan'),
levels = c('Pembelian', 'Penjualan', 'Dividen', 'Capital Gain')),
Tanggal_Transaksi = as.Date(c('2023-01-15', '2023-02-20', '2023-03-10', '2023-04-05', '2023-05-12', '2023-06-18', '2023-07-22', '2023-08-30', '2023-09-14', '2023-10-25')),
Jumlah_Unit = c(100.00, 50.00, 10.00, 75.00, 200.00, 30.00, 5.00, 120.00, 150.00, 80.00),
Harga_Per_Unit = c(1500.00, 2000.00, 500.00, 1200.00, 800.00, 2500.00, 300.00, 950.00, 1700.00, 1350.00),
Total_Nilai = c(150000.00, 100000.00, 5000.00, 90000.00, 160000.00, 75000.00, 1500.00, 114000.00, 255000.00, 108000.00),
Biaya_Transaksi = c(750.00, 500.00, 0.00, 450.00, 800.00, 375.00, 0.00, 570.00, 1275.00, 540.00)
)
Kinerja_Portofolio <- data.frame(
ID_Kinerja = paste0('K', 1:20),
ID_Portofolio = c('P001', 'P002', 'P003', 'P004', 'P005', 'P006', 'P007', 'P008', 'P009', 'P010', 'P011', 'P012', 'P001', 'P002', 'P003', 'P004', 'P005', 'P006', 'P007', 'P008'),
Tanggal_Evaluasi = as.Date(c(rep('2023-01-15', 6), rep('2023-02-20', 6), rep('2023-03-25', 6), rep('2023-04-30', 2))),
Nilai_Awal = c(1000000, 2500000, 500000, 750000, 3000000, 1500000, 1000000, 1800000, 2200000, 950000, 1300000, 2750000, 1050000, 2450000, 520000, 780000, 3150000, 1470000, 1080000, 1764000),
Nilai_Sekarang = c(1050000, 2450000, 520000, 780000, 3150000, 1470000, 1080000, 1764000, 2332000, 1007000, 1261000, 2915000, 1071000, 2597000, 530400, 795600, 3276000, 1499400, 1123200, 1728720),
Return_Persen = c(5.0, -2.0, 4.0, 4.0, 5.0, -2.0, 8.0, -2.0, 6.0, 6.0, -3.0, 6.0, 2.0, 6.0, 2.0, 2.0, 4.0, 2.0, 4.0, -2.0),
Volatilitas = c(2.1, 3.4, 1.8, 2.3, 2.9, 3.1, 2.5, 3.0, 2.7, 2.4, 3.2, 3.3, 2.0, 3.3, 1.9, 2.2, 2.8, 3.0, 2.4, 2.9)
)
Pengguna dapat melihat Profil Risiko Investor dan Total nilai saham yang ditransaksikan. Hal ini sangat bermanfaat untuk menganalisis perilaku investor dan mengelompokkannya berdasarkan volume transaksi yang dilakukan.
Pengguna dapat melihat siapa investor yang memiliki nilai investasi terteinggi