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IMPLEMENTASI LOGIKA FUZZY
DALAM PROGRAM
PENGAMBILAN KEPUTUSAN
PENERIMA BEASISWA
ILHAM ABDUL RAHMAN F 10112093
PANDEGA HAQQI SADIEDA 10112146
IMAM NUR ARIFIN 10112652
AI-3
Fuzzy Logic
Peningkatan dari logika Boolean yang berhadapan dengan
konsep kebenaran sebagian. Saat logika klasik menyatakan
bahwa segala hal dapat diekspresikan dalam
istilah biner (0 atau 1, hitam atau putih, ya atau tidak),
logika fuzzy menggantikan kebenaran boolean
dengan tingkat kebenaran.
Logika Fuzzy memungkinkan nilai keanggotaan antara 0
dan 1, tingkat keabuan dan juga hitam dan putih, dan
dalam bentuk linguistik, konsep tidak pasti seperti
"sedikit", "lumayan", dan "sangat". Logika ini
berhubungan dengan set fuzzy dan teori kemungkinan.
Logika fuzzy diperkenalkan oleh Dr. Lotfi
Zadeh dari Universitas California, Berkeley pada 1965
Algoritma Logika Fuzzy
Kasus
 Kasus “Pengambilan keputusan penerima
beasiswa”
 Parameter yang digunakan dalam inputan yaitu
nilai IPK(Indeks Prestasi Komulatif) dan Gaji Orang tua
Derajat Keanggotaan Logika Fuzzy
Dalam kasus ini, kami menggunakan 2 derajat keanggotaan yaitu :
1. Derajat keanggotaan segitiga
Derajat Keanggotaan Logika Fuzzy
2. Derajat keanggotaan trapesium
Aturan
Fuzzification
Masukkan-masukkan yang nilai kebenarannya bersifat
pasti (crips input) konversi ke bentuk fuzzy input, yang
berupa nilai yang semantiknya ditentukan berdasarkan
fungsi keanggotaan.
Fuzzification IPK
Fuzzification Gaji Orang tua
Inference
//rule 1:ipk buruk and gaji kecil -> rendah
if(((inRange(ipkBuruk, ipk))==1)&&(inRange(gajiKecil,
gaji)==1)) {}
//rule 2:ipk buruk and gaji sedang -> rendah
if(((inRange(ipkBuruk,
ipk))==1)&&(inRange(gajiSedang, gaji)==1)){}
//rule 3:ipk buruk and gaji besar -> rendah
if(((inRange(ipkBuruk,
ipk))==1)&&(inRange(gajiBesar, gaji)==1)){}
…
//rule 11:ipk bagus and gaji besar -> tinggi
if(((inRange(ipkBagus,
ipk))==1)&&(inRange(gajiBesar, gaji)==1)){}
//rule 12:ipk bagus and gaji sangat besar -> rendah
if(((inRange(ipkBagus,
ipk))==1)&&(inRange(gajiSangatBesar, gaji)==1)){}
Defuzzyfication
Sebelum Defuzzyfication, harus melakukan proses
composition, yaitu agregasi hasil Clipping dari semua
aturan fuzzy sehingga kita dapatkan satu fuzzy set tunggal.
Merupakan proses pengubahan variabel berbentuk fuzzy
tersebut menjadi data-data pasti (crisp).
Defuzzyfication
DEMO PROGRAM

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7 Implementasi_Logika_Fuzzy.pptx

  • 1. IMPLEMENTASI LOGIKA FUZZY DALAM PROGRAM PENGAMBILAN KEPUTUSAN PENERIMA BEASISWA ILHAM ABDUL RAHMAN F 10112093 PANDEGA HAQQI SADIEDA 10112146 IMAM NUR ARIFIN 10112652 AI-3
  • 2. Fuzzy Logic Peningkatan dari logika Boolean yang berhadapan dengan konsep kebenaran sebagian. Saat logika klasik menyatakan bahwa segala hal dapat diekspresikan dalam istilah biner (0 atau 1, hitam atau putih, ya atau tidak), logika fuzzy menggantikan kebenaran boolean dengan tingkat kebenaran.
  • 3. Logika Fuzzy memungkinkan nilai keanggotaan antara 0 dan 1, tingkat keabuan dan juga hitam dan putih, dan dalam bentuk linguistik, konsep tidak pasti seperti "sedikit", "lumayan", dan "sangat". Logika ini berhubungan dengan set fuzzy dan teori kemungkinan. Logika fuzzy diperkenalkan oleh Dr. Lotfi Zadeh dari Universitas California, Berkeley pada 1965
  • 5. Kasus  Kasus “Pengambilan keputusan penerima beasiswa”  Parameter yang digunakan dalam inputan yaitu nilai IPK(Indeks Prestasi Komulatif) dan Gaji Orang tua
  • 6. Derajat Keanggotaan Logika Fuzzy Dalam kasus ini, kami menggunakan 2 derajat keanggotaan yaitu : 1. Derajat keanggotaan segitiga
  • 7. Derajat Keanggotaan Logika Fuzzy 2. Derajat keanggotaan trapesium
  • 9. Fuzzification Masukkan-masukkan yang nilai kebenarannya bersifat pasti (crips input) konversi ke bentuk fuzzy input, yang berupa nilai yang semantiknya ditentukan berdasarkan fungsi keanggotaan.
  • 12. Inference //rule 1:ipk buruk and gaji kecil -> rendah if(((inRange(ipkBuruk, ipk))==1)&&(inRange(gajiKecil, gaji)==1)) {} //rule 2:ipk buruk and gaji sedang -> rendah if(((inRange(ipkBuruk, ipk))==1)&&(inRange(gajiSedang, gaji)==1)){} //rule 3:ipk buruk and gaji besar -> rendah if(((inRange(ipkBuruk, ipk))==1)&&(inRange(gajiBesar, gaji)==1)){} … //rule 11:ipk bagus and gaji besar -> tinggi if(((inRange(ipkBagus, ipk))==1)&&(inRange(gajiBesar, gaji)==1)){} //rule 12:ipk bagus and gaji sangat besar -> rendah if(((inRange(ipkBagus, ipk))==1)&&(inRange(gajiSangatBesar, gaji)==1)){}
  • 13. Defuzzyfication Sebelum Defuzzyfication, harus melakukan proses composition, yaitu agregasi hasil Clipping dari semua aturan fuzzy sehingga kita dapatkan satu fuzzy set tunggal. Merupakan proses pengubahan variabel berbentuk fuzzy tersebut menjadi data-data pasti (crisp).