TUGAS 3 DATA MINING METODE KNN MIN-MAX-NORMALIZATION Oleh: RAHMANITA M. KARIMA(0810963023) MUTYA FANI ATSOMYA (0810963019) RIA KURNIANTI(0810963065) RIZKHY AYUNING TYAS(0810963067) PROGRAM STUDI ILMU KOMPUTER JURUSAN MATEMATIKAFAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNIVERSITAS BRAWIJAYA MALANG 2011 TUGAS DATA MINING 2 CONTOH DATA TINGGI BADAN MODEL INDONESIA NONAMATINGGI (cm) JENIS KELAMIN 0yuna162Tinggi 1so-young160Tinggi 2jesica173Tinggi 3tifani168Tinggi 4taeyon150Pendek 5yuri166Tinggi 6ria155Pendek 7dinda161Tinggi 8sunny162,5Tinggi 9mumy152Pendek 10adit165Tinggi 11wicaksono166Tinggi 12akbar156Pendek 13pono178Tinggi 14rahmanita169Tinggi 15maulidia167Tinggi 16karima162Tinggi 17mutya159Pendek 18rizkhy ayuning151,5Pendek 19atsomya152Pendek 20budi153Pendek PERHITUNGAN 1. NORMALISASI MIN-MAX Min(X) =150 Max (X) =178 Range (X) =Min (X) Min (X) =178 150 = 28 NO HASIL NORMALISASI 00,428571429 10,357142857 20,821428571 30,642857143 40 50,571428571 TUGAS DATA MINING 3 60,178571429 70,392857143 80,446428571 90,071428571 100,535714286 110,571428571 120,214285714 131 140,678571429 150,607142857 160,428571429 170,321428571 180,053571429 190,071428571 200,107142857 2. JARAK ANTAR VARIABELRumus jarak: D(new,object)=SQRT(POWER(normalisasi_new - normalisasi_object)) Misalnya data yang akan di bandingkan adalah 165. Di normalisasinya:

=0,535714285 NOObjectJarak 0d(new,yuna)0,107142857 1d(new,so-young)0,178571429 2d(new,jesica)0,285714286 3d(new,tifani)0,107142857 4d(new,taeyon)0,535714286 5d(new,yuri)0,035714286 6d(new,ria)0,357142857 7d(new,dinda)0,142857143 8d(new,sunny)0,089285714 9d(new,mumy)0,464285714 10d(new,adit)0 11d(new,wicaksono)0,035714286 12d(new,akbar)0,321428571 13d(new,pono)0,464285714 14d(new, rahmanita)0,142857143 15d(new,maulidia)0,071428571 TUGAS DATA MINING 4 16d(new,karima)0,107142857 17d(new,mutya)0,214285714 18 d(new,rizkhy ayuning)0,482142857 19d(new,atsomya)0,464285714 20d(new,budi)0,428571429

3. SIMPLE UNWEIGHT VOTING Misalkan K=2 Langkah-Langkah: a. Sorting NODISTANCESORTING 00,1071428570 10,1785714290,035714286 20,2857142860,035714286 30,1071428570,071428571 40,5357142860,089285714 50,0357142860,107142857 60,3571428570,107142857 70,1428571430,107142857 80,0892857140,142857143 90,4642857140,142857143 1000,178571429 110,0357142860,214285714 120,3214285710,285714286 130,4642857140,321428571 140,1428571430,357142857 150,0714285710,428571429 160,1071428570,464285714 170,2142857140,464285714 180,4821428570,464285714 190,4642857140,482142857 200,4285714290,535714286 karena k=2 , maka diambil 2 terendah dari hasil sorting maka hasilnya adalah 0 dan 0,035714286. TUGAS DATA MINING 5 SOURCE CODE KNN_nor mal i sasi _unwei ght . j ava packaget ugasdat mi nknn_nor m_unwei ght ; i mpor t j avax. swi ng. J Text Ar ea; publ i ccl assKNN_nomal i sasi _unwei ght ext endsj avax. swi ng. J Fr ame{ publ i cKNN_nomal i sasi _unwei ght ( ) { i ni t Component s( ) ;b1. set Enabl ed( f al se) ;b2. set Enabl ed( f al se) ;b3. set Enabl ed( f al se) ;kedekat an. set Enabl ed( f al se) ;r eset . set Enabl ed( f al se) ;} publ i cSt r i ngnama[ ] ={"yuna", "so- young", "j esi ca", "t i f ani ", "t aeyon","yur i ", "r i a", "di nda", "sunny", "mumy", "adi t ", "wi caksono", "akbar ","pono", "r ahmani t a", "maul i da", "kar i ma", "Mut ya", "Ri zkhy Ayuni ng", "At somya", "Budi " };publ i cdoubl et i nggi [ ] ={162, 160, 173 , 168, 150, 166, 155, 161, 162. 5, 152,165, 166, 156, 178, 169,167, 162, 159, 151. 5, 152, 153 };publ i cSt r i ngket [ ] ={"Ti nggi ", "Ti nggi ", "Ti nggi ", "Ti nggi ", "Pendek","Ti nggi ", "Pendek", "Ti nggi ", "Ti nggi ", "Pendek","Ti nggi ", "Ti nggi ", "Pendek", "Ti nggi ", "Ti nggi ", "Ti nggi ", "Ti nggi ", "Pendek", "Pendek", "Pendek", "Pendek" };publ i cdoubl emsk1;publ i ci nt k;publ i cst at i cTugasDat mi nh=new TugasDat mi n( ) ; publ i cdoubl emx=h. maksi mum( t i nggi ) ;publ i cdoubl emn=h. mi ni mum( t i nggi ) ;publ i cSt r i ngkl asi f i kasi ;publ i cSt r i ngr st ="";publ i ci nt count T= 0;publ i ci nt count P= 0;publ i cdoubl er =mx- mn;publ i ci nt ah=t i nggi . l engt h;publ i cdoubl ej ar ak_dengan_new [ ] =new doubl e[ ah] ; publ i cdoubl enor mal i sasi ( doubl ea) { doubl enor m;nor m=( a- mn) / r ;r et ur nnor m;} publ i cst at i cvoi dsss_ur ut ( doubl ej [ ] , St r i ngk[ ] ) { i nt n= j . l engt h;f or ( i nt pass=1; pass< n; pass++) { f or ( i nt i =0; i < n- pass; i ++) { i f ( j [ i ] > j [ i +1] ) { doubl et emp= j [ i ] ;j [ i ] = j [ i +1] ;j [ i +1] = t emp;/ / sor t i ngkat egor i ber dasar kanj ar ak TUGAS DATA MINING 6 St r i ngaduh=k[ i ] ;k[ i ] =k[ i +1] ;k[ i +1] =aduh;} } } / / r et ur nx;} publ i cvoi dcl assi f i cat i on( J Text Ar eaj j ){ sss_ur ut ( j ar ak_dengan_new, ket ) ;k=I nt eger . par seI nt ( masukan_k. get Text ( ) ) ;/ / sss_ur ut ( j ar ak_dengan_new, ket ) ;j j . append( "Dengank= "+k+", maka\ n") ; f or ( i nt b= 0; b