Nama = Izza Syahri Muharram || Nim = 220605110073 || Mata kuliah = Linear Algebra || Dosen Pengampu = Prof. Dr. SUHARTONO, M.Kom || Teknik Informatika || Universitas Islam Negeri Malang.
# Definisi matriks A
A <- matrix(c(1, 2, 3, 4), nrow = 2, byrow = TRUE)
# Definisi matriks B
B <- matrix(c(5, 6, 7, 8), nrow = 2, byrow = TRUE)
# Penjumlahan matriks A dan B
C <- A + B
# Print hasil penjumlahan
print(C)
## [,1] [,2]
## [1,] 6 8
## [2,] 10 12
Pengurangan matrix
# Definisi matriks A
A <- matrix(c(1, 2, 3, 4), nrow = 2, byrow = TRUE)
# Definisi matriks B
B <- matrix(c(5, 6, 7, 8), nrow = 2, byrow = TRUE)
# Pengurangan matriks A dan B
C <- A - B
# Print hasil pengurangan
print(C)
## [,1] [,2]
## [1,] -4 -4
## [2,] -4 -4
Perkalian matrix
# Definisi matriks A
A <- matrix(c(1, 2, 3, 4), nrow = 2, byrow = TRUE)
# Definisi matriks B
B <- matrix(c(5, 6, 7, 8), nrow = 2, byrow = TRUE)
# Perkalian matriks A dan B
C <- A %*% B
# Print hasil perkalian
print(C)
## [,1] [,2]
## [1,] 19 22
## [2,] 43 50
transpos matrix
# Definisi matriks A
A <- matrix(c(1, 2, 3, 4), nrow = 2, byrow = TRUE)
# Transpos matriks A
B <- t(A)
# Print hasil transpos
print(B)
## [,1] [,2]
## [1,] 1 3
## [2,] 2 4
invers matrix
# Definisi matriks A
A <- matrix(c(1, 2, 3, 4), nrow = 2, byrow = TRUE)
# Invers matriks A
B <- solve(A)
# Print hasil invers
print(B)
## [,1] [,2]
## [1,] -2.0 1.0
## [2,] 1.5 -0.5
Latihan 1. Misalkan kita memiliki matriks berikut dengan bilangan tertentu vektor baris dan vektor kolom. A B C D E F (2 × 3) (3 × 3) (4 × 3) (2 × 3) (3 × 2) (2 × 4) Tentukan banyaknya vektor baris dan banyaknya vektor kolom dari mengikuti: 1.CT 2. A + ET 3. A · B 4. A · B + D 5. A·B·CT 6. (A · B · CT )T 7. A · B · CT + F
# Define matrices A, B, C, D, E, F with their respective dimensions
A <- matrix(1:6, nrow = 2, ncol = 3, byrow = TRUE)
B <- matrix(7:15, nrow = 3, ncol = 3, byrow = TRUE)
C <- matrix(16:31, nrow = 4, ncol = 3, byrow = TRUE)
## Warning in matrix(16:31, nrow = 4, ncol = 3, byrow = TRUE): data length [16] is
## not a sub-multiple or multiple of the number of columns [3]
D <- matrix(32:37, nrow = 2, ncol = 3, byrow = TRUE)
E <- matrix(38:43, nrow = 3, ncol = 2, byrow = TRUE)
F <- matrix(44:51, nrow = 2, ncol = 4, byrow = TRUE)
# Determine the number of row vectors and column vectors of the given operations
# 1. CT
result1 <- t(C)
dim1 <- dim(result1)
# 2. A + ET
result2 <- A + t(E)
dim2 <- dim(result2)
# 3. A · B
result3 <- A %*% B
dim3 <- dim(result3)
# 4. A · B + D
result4 <- result3 + D
dim4 <- dim(result4)
# 5. A · B · CT
result5 <- result3 %*% t(C)
dim5 <- dim(result5)
# 6. (A · B · CT)T
result6 <- t(result5)
dim6 <- dim(result6)
# 7. A · B · CT + F
result7 <- result5 + F
dim7 <- dim(result7)
# Print the dimensions
print("Dimensions:")
## [1] "Dimensions:"
print("1. CT")
## [1] "1. CT"
print(dim1)
## [1] 3 4
print("2. A + ET")
## [1] "2. A + ET"
print(dim2)
## [1] 2 3
print("3. A · B")
## [1] "3. A · B"
print(dim3)
## [1] 2 3
print("4. A · B + D")
## [1] "4. A · B + D"
print(dim4)
## [1] 2 3
print("5. A · B · CT")
## [1] "5. A · B · CT"
print(dim5)
## [1] 2 4
print("6. (A · B · CT)T")
## [1] "6. (A · B · CT)T"
print(dim6)
## [1] 4 2
print("7. A · B · CT + F")
## [1] "7. A · B · CT + F"
print(dim7)
## [1] 2 4
Daftar pustaka 1. Yoshida.Ruriko.2021.Linear Algebra and Its Applications With R.London. CRC Press.