FAKTOR KEPASTIAN DAN KETIDAKPASTIAN Farah Zakiyah Rahmanti Mei 2015
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Overview
Penalaran Faktor Ketidakpastian Probabilitas Faktor Kepastian (CF / Certainty Factor)
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Penalaran (1)
Penambahan fakta-fakta baru membuat tidak konsisten.
Karakteristik :
Ketidakpastian
Adanya perubahan pengetahuan
Adanya penambahan fakta baru yang dapat mengubah kesimpulan yang sudah dibentuk
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Penalaran (2)
Contoh :
Premise 1 : aljabar merupakan pelajaran yang sulit
Premise 2 : geometri merupakan pelajaran yang sulit
Premise 3 : kalkulus merupakan pelajaran yang sulit
Kesimpulan : matematika merupakan pelajaran yang sulit
Premise 4 : sosiologi merupakan pelajaran yang sulit
Tidak berlaku, karena sosiologi bukan merupakan bagian dari matematika
Penalaran induktif sangat mungkin adanya ketidakpastian
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Faktor Ketidakpastian
Kurangnya informasi yang memadai
Terhalang untuk membuat keputusan yang baik
Salah satu teori yang berkaitan dengan faktor ketidakpastian : probabilitas Bayes
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Probabilitas
Probabilitas menunjukkan bahwa kemungkinan sesuatu akan terjadi atau tidak.
Contoh :
Ada 10 alumni Teknik Informatika, 3 diantaranya mahir bahasa pemrograman Java.
Maka probabilitasnya p(java) = 3/10 = 0.3
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Faktor Kepastian (CF / Certainty Factor)
CF menunjukkan ukuran kepastian fakta-fakta atau aturan-aturan.
CF [h,e] = MB [h,e] - MD [h,e]
CF [h,e] = certainty factor (faktor kepastian) MB [h,e] = measure of belief level to the hypothesis h, if it is influenced by evidence e (between 0 and 1). MD [h,e] = measure of disbelief level to the hypothesis h, if it is influenced by evidence e (between 0 and 1).
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Certainty Factor (2)
Some evidence are combined to determine the CF of a hypothesis.
If e1 and e2 are the observations, then:
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Certainty Factor (3)
Example : Suppose an observation gives credence to h with MB [h, e1] = 0.3 and MD [h, e1] = 0, then : CF[h,e1]=0.3-0=0.3 if there is a new observation with MB [h, e2] = 0.2 and MD [h, e2] = 0, then: MB[h,e1 ^ e2]=0.3+0.2*(1-0.3)=0.44 MD[h,e1 ^ e2]=0 CF[h,e1 ^ e2]=0.44-0=0.44 Universitas Dian Nuswantoro
Certainty Factor (4)
Example : Asih suffers freckles on her face. The doctor estimates that Asih suffered pox with belief MB [pox, freckles] = 0.8 and MD [pox, freckles] = 0.01, then : CF[pox,freckles]=0.8-0.01=0.79 If there is a new observation that Asih is also fever with belief MB[pox,fever]=0.7 and MD[pox,fever]=0.08, then : MB[pox,freckles^fever]=0.8+0.7*(1-0.8)=0.9 MD[pox,freckles^fever]=0.01+0.08*(1-0.01)=0.0892 CF[pox,freckles^fever]=0.94-0.0892=0.8508 Universitas Dian Nuswantoro
Certainty Factor (5)
CF dihitung dari beberapa hipotesis. Jika terdapat hipotesis h1 dan h2, maka :
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Certainty Factor (6)
Contoh : Suppose an observation gives credence to h with MB [h1, e] = 0.5 and MD [h1, e] = 0.2, then : CF[h1,e]=0.5-0.2=0.3 if these observations also give credence to hS2 with MB [h2, e] = 0.8 and MD [h2, e] = 0.1, then: CF [h2, e] = 0.8-0.1 = 0.7 Finding CF [h1 ^ h2, e] is obtained from MB [h1 ^ h2, e] = min (0.5; 0.8) = 0.5 MD [h1 ^ h2, e] = min (0.2; 0.1) = 0.1 CF [h1 ^ h2, e] = 0.5-0.1 = 0,4 Finding CF [h1∨h2, e] is obtained from MB [h1∨ h2, e] = max (0.5; 0.8) = 0.8 MD [h1∨ h2, e] = max (0.2; 0.1) = 0.2 CF [h1∨ h2, e] = 0.8-0.2 Universitas = 0.6 Dian Nuswantoro
Certainty Factor (7) Example :
Asih suffer freckles on her face. The doctor estimates that Asih suffered pox with belief MB [pox, freckles] = 0.8 and MD [pox, freckles] = 0.01, then : CF[pox,freckles]=0.8-0.01=0.79
If these observations also provide belief that Asih may also affected by allergies with belief MB [allergies, freckles] = 0.4 and MD [pox, freckles] = 0.3 then : CF [allergies, freckles] = 0.4-0.3 = 0.1 Finding CF[pox^ allergies,freckles] is obtained from : MB[pox^ allergies,freckles]=min(0.8;0.4)=0.4 MD[pox^ allergies,freckles]=min(0.01;0.3)=0.01 CF[pox^ allergies,freckles]=0.4-0.01=0.39 Finding CF[pox ∨ allergies,freckles] is obtained from : MB[pox ∨ allergies,freckles]=max(0.8;0.4)=0.8 MD[pox ∨ allergies,freckles]=max(0.01;0.3)=0.3 CF[pox ∨ allergies,freckles]=0.8-0.3=0.5 Universitas Dian Nuswantoro
Certainty Factor (7) Conclusion: All belief factors that Asif suffer pox from the appearance of freckles on her face is 0.79. Similarly, the belief factor that Asih suffer allergy from the appearance of freckles on her face is 0.1. with the same symptoms that influence two different hypotheses, so the belief factor become : Asih suffers pox and allergies = 0.39 Asih suffers pox or allergic = 0.5 Universitas Dian Nuswantoro
Reference
Ahmad Haidaroh, “Pengenalan Kecerdasan buatan”, Materi ke-5, STIKOM Artha Buana
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