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Sindy Nova, S.Kom., MMSI
 Kompresi berarti memampatkan/mengecilkan
ukuran.
 Kompresi data adalah : Proses mengkodekan
informasi menggunakan bit atau information
bearing unit yang lain yang lebih rendah
daripada representasi data yang tidak
terkodekan dengan suatu sistem enkoding
tertentu.
 Contoh kompresi sederhana misalnya kata
“Yang” menjadi kata “Yg”.
 Pengiriman data hasil kompresi dapat
dilakukan jika pihak pengirim dan pihak
penerima memiliki aturan yang sama dalam
hal kompresi data.
 Kompresi data menjadi sangat penting,
karena memperkecil kebutuhan penyimpanan
data, mempercepat pengiriman data, dan
memperkecil kebutuhan bandwith.
 Contoh teknik kompresi gambar :
 JPEG, PNG, GIF
 Contoh teknik kompresi audio :
 MP3, AAC, RMA, WMA
 Contoh teknik kompresi video :
 MPEG, 3GP
 Berdasarkan Mode Penerimaan Data oleh
Manusia :
 Dialoque Mode : Yaitu proses penerimaan data
dimana pengirim dan penerima seakan berdialog
langsung (Real Time).
 Retrieval Mode : Yaitu proses penerimaan data
tidak dilakukan secara Real Time.
 Berdasarkan Output :
 Lossy Compression : Teknik kompresi dimana data
hasil dekompresi tidak sama dengan data
sebelum kompresi, namun sudah “cukup” untuk
digunakan.
 Loseless Compression : Teknik kompresi dimana
data hasil kompresi dapat didekompres lagi dan
hasilnya tepat sama seperti data sebelum proses
kompresi.
 Entropy Encoding
 Bersifat Loseless.
 Tekniknya tidak berdasarkan media dengan
spesifikasi dan karakteristik tertentu namun
berdasarkan urutan datanya.
 Statistical encoding, tidak memperhatikan
semantik data.
 Contoh : Run Length Encoding, Static Huffman
Coding.
 Source Coding
 Bersifat Lossy.
 Berkaitan dengan data semantik (arti data) dan media.
 Contoh : Prediction (DPCM, DM), Transformation (FFT,
DCT), Layered Coding (Bit Position, Subsampling), Vector
Quantization.
 Hybrid Coding
 Gabungan antara Lossy dengan Loseless.
 Contoh : JPEG, MPEG.
 RUN LENGTH ENCODING (RLE)
 Kompresi data teks dilakukan jika ada beberapa
huruf yang sama yang ditampilkan berturut-
turut.
 Memiliki 2 type yaitu :
 RLE Tipe 1, dan
 RLE Tipe 2
 Minimal 4 huruf sama.
 RLE Tipe 1 menggunakan tanda “!”
 RLE Tipe 2 menggunakan tanda negatif (-)
ABCCCCCCCCDEFGGGG
 RLE Tipe 1 :
 RLE Tipe 2 :
 STATIC HUFFMAN CODING ALGORITHM
 Frekwensi karakter dari string yang akan
dikompres dianalisis terlebih dahulu.
 Selanjutnya dibuat pohon Huffman yang
merupakan pohon biner dengan ROOT awal yang
diberi nilai 0 (disebelah kiri) dan 1 (disebelah
kanan).
 Untuk dahan sebelah kiri, diberi nilai 1
(disebelah kiri) dan 0 (disebelah kanan).
 Untuk dahan sebelah kanan, diberi nilai 0
(disebelah kiri) dan 1 (disebelah kanan).
 Menggunakan teknik A Bottom Up Approach : dimana
frekwensi terkecil dikerjakan terlebih dahulu untuk
diletakkan ke dalam leaf (daun).
 Kemudian daun-daun akan dikombinasikan dan
dijumlahkan probabilitasnya menjadi Root di atasnya.
 Contoh : MAMA SAYA
A = 4 → 4/8 = 0,5
M = 2 → 2/8 = 0,25
S = 1 → 1/8 = 0,125
Y = 1 → 1/8 = 0,125
 SHANNON FANO ALGORITHM
 Dikembangkan oleh Shannon dan Robert Fano.
 Urutkan simbol berdasarkan frekwensi
kemunculannya.
 Bagi simbol menjadi 2 bagian secara rekursif,
dengan jumlah yang kira-kira sama pada kedua
bagian, sampai tiap bagian hanya terdiri dari
satu simbol.
 Menggunakan teknik Top Down Approach :
dimana frekwensi terbesar dimasukan ke dalam
leaf terlebih dahulu.
 Contoh : HELLO
L = 2
H = 1
E = 1
O = 1

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Kompresi Data Digital

  • 2.  Kompresi berarti memampatkan/mengecilkan ukuran.  Kompresi data adalah : Proses mengkodekan informasi menggunakan bit atau information bearing unit yang lain yang lebih rendah daripada representasi data yang tidak terkodekan dengan suatu sistem enkoding tertentu.
  • 3.  Contoh kompresi sederhana misalnya kata “Yang” menjadi kata “Yg”.  Pengiriman data hasil kompresi dapat dilakukan jika pihak pengirim dan pihak penerima memiliki aturan yang sama dalam hal kompresi data.  Kompresi data menjadi sangat penting, karena memperkecil kebutuhan penyimpanan data, mempercepat pengiriman data, dan memperkecil kebutuhan bandwith.
  • 4.  Contoh teknik kompresi gambar :  JPEG, PNG, GIF  Contoh teknik kompresi audio :  MP3, AAC, RMA, WMA  Contoh teknik kompresi video :  MPEG, 3GP
  • 5.  Berdasarkan Mode Penerimaan Data oleh Manusia :  Dialoque Mode : Yaitu proses penerimaan data dimana pengirim dan penerima seakan berdialog langsung (Real Time).  Retrieval Mode : Yaitu proses penerimaan data tidak dilakukan secara Real Time.
  • 6.  Berdasarkan Output :  Lossy Compression : Teknik kompresi dimana data hasil dekompresi tidak sama dengan data sebelum kompresi, namun sudah “cukup” untuk digunakan.  Loseless Compression : Teknik kompresi dimana data hasil kompresi dapat didekompres lagi dan hasilnya tepat sama seperti data sebelum proses kompresi.
  • 7.  Entropy Encoding  Bersifat Loseless.  Tekniknya tidak berdasarkan media dengan spesifikasi dan karakteristik tertentu namun berdasarkan urutan datanya.  Statistical encoding, tidak memperhatikan semantik data.  Contoh : Run Length Encoding, Static Huffman Coding.
  • 8.  Source Coding  Bersifat Lossy.  Berkaitan dengan data semantik (arti data) dan media.  Contoh : Prediction (DPCM, DM), Transformation (FFT, DCT), Layered Coding (Bit Position, Subsampling), Vector Quantization.  Hybrid Coding  Gabungan antara Lossy dengan Loseless.  Contoh : JPEG, MPEG.
  • 9.  RUN LENGTH ENCODING (RLE)  Kompresi data teks dilakukan jika ada beberapa huruf yang sama yang ditampilkan berturut- turut.  Memiliki 2 type yaitu :  RLE Tipe 1, dan  RLE Tipe 2  Minimal 4 huruf sama.  RLE Tipe 1 menggunakan tanda “!”  RLE Tipe 2 menggunakan tanda negatif (-)
  • 10. ABCCCCCCCCDEFGGGG  RLE Tipe 1 :  RLE Tipe 2 :
  • 11.  STATIC HUFFMAN CODING ALGORITHM  Frekwensi karakter dari string yang akan dikompres dianalisis terlebih dahulu.  Selanjutnya dibuat pohon Huffman yang merupakan pohon biner dengan ROOT awal yang diberi nilai 0 (disebelah kiri) dan 1 (disebelah kanan).  Untuk dahan sebelah kiri, diberi nilai 1 (disebelah kiri) dan 0 (disebelah kanan).  Untuk dahan sebelah kanan, diberi nilai 0 (disebelah kiri) dan 1 (disebelah kanan).
  • 12.  Menggunakan teknik A Bottom Up Approach : dimana frekwensi terkecil dikerjakan terlebih dahulu untuk diletakkan ke dalam leaf (daun).  Kemudian daun-daun akan dikombinasikan dan dijumlahkan probabilitasnya menjadi Root di atasnya.  Contoh : MAMA SAYA A = 4 → 4/8 = 0,5 M = 2 → 2/8 = 0,25 S = 1 → 1/8 = 0,125 Y = 1 → 1/8 = 0,125
  • 13.  SHANNON FANO ALGORITHM  Dikembangkan oleh Shannon dan Robert Fano.  Urutkan simbol berdasarkan frekwensi kemunculannya.  Bagi simbol menjadi 2 bagian secara rekursif, dengan jumlah yang kira-kira sama pada kedua bagian, sampai tiap bagian hanya terdiri dari satu simbol.
  • 14.  Menggunakan teknik Top Down Approach : dimana frekwensi terbesar dimasukan ke dalam leaf terlebih dahulu.  Contoh : HELLO L = 2 H = 1 E = 1 O = 1