Kesimpulan Saran Aplikasi Relasi Peluang Bersyarat Fuzzy pada Sistem Informasi Fuzzy

Intan R, Mukaidono M . 2004. Fuzzy Conditional Probability Relations and their Applications in Fuzzy Information Systems. Knowledge and Information Systems 2004 6 : 345-365. http:www.proquest.pqdweb.comfuzzy relation. [14 Februari 2008] Klir GJ, Yuan B . 1995. Fuzzy Sets and Fuzzy Logic : Theory and Applications. Prentice-Hall, New Jersey. Pawlak Z . 1991. Rough Sets Theoretical Aspects of Reasoning about Data. Kluwer Kusumadewi, S . 2002. Analisis Desain Sistem Fuzzy Menggunakan Tool Box Matlab. Graha Ilmu, Yogyakarta. Yager RR. 1990. Ordinal Measures of Specificity. Int J Gen Syst 17:57-72. Zadeh LA . 1970. Similarity Relations and Fuzzy Orderings. Inf Sci 32 :177-200. LAMPIRAN LAMPIRAN 1 Hasil Transformasi Tabel 3 Tabel 17 I U, A = {c 1 = ‘w 1 ’, c 2 = ‘x 1 ’, c 3 = ‘y 1 ’, b 1 = ‘z 1 ’} U c 1 = ’w 1 ’ c 2 = ’x 1 ’ c 3 = ’ y 1 ’ b 1 = ’ z 1 ’ min c 1 , c 2 , c 3 min c 1 , c 2 , c 3, b 1 u 1 1 1 1 1 1 1 u 2 1 0 0 1 u 3 0 0 0 0 u 4 1 1 1 0 1 u 5 0 0 1 0 u 6 1 1 1 1 1 1 u 7 0 0 0 1 u 8 1 0 0 1 ∑ 5 3 4 5 3 2 1 2 3 1 1 1 2 3 m in , , , 2 , , m in , , 3 c c c b C B c c c δ = = ∑ ∑ 1 2 3 1 1 1 m in , , , 2 , . m in 5 c c c b B C b δ = = ∑ ∑ Tabel 18 I U, A = {c 1 = ‘w 1 ’, c 2 = ‘x 2 ’, c 3 = ‘y 3 ’, b 1 = ‘z 1 ’} U c 1 = ’w 1 ’ c 2 = ’x 2 ’ c 3 = ’ y 3 ’ b 1 = ’ z 1 ’ min c 1 , c 2 , c 3 min c 1 , c 2 , c 3, b 1 u 1 1 0 0 1 u 2 1 1 1 1 1 1 u 3 0 1 1 0 u 4 1 0 0 0 u 5 0 1 0 0 u 6 1 0 0 1 u 7 0 1 1 1 u 8 1 1 1 1 1 1 ∑ 5 5 4 5 2 2 1 2 3 1 2 1 2 3 m in , , , 2 , 1, m in , , 2 c c c b C B c c c δ = = = ∑ ∑ 1 2 3 1 2 1 m in , , , 2 , . m in 5 c c c b B C b δ = = ∑ ∑ Tabel 19 I U, A = {c 1 = ‘w 2 ’, c 2 = ‘x 2 ’, c 3 = ‘y 3 ’, b 1 = ‘z 2 ’} U c 1 = ’w 2 ’ c 2 = ’x 2 ’ c 3 = ’ y 3 ’ b 1 = ’ z 2 ’ min c 1 , c 2 , c 3 min c 1 , c 2 , c 3, b 1 u 1 0 0 0 0 u 2 0 1 1 0 u 3 1 1 1 1 1 1 u 4 0 0 0 1 u 5 1 1 0 1 u 6 0 0 0 0 u 7 1 1 1 0 1 u 8 0 1 1 0 ∑ 3 5 4 3 2 1 1 2 3 1 3 1 2 3 m in , , , 1 , , m in , , 2 c c c b C B c c c δ = = ∑ ∑ 1 2 3 1 3 1 m in , , , 1 , . m in 3 c c c b B C b δ = = ∑ ∑ Tabel 20 I U, A = {c 1 = ‘w 1 ’, c 2 = ‘x 1 ’, c 3 = ‘y 1 ’, b 1 = ‘z 2 ’} U c 1 = ’w 1 ’ c 2 = ’x 1 ’ c 3 = ’ y 1 ’ b 1 = ’ z 2 ’ min c 1 , c 2 , c 3 min c 1 , c 2 , c 3, b 1 u 1 1 1 1 0 1 u 2 1 0 0 0 u 3 0 0 0 1 u 4 1 1 1 1 1 1 u 5 0 0 1 1 u 6 1 1 1 0 1 u 7 0 0 0 0 u 8 1 0 0 0 ∑ 5 3 4 3 3 1 1 2 3 1 4 1 2 3 m in , , , 1 , , m in , , 3 c c c b C B c c c δ = = ∑ ∑ 1 2 3 1 4 1 m in , , , 1 , . m in 3 c c c b B C b δ = = ∑ ∑ Tabel 21 I U, A = {c 1 = ‘w 2 ’, c 2 = ‘x 2 ’, c 3 = ‘y 1 ’, b 1 = ‘z 2 ’} U c 1 = ’w 2 ’ c 2 = ’x 2 ’ c 3 = ’ y 1 ’ b 1 = ’ z 2 ’ min c 1 , c 2 , c 3 min c 1 , c 2 , c 3, b 1 u 1 0 0 1 0 u 2 0 1 0 0 u 3 1 1 0 1 u 4 0 0 1 1 u 5 1 1 1 1 1 1 u 6 0 0 1 0 u 7 1 1 0 0 u 8 0 1 0 0 ∑ 3 5 4 3 1 1 1 2 3 1 5 1 2 3 m in , , , 1 , 1, m in , , 1 c c c b C B c c c δ = = = ∑ ∑ 1 2 3 1 5 1 m in , , , 1 , . m in 3 c c c b B C b δ = = ∑ ∑ Tabel 22 I U, A = {c 1 = ‘w 1 ’, c 2 = ‘x 1 ’, c 3 = ‘y 1 ’, b 1 = ‘z 1 ’} U c 1 = ’w 1 ’ c 2 = ’x 1 ’ c 3 = ’ y 1 ’ b 1 = ’ z 1 ’ min c 1 , c 2 , c 3 min c 1 , c 2 , c 3, b 1 u 1 1 1 1 1 1 1 u 2 1 0 0 1 u 3 0 0 0 0 u 4 1 1 1 0 1 u 5 0 0 1 0 u 6 1 1 1 1 1 1 u 7 0 0 0 1 u 8 1 0 0 1 ∑ 5 3 4 5 3 2 1 2 3 1 6 1 2 3 m in , , , 2 , , m in , , 3 c c c b C B c c c δ = = ∑ ∑ 1 2 3 1 6 1 m in , , , 2 , . m in 5 c c c b B C b δ = = ∑ ∑ Tabel 23 I U, A = {c 1 = ‘w 2 ’, c 2 = ‘x 2 ’, c 3 = ‘y 3 ’, b 1 = ‘z 1 ’} U c 1 = ’w 2 ’ c 2 = ’x 2 ’ c 3 = ’ y 3 ’ b 1 = ’ z 1 ’ min c 1 , c 2 , c 3 min c 1 , c 2 , c 3, b 1 u 1 0 0 0 1 u 2 0 1 1 1 u 3 1 1 1 0 1 u 4 0 0 0 0 u 5 1 1 0 0 u 6 0 0 0 1 u 7 1 1 1 1 1 1 u 8 0 1 1 1 ∑ 3 5 4 5 2 1 1 2 3 1 7 1 2 3 m in , , , 1 , , m in , , 2 c c c b C B c c c δ = = ∑ ∑ 1 2 3 1 7 1 m in , , , 1 , . m in 5 c c c b B C b δ = = ∑ ∑ Tabel 24 I U, A = {c 1 = ‘w 1 ’, c 2 = ‘x 2 ’, c 3 = ‘y 3 ’, b 1 = ‘z 1 ’} U c 1 = ’w 1 ’ c 2 = ’x 2 ’ c 3 = ’ y 3 ’ b 1 = ’ z 1 ’ min c 1 , c 2 , c 3 min c 1 , c 2 , c 3, b 1 u 1 1 0 0 1 u 2 1 1 1 1 1 1 u 3 0 1 1 0 u 4 1 0 0 0 u 5 0 1 0 0 u 6 1 0 0 1 u 7 0 1 1 1 u 8 1 1 1 1 1 1 ∑ 5 5 4 5 2 2 1 2 3 1 8 1 2 3 m in , , , 2 , 1, m in , , 2 c c c b C B c c c δ = = = ∑ ∑ 1 2 3 1 8 1 m in , , , 2 , . m in 5 c c c b B C b δ = = ∑ ∑ LAMPIRAN 2 Perhitungan Tabel 5 untuk Contoh 4 Derajat kemiripan antara setiap himpunan fuzzy hasil dari fuzzy partition dengan data crisp yang ada pada Tabel 5 akan dihitung dengan menggunakan FCPR sebagai berikut : R E he, N = | | | | he N N ∩ = min0,1 1 = 0, R E he, ES = | | | | he ES ES ∩ = min0,1 1 = 0, R E he, JHS = | | | | he JHS JHS ∩ = min0,1 1 = 0, R E he, SHS = | | | | he SHS SHS ∩ = min0.1,1 1 = 0.1, R E he, BA = | | | | he BA BA ∩ = min0.8,1 1 = 0.8, R E he, MS = | | | | he MS MS ∩ = min1,1 1 = 1, R E he, PhD = | | | | he PhD PhD ∩ = min1,1 1 = 1, R E me, N = | | | | me N N ∩ = min0,1 1 = 0, R E me, ES = | | | | me ES ES ∩ = min0, 0.2 1 = 0.2, R E me, JHS = | | | | me JHS JHS ∩ = min0.5,1 1 = 0.5, R E me, SHS = | | | | me SHS SHS ∩ = min0.9,1 1 = 0.9, R E me, BA = | | | | me BA BA ∩ = min0.2,1 1 = 0.2, R E me, MS = | | | | me MS MS ∩ = min0,1 1 = 0, R E me, PhD = | | | | me PhD PhD ∩ = min0,1 1 = 0, R E le, N = | | | | le N N ∩ = min1,1 1 = 1, R E le, ES = | | | | le ES ES ∩ = min0.8,1 1 = 0.8, R E le, JHS = | | | | le JHS JHS ∩ = min0.5,1 1 = 0.5, R E le, SHS = | | | | le SHS SHS ∩ = min0,1 1 = 0, R E le, BA = | | | | le BA BA ∩ = min0,1 1 = 0, R E le, MS = | | | | le MS MS ∩ = min0,1 1 = 0, R E le, PhD = | | | | le PhD PhD ∩ = min0,1 1 = 0, R S hs, 100 = | 100 | | 100 | hs ∩ = min0,1 1 = 0, R S hs, 125 = | 125 | | 125 | hs ∩ = min0,1 1 = 0, R S hs, 150 = | 150 | | 150 | hs ∩ = min0,1 1 = 0, R S hs, 175 = | 175 | | 175 | hs ∩ = min0,1 1 = 0, R S hs, 200 = | 200 | | 200 | hs ∩ = min0,1 1 = 0, R S hs, 250 = | 250 | | 250 | hs ∩ = min0,1 1 = 0, R S hs, 255 = | 255 | | 255 | hs ∩ = min0.1,1 1 = 0.1, R S hs, 275 = | 275 | | 275 | hs ∩ = min0.5,1 1 = 0.5, R S hs, 300 = | 300 | | 300 | hs ∩ = min1,1 1 = 1, R S hs, 315 = | 315 | | 315 | hs ∩ = min1,1 1 = 1, R S hs, 340 = | 340 | | 340 | hs ∩ = min1,1 1 = 1, R S hs, 350 = | 350 | | 350 | hs ∩ = min1,1 1 = 1, R S hs, 355 = | 355 | | 355 | hs ∩ = min1,1 1 = 1, R S hs, 360 = | 360 | | 360 | hs ∩ = min1,1 1 = 1, R S hs, 374 = | 374 | | 374 | hs ∩ = min1,1 1 = 1, R S hs, 400 = | 300 | | 300 | hs ∩ = min1,1 1 = 1, R S hs, 415 = | 415 | | 415 | hs ∩ = min1,1 1 = 1, R S hs, 420 = | 420 | | 420 | hs ∩ = min1,1 1 = 1, R S hs, 470 = | 470 | | 470 | hs ∩ = min1,1 1 = 1, R S hs, 500 = | 500 | | 500 | hs ∩ = min1,1 1 = 1, R S ms, 100 = | 100 | | 100 | ms ∩ = min0,1 1 = 0, R S ms, 125 = | 125 | | 125 | ms ∩ = min0.5,1 1 = 0.5, R S ms, 150 = | 150 | | 150 | ms ∩ = min1,1 1 = 1, R S ms, 175 = | 175 | | 175 | ms ∩ = min1,1 1 = 1, R S ms, 200 = | 200 | | 200 | ms ∩ = min1,1 1 = 1, R S ms, 250 = | 250 | | 250 | ms ∩ = min1,1 1 = 1, R S ms, 255 = | 255 | | 255 | ms ∩ = min0.9,1 1 = 0.9, R S ms, 275 = | 275 | | 275 | ms ∩ = min0.5,1 1 = 0.5, R S ms, 300 = | 300 | | 300 | ms ∩ = min0,1 1 = 0, R S ms, 315 = | 315 | | 315 | ms ∩ = min0,1 1 = 0, R S ms, 340 = | 340 | | 340 | ms ∩ = min0,1 1 = 0, R S ms, 350 = | 350 | | 350 | ms ∩ = min0,1 1 = 0, R S ms, 355 = | 355 | | 355 | ms ∩ = min0,1 1 = 0, R S ms, 360 = | 360 | | 360 | ms ∩ = min0,1 1 = 0, R S ms, 374 = | 374 | | 374 | ms ∩ = min0,1 1 = 0, R S ms, 400 = | 300 | | 300 | ms ∩ = min0,1 1 = 0, R S ms, 415 = | 415 | | 415 | ms ∩ = min0,1 1 = 0, R S ms, 420 = | 420 | | 420 | ms ∩ = min0,1 1 = 0, R S ms, 470 = | 470 | | 470 | ms ∩ = min0,1 1 = 0, R S ms, 500 = | 500 | | 500 | ms ∩ = min0,1 1 = 0, R S ls, 100 = | 100 | | 100 | ls ∩ = min1,1 1 = 1, R S ls, 125 = | 125 | | 125 | ls ∩ = min0.5,1 1 = 0.5, R S ls, 150 = | 150 | | 150 | ls ∩ = min0,1 1 = 0, R S ls, 175 = | 175 | | 175 | ls ∩ = min0,1 1 = 0, R S ls, 200 = | 200 | | 200 | ls ∩ = min0,1 1 = 0, R S ls, 250 = | 250 | | 250 | ls ∩ = min0,1 1 = 0, R S ls, 255 = | 255 | | 255 | ls ∩ = min0,1 1 = 0, R S ls, 275 = | 275 | | 275 | ls ∩ = min0,1 1 = 0, R S ls, 300 = | 300 | | 300 | ls ∩ = min0,1 1 = 0, R S ls, 315 = | 315 | | 315 | ls ∩ = min0,1 1 = 0, R S ls, 340 = | 340 | | 340 | ls ∩ = min0,1 1 = 0, R S ls, 350 = | 350 | | 350 | ls ∩ = min0,1 1 = 0, R S ls, 355 = | 355 | | 355 | ls ∩ = min0,1 1 = 0, R S ls, 360 = | 360 | | 360 | ls ∩ = min0,1 1 = 0, R S ls, 374 = | 374 | | 374 | ls ∩ = min0,1 1 = 0, R S ls, 400 = | 300 | | 300 | ls ∩ = min0,1 1 = 0, R S ls, 415 = | 415 | | 415 | ls ∩ = min0,1 1 = 0, R S ls, 420 = | 420 | | 420 | ls ∩ = min0,1 1 = 0, R S ls, 470 = | 470 | | 470 | ls ∩ = min0,1 1 = 0, R S ls, 500 = | 500 | | 500 | ls ∩ = min0,1 1 = 0. LAMPIRAN 3 Perhitungan Tabel 5 Tabel 25 Transformasi Tabel 5 U le me he ls ms hs min le, ls min le, ms min le, hs min me, ls u 01 0 0 1 0 0 1 0 0 0 0 u 02 0 0.9 0.1 0 1 0 0 0 0 0 u 03 0 0 1 0 0 1 0 0 0 0 u 04 0.5 0.5 0 0 1 0 0 0.5 0 0 u 05 0.8 0.2 0 0.5 0.5 0 0.5 0.5 0 0.2 u 06 0 0.9 0.1 0 1 0 0 0 0 0 u 07 0 0 1 0 0 1 0 0 0 0 u 08 0 0.9 0.1 0 1 0 0 0 0 0 u 09 0 0 1 0 0 1 0 0 0 0 u 10 0 0.9 0.1 0 0.5 0.5 u 11 1 0 0 1 0 0 1 0 0 0 u 12 0 0.9 0.1 0 0 1 0 0 0 0 u 13 0 0.2 0.8 1 0 0 u 14 0 0.9 0.1 0 0 1 u 15 0 0.2 0.8 0 0 1 u 16 0 0.9 0.1 0 1 0 u 17 0 0.2 0.8 0 0 1 u 18 0 0 1 0 0 1 0 u 19 0.8 0.2 0 0.5 0.5 0 0.5 0.5 0.2 u 20 0 0.9 0.1 0 1 0 u 21 0 0.2 0.8 0 0 1 u 22 0 0.9 0.1 0 0.9 0.1 0 u 23 0 0.2 0.8 0 0 1 u 24 0 0.9 0.1 0 1 0 ∑ 3.1 10.9 10 2 9.4 12.6 2 1.5 0.4 Lanjutan Tabel 25 U min me, ms min me, hs min he, ls min he, ms min he, hs min ls, le min ls, me min ls, he u 01 0 0 0 0 1 0 0 0 u 02 0.9 0 0 0.1 0 0 0 0 u 03 0 0 0 0 1 0 0 0 u 04 0.5 0 0 0 0 0 0 0 u 05 0.2 0 0 0 0 0.5 0.2 0 u 06 0.9 0 0 0.1 0 0 0 0 u 07 0 0 1 0 0 0 u 08 0.9 0 0 0.1 0 0 0 0 u 09 0 0 0 0 1 0 0 0 u 10 0.5 0.5 0.1 0.1 0 0 0 u 11 0 0 0 0 0 1 0 0 u 12 0 0.9 0 0 0.1 0 0 0 u 13 0 0.2 0 0 0.8 0 0 0 u 14 0 0.9 0.1 0 0 0 u 15 0 0.2 0 0 0.8 0 0 0 u 16 0.9 0 0 0.1 0 0 0 0 u 17 0 0.2 0.8 0 0 0 u 18 0 0 0 0 1 0 0 0 u 19 0.2 0 0 0 0 0.5 0.2 0 u 20 0.9 0 0 0.1 0 0 0 0 u 21 0 0.2 0 0 0.8 0 0 0 u 22 0.9 0.1 0 0.1 0.1 0 0 0 u 23 0 0.2 0 0 0.8 0 0 0 u 24 0.9 0 0 0.1 0 0 0 0 ∑ 7.7 3.4 0 0.8 9.4 2 0.4 0 Lanjutan Tabel 25 U min ms, le min ms, me min ms, he min hs, le min hs, me min hs, he u 01 0 0 0 0 1 u 02 0 0.9 0.1 0 0 0 u 03 0 0 0 0 1 u 04 0.5 0.5 0 0 0 0 u 05 0.5 0.2 0 0 0 0 u 06 0 0.9 0.1 0 0 0 u 07 0 0 1 u 08 0 0.9 0.1 0 0 0 u 09 0 0 0 0 1 u 10 0 0.5 0.1 0.5 0.1 u 11 0 0 0 0 0 u 12 0 0 0 0.9 0.1 u 13 0 0 0 0.2 0.8 u 14 0 0 0.9 0.1 u 15 0 0 0 0.2 0.8 u 16 0 0.9 0.1 0 0 0 u 17 0 0 0.2 0.8 u 18 0 0 0 0 1 u 19 0.5 0.2 0 0 0 0 u 20 0 0.9 0.1 0 0 0 u 21 0 0 0 0.2 0.8 u 22 0 0.9 0.1 0.1 0.1 u 23 0 0 0 0.2 0.8 u 24 0 0.9 0.1 0 0 0 ∑ 1.5 7.7 0.8 0 3.4 9.4 ϕ ℜ {le}, {ls} = m in , 2 1, m in 2 le ls ls = = ∑ ∑ ϕ ℜ {ls},{me} = m in , 0.4 0 .04, m in 10 .9 ls m e m e = = ∑ ∑ ϕ ℜ {me},{ls} = m in , 0 .4 0 .2, m in 2 m e ls ls = = ∑ ∑ ϕ ℜ {ms},{me}= m in , 7.7 0.71, m in 10 .9 m s m e m e = = ∑ ∑ ϕ ℜ {he},{ls} = m in , 0, m in 2 he ls ls = = ∑ ∑ ϕ ℜ {hs},{me} = m in , 3.4 0.31, m in 10.9 hs m e m e = = ∑ ∑ ϕ ℜ {ls},{le}= m in , 2 0.6 5, m in 3 .1 ls le le = = ∑ ∑ ϕ ℜ {le},{hs}= m in , 0, m in 12.6 le h s h s = = ∑ ∑ ϕ ℜ {ms},{le}= m in , 1.5 0.4 8, m in 3.1 m s le le = = ∑ ∑ ϕ ℜ {me},{hs} = m in , 3 .4 0.27, m in 12 .6 m e hs h s = = ∑ ∑ ϕ ℜ {hs},{le} = m in , 0, m in 3 .1 hs le le = = ∑ ∑ ϕ ℜ {he},{hs}= m in , 9.4 0 .75, m in 12 .6 h e hs hs = = ∑ ∑ ϕ ℜ {le},{ms}= m in , 1.5 0 .16, m in 9 .4 le m s m s = = ∑ ∑ ϕ ℜ {ls},{he} = m in , 0, m in 10 ls he he = = ∑ ∑ ϕ ℜ {me},{ms}= m in , 7.7 0.8 2, m in 9 .4 m e m s m s = = ∑ ∑ ϕ ℜ {ms},{he} = m in , 0.8 0.0 8, m in 10 m s he he = = ∑ ∑ ϕ ℜ {he},{ms} = m in , 0.8 0 .09, m in 9.4 h e m s m s = = ∑ ∑ ϕ ℜ {hs},{he} = m in , 9 .4 0.94 . m in 1 0 h s he he = = ∑ ∑ LAMPIRAN 4 Hasil Transformasi Tabel 8 untuk Contoh 5 Tabel 26 Relasi RN=j,C=m,G=A N = j C = m G = A minN= j,G=A minN=j,C=m minN=j,C=m,G=A 1 1 1 1 1 1 1 0 0 1 0 0 0 1 0 0 0 1 0 0 1 ∑ = 3 ∑ = 2 ∑ = 3 ∑ = 1 ∑ = 1 ∑ = 1 1. 0 .1 m in , 1 ˆ G= | N= , m in 3 G N Q A j N ℜ = = ∑ ∑ 2. 0.1 min , , 1 ˆ G=A | N=j, C=m 1. min , 1 G N C Q N C ℜ = = = ∑ ∑ LAMPIRAN 5 Hasil Transformasi Tabel 9 untuk Contoh 6 Tabel 27 Relasi RN, G N = j N = p G = A G = B Min G=A,N=j min G=B, N=j min G=A, N=p min G=B,N=p 1 0 1 0 1 1 0 0 1 1 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1 0 1 1 0 1 ∑ = 3 ∑ = 3 ∑ = 3 ∑ = 3 ∑ = 1 ∑ = 2 ∑ = 2 ∑ = 1 1. 0 .1 m in = , = 1 ˆ G= | N= , m in = 3 G A N j Q A j N j ℜ = = ∑ ∑ 2. 0.1 m in = , = 2 ˆ G= | N= , m in = 3 G B N j Q B j N j ℜ = = ∑ ∑ 3. 0.1 m in = , = 1 ˆ G= | N= , m in = 3 G B N p Q B p N p ℜ = = ∑ ∑ 4. 0.1 m in = , = 2 ˆ G= | N = , m in = 3 G A N p Q A p N p ℜ = = ∑ ∑ LAMPIRAN 6 Hasil Transformasi Tabel 10 untuk Contoh 6 Tabel 28 Relasi RC, G C=m C =b C =k G = A G = B min C=m,G=A min C=m,G=B min C=b,G=A 1 0 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 1 0 1 0 1 0 1 ∑ = 2 ∑ = 2 ∑ = 2 ∑ = 3 ∑ = 3 ∑ = 1 ∑ = 2 ∑ = 2 Lanjutan Tabel 28 min C=’b’,G=’B’ min C=b,G=A min C=b,G=B 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 ∑ = 1 ∑ = 2 ∑ = 1 1. 0.1 m in = , = 1 ˆ = | = , m in = 2 G A C m Q G A C m C m ℜ = = ∑ ∑ 2. 0 .1 m in = , = 1 ˆ = | = , m in = 2 G A C b Q G A C b C b ℜ = = ∑ ∑ 3. 0.1 m in = , = 1 ˆ = | = , m in = 2 G A C k Q G A C k C k ℜ = = ∑ ∑ 4. 0.1 m in = , = 1 ˆ = | = , m in = 2 G B C m Q G B C m C m ℜ = = ∑ ∑ 5. 0.1 m in = , = 1 ˆ = | = , m in = 2 G B C k Q G B C k C k ℜ = = ∑ ∑ 6. 0.1 m in = , = 1 ˆ = | = . m in = 2 G B C b Q G B C b C b ℜ = = ∑ ∑ LAMPIRAN 7 Hasil Transformasi Tabel 4 untuk Contoh 7 dan Contoh 8 Tabel 29 RU, he, hs U he hs minhe,hs maxhe,hs u 01 1 1 1 1 u 02 0.1 0 0 0.1 u 03 1 1 1 1 u 04 0 0 u 05 0 0 u 06 0.1 0 0 0.1 u 07 1 1 1 1 u 08 0.1 0 0 0.1 u 09 1 1 1 1 u 10 0.1 0.5 0.1 0.5 u 11 0 0 u 12 0.1 1 0.1 1 u 13 0.8 1 0.8 1 u 14 0.1 1 0.1 1 u 15 0.8 1 0.8 1 u 16 0.1 0 0.1 u 17 0.8 1 0.8 1 u 18 1 1 1 1 u 19 0 0 u 20 0.1 0 0.1 u 21 0.8 1 0.8 1 u 22 0.1 0.1 0.1 0.1 u 23 0.8 1 0.8 1 u 24 0.1 0 0.1 ∑ 10 12.6 9.4 13.2 LAMPIRAN 8 Perhitungan dengan Menggunakan Persamaan 27 yaitu Peluang Query untuk Objek u i dengan Diberikan “he AND hs” ˆ Q ℜ u 1 |he,hs = 1 min , , 1 0.106, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 2 |he,hs = 2 min , , 0, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 3 |he,hs = 3 min , , 1 0.106, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 4 |he,hs = 4 min , , 0, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 5 |he,hs = 5 min , , 0, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 6 |he,hs = 6 min , , 0, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 7 |he,hs = 7 min , , 1 0.106, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 8 |he,hs = 8 min , , 0, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 9 |he,hs = 9 min , , 1 0.106, 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 10 |he,hs = 10 min , , 0.1 0.0106, 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 12 |he,hs = 12 min , , 0.1 0.0106, 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 13 |he,hs = 13 min , , 0.8 0.085, 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 14 |he,hs = 14 min , , 0.1 0.0106 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 15 |he,hs = 15 min , , 0.8 0.085, 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 16 |he,hs = 16 min , , 0, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 17 |he,hs = 17 min , , 0.8 0.085, 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 18 |he,hs = 18 min , , 1 0.106, 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 19 |he,hs = 19 min , , 0, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 20 |he,hs = 20 min , , 0, min , 9.4 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 21 |he,hs = 21 min , , 0.8 0.085, 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 22 |he,hs = 22 min , , 0.1 0.0106, 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 23 |he,hs = 23 min , , 0.8 0.085, 9.4 min , u he hs he hs = = ∑ ∑ ˆ Q ℜ u 24 |he,hs = 24 min , , 0. min , 9.4 u he hs he hs = = ∑ ∑ LAMPIRAN 9 Perhitungan dengan Menggunakan Persamaan 27 yaitu Peluang Query untuk “he AND hs” dengan Diberikan Objek u i ˆ Q ℜ he,hs |u 1 = 1 1 min , , 1 1, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 2 = 2 2 min , , 0, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 3 = 3 3 min , , 1 1, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 4 = 4 4 min , , 0, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs| u 5 = 5 5 min , , 0, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 6 = 6 6 min , , 0, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 7 = 7 7 min , , 1 1, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 8 = 8 8 min , , 0, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 9 = 9 9 min , , 1 1, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 10 = 10 10 min , , 0.1 0.1, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 11 = 10 10 min , , 0, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 12 = 12 12 min , , 0.1 0.1, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 13 = 13 13 min , , 0.8 0.8, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 14 = 14 14 min , , 0.1 0.1, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 15 = 15 15 min , , 0.8 0.8, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 16 = 16 16 min , , min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 17 = 17 17 min , , 0.8 0.8, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 18 = 18 18 min , , 1 1, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 19 = 19 19 min , , 0, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 20 = 20 20 min , , 0, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 21 = 21 21 min , , 0.8 0.8, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 22 = 22 22 min , , 0.1 0.1, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 23 = 23 23 min , , 0.8 0.8, min 1 u he hs u = = ∑ ∑ ˆ Q ℜ he,hs |u 24 = 24 24 min , , 0. min 1 u he hs u = = ∑ ∑ LAMPIRAN 10 Perhitungan dengan Menggunakan Persamaan 30 yaitu Peluang Query Objek u i dengan Diberikan “he OR hs” ˆ Q ℜ u 1 |he,hs = 1 maxmin , , 1 0.076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 2 |he,hs = 2 maxmin , , 0.1 0.0076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 3 |he,hs = 3 maxmin , , 1 0.076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 4 |he,hs = 4 maxmin , , 0, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 5 |he,hs = 5 maxmin , , 0, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 6 |he,hs = 6 maxmin , , 0.1 0.0076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 7 |he,hs = 7 maxmin , , 1 0.076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 8 |he,hs = 8 maxmin , , 0.1 0.0076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 9 |he,hs = 9 maxmin , , 1 0.076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 10 |he,hs = 10 maxmin , , 0.5 0.0379, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 11 |he,hs = 11 maxmin , , 0, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 12 |he,hs = 12 maxmin , , 1 , max , 13.2 u he hs he hs = ∑ ∑ ˆ Q ℜ u 13 |he,hs = 13 maxmin , , 1 0.076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 14 |he,hs = 14 maxmin , , 1 0.076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 15 |he,hs = 15 min , , 1 0.076, min , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 16 |he,hs = 16 maxmin , , 0.1 0.0076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 17 |he,hs = 17 maxmin , , 1 0.076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 18 |he,hs = 18 maxmin , , 1 0.076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 19 |he,hs = 19 maxmin , , 0, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 20 |he,hs = 20 maxmin , , 0.1 0.0076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 21 |he,hs = 21 maxmin , , 1 0.076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 22 |he,hs = 22 maxmin , , 0.1 0.0076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 23 |he,hs = 23 maxmin , , 1 0.076, max , 13.2 u he hs he hs = = ∑ ∑ ˆ Q ℜ u 24 |he,hs = 24 maxmin , , 0.1 0.0076. max , 13.2 u he hs he hs = = ∑ ∑ LAMPIRAN 11 Perhitungan dengan Menggunakan Persamaan 30 yaitu Peluang Query untuk “he OR hs” dengan Diberikan Objek u i ˆ Q ℜ he,hs |u 1 = 1 1 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ he,hs |u 2 = 2 2 maxmin , , max u he hs u ∑ ∑ = 0.1, ˆ Q ℜ he,hs |u 3 = 3 3 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ he,hs |u 4 = 4 4 maxmin , , max u he hs u ∑ ∑ = 0, ˆ Q ℜ he,hs |u 5 = 5 5 maxmin , , max u he hs u ∑ ∑ = 0, ˆ Q ℜ he,hs |u 6 = 6 6 maxmin , , max u he hs u ∑ ∑ = 0.1, ˆ Q ℜ he,hs |u 7 = 7 7 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ he,hs |u 8 = 8 8 maxmin , , max u he hs u ∑ ∑ = 0.1, ˆ Q ℜ he,hs |u 9 = 9 9 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ he,hs |u 10 = 10 10 maxmin , , max u he hs u ∑ ∑ = 0.5, ˆ Q ℜ he,hs |u 11 = 11 11 maxmin , , max u he hs u ∑ ∑ = 0, ˆ Q ℜ he,hs |u 12 = 12 12 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ he,hs |u 13 = 13 13 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ he,hs |u 14 = 14 14 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ he,hs |u 15 = 15 15 min , , min u he hs u ∑ ∑ = 1, ˆ Q ℜ he,hs |u 16 = 16 16 maxmin , , max u he hs u ∑ ∑ = 0.1, ˆ Q ℜ he,hs |u 17 = 17 17 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ he,hs |u 18 = 18 18 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ he,hs |u 19 = 19 19 maxmin , , max u he hs u ∑ ∑ = 0, ˆ Q ℜ he,hs |u 20 = 20 20 maxmin , , max u he hs u ∑ ∑ = 0.1, ˆ Q ℜ u 21 |he,hs = 21 21 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ u 22 |he,hs = 22 22 maxmin , , max u he hs u ∑ ∑ = 0.1, ˆ Q ℜ u 23 |he,hs = 23 23 maxmin , , max u he hs u ∑ ∑ = 1, ˆ Q ℜ u 24 |he,hs = 24 24 maxmin , , max u he hs u ∑ ∑ = 0.1. APLIKASI RELASI PELUANG BERSYARAT FUZZY PADA SISTEM INFORMASI FUZZY NIKEN WIDIASTUTI DEPARTEMEN MATEMATIKA FAKULTAS MATEMATIKA DAN ILMU PENGETAHUN ALAM INSTITUT PERTANIAN BOGOR BOGOR 2008 ABSTRAK NIKEN WIDIASTUTI . Aplikasi Relasi Peluang Bersyarat Fuzzy pada Sistem Informasi Fuzzy. Dibimbing oleh SRI NURDIATI dan N. K. KUTHA ARDANA. Dalam kehidupan sehari-hari seringkali ditemui suatu fenomena yaitu data mengandung sesuatu yang tidak akurat. Data yang tidak akurat tersebut dapat berupa kata-kata manusia yang bersifat relatif. Pada kasus ini, himpunan fuzzy dapat digunakan untuk merepresentasikan data yang tidak akurat tersebut dengan derajat keakuratan data yang berbeda. Pada tulisan ini diperkenalkan relasi peluang bersyarat fuzzy atau fuzzy conditional probability relations FCPR yang digunakan untuk merepresentasikan relasi kemiripan antara dua himpunan fuzzy yang tidak perlu simetris atau transitif. Konsep FCPR yang dibahas difokuskan pada relasi kemiripan yang lemah yang merupakan tipe khusus pada relasi fuzzy biner dengan perumuman relasi kemiripan. Sistem informasi fuzzy yang digunakan adalah tabel data fuzzy sederhana yang merupakan aplikasi dari knowledge discovery and data mining KDD. Dengan memanfaatkan derajat dari dasar kemiripan FCPR, aplikasi FCPR yaitu konsep Į-objek redundan, ketergantungan atribut, pendekatan data reduksi dan proyeksi, dan pendekatan data query. Perhitungan berdasarkan FCPR berguna untuk menentukan derajat kemiripan dari dua kata yang tidak perlu simetris atau transitif. Konsep Į-objek redundan sangat penting untuk mereduksi angka dari aturan keputusan dengan adanya tabel keputusan. Konsep ketergantungan atribut berdasarkan FCPR sangat penting untuk manganalisa ketergantungan dari atribut. Aplikasi pendekatan data reduksi dan proyeksi digunakan untuk menemukan relasi di antara anak himpunan fuzzy dari partisi fuzzy dan menghasilkan fuzzy integrity constraints. Aplikasi pendekatan data query digunakan untuk menghasilkan relasi fuzzy query dengan adanya tabel keputusan. Kata kunci : Relasi peluang bersyarat fuzzy, fungsi ketergantungan fuzzy FFD, fuzzy integrity constraints FIC, knowledge discovery and data mining KDD, data query. ABSTRACT NIKEN WIDIASTUTI . The Applications of Fuzzy Conditional Probability Relations in Fuzzy Information Systems. Supervised by SRI NURDIATI and N. K. KUTHA ARDANA. In our daily life we often find a phenomenon related to an imprecise data. The imprecise data can be in the form of relative human words. In this case, fuzzy sets can be used to represent the imprecise data in which preciseness degrees of data are intuitively different. This paper introduced a fuzzy conditional probability relations FCPR which is used to represent a similarity relation between two fuzzy sets, The two fuzzy sets may not necessarily be symmetric or transitive. The concept of FCPR which is explained in this paper was focused on a weak relation similarity which turned out to be a special type of fuzzy binary relation generalizing similarity relation. Fuzzy information system which is used in this paper was a simple fuzzy data table that was an application of knowledge discovery and data mining KDD. By using degrees of similarity FCPR, the application of FCPR consists of Į-redundant object concept, a concept of dependency of attribute, approximate data reduction and projection, and approximate data query. Calculation based on FCPR is used to determine degrees of similarity which may not necessarily be symmetric or transitive. The concept of Į -redundant object is very important for the purpose of reducing the number of decision rules concerning a decision table. The concept of dependency attribute based on the FCPR is very important for purpose of analyzing dependency of attribute. Application of approximate data reduction and projection is used to find relations among fuzzy subsets as results of fuzzy partition and provide fuzzy integrity constraints. Application of approximate data query is used to design a fuzzy query relation in which the present the decision table. Keywords : F uzzy conditional probability relations , fuzzy functional dependency FFD, fuzzy integrity constraints FIC, knowledge discovery and data mining KDD, data query. I PENDAHULUAN

1.1 Latar Belakang

Kata-kata manusia pada data mempunyai perbedaan batas pengertian. Beberapa kata mungkin mempunyai arti yang lebih umum dibandingkan dengan yang lainnya. Derajat kemiripan dari dua kata tidak perlu simetris atau transitif. Sebagai contoh, warna “merah” mempunyai pengertian yang lebih umum dan lebih luas dibandingkan dengan warna “merah tua” yang mempunyai arti lebih spesifik. Kata “merah” mempunyai interval yang lebih luas daripada kata “merah tua” sehingga interval dari pengertian warna adalah berbeda untuk dua kata. Biasanya kalimat “merah tua seperti merah” lebih benar dan biasa digunakan daripada kalimat “merah seperti merah tua”. Selain itu, dapat dikatakan bahwa derajat kemiripan dari “merah dengan diberikan merah tua” adalah berbeda dengan “merah tua dengan diberikan merah”. Dalam kehidupan sehari-hari seringkali ditemui suatu fenomena yaitu data mengandung sesuatu yang tidak akurat dengan derajat keakuratan data yang berbeda. Data yang tidak akurat tersebut dapat berupa kata-kata manusia yang bersifat relatif. Himpunan fuzzy dapat merepresentasikan data yang tidak akurat tersebut. Pada tulisan ini, relasi peluang bersyarat fuzzy atau fuzzy conditional probability relations FCPR digunakan untuk merepresentasikan relasi kemiripan antara dua himpunan fuzzy yang tidak perlu simetris atau transitif. Konsep dari FCPR akan difokuskan pada relasi kemiripan yang lemah yaitu relasi fuzzy biner dengan perumuman relasi kemiripan Zadeh 1970 dalam Intan dan Mukaidono 2004. Sistem informasi fuzzy yang digunakan pada tulisan ini adalah tabel data fuzzy sederhana yang merupakan aplikasi dari knowledge discovery and data mining KDD. Pertama, akan diperkenalkan FCPR dari dua himpunan fuzzy pada sistem informasi fuzzy yang diberikan. Kemudian, dengan memanfaatkan derajat kemiripan dari FCPR, maka tulisan ini akan memperkenalkan aplikasi FCPR pada sistem informasi fuzzy yang diberikan. Aplikasi tersebut adalah konsep α-objek redundan, ketergantungan atribut, pendekatan data reduksi dengan operator proyeksi, dan aplikasi yang terakhir adalah pendekatan data query yang berdasarkan pada input bergantung dan input bebas.

1.2 Tujuan

Tujuan penulisan ini adalah : 1. Merekonstruksi FCPR dari dua himpunan fuzzy pada sistem informasi fuzzy. 2. Merekonstruksi α-objek redundan berdasarkan pada FCPR pada sistem informasi fuzzy. 3. Merekonstruksi ketergantungan atribut berdasarkan pada FCPR pada sistem informasi fuzzy. 4. Merekonstruksi pendekatan data reduksi dengan operator proyeksi, dan pendekatan data query pada sistem informasi fuzzy.

1.3 Ruang Lingkup

Ruang lingkup penulisan ini adalah : 1. Tulisan ini dibatasi pada rujukan utama jurnal Fuzzy Conditional Probability Relations and Their Applications in Fuzzy Information Systems Intan dan Mukaidono 2004. 2. Sistem informasi fuzzy yang digunakan dibatasi pada tabel data fuzzy yang sederhana ukuran tabel tidak besar. 3. Konsep FCPR hanya difokuskan pada relasi kemiripan yang lemah yaitu relasi fuzzy biner dengan perumuman relasi kemiripan. II TINJAUAN PUSTAKA

2.1 Himpunan Crisp dan Himpunan Fuzzy Definisi 1 Himpunan Crisp

Himpunan crisp A didefinisikan oleh elemen-elemen yang ada pada himpunan itu. Jika a A ∈ , maka nilai yang berhubungan dengan a adalah 1. Namun jika , a A ∉ maka nilai yang berhubungan dengan a adalah 0. Keanggotaan himpunan crisp selalu dapat dikategorikan secara penuh tanpa ada ambiguitas. Kusumadewi 2002 I PENDAHULUAN

1.1 Latar Belakang