SUPERVISED S U H E N G K I C TEK. 01 Decision Tree 02 Random Forest OUTLINEOUTLINE.

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1 SUPERVISED S U H E N G K I C5602202004 TEK

2 01 Decision Tree 02 Random Forest OUTLINEOUTLINE

3 DECISION TREE LEARNING COMPONENTS ROOT NODE Mengacu pada awal pohon keputusan dengan pemisahan maksimum (Informasi Gain) NODE Node adalah kondisi dengan beberapa hasil di pohon LEAF Ini adalah keputusan akhir (titik akhir dari sebuah node dari kondisi (pernyataan)

4 DECISION TREE LEARNING COMPONENTS

5 DECISION TREE LEARNING (DTL)

6

7 Memilih atribut yang paling berguna untuk mengklasifikasikan contoh. informasi yang diperoleh, Penguat informasi, Gini index, Rasio kemungkinan statistik chi-squared, dan Kriteria DKM. PEMILIHAN ATRIBUT METODE UNTUK MEMILIH ATRIBUT PEMISAH MEMILIH ATRIBUT PEMISAH

8 S E T I A P N O D E M E N G H A S I L K A N D A T A M A K S I M U M D I S E T I A P P E M I S A H A N Y A N G D A P A T D I C A P A I O L E H I G INFORMATION GAIN (IG)

9 I N I D A P A T D I H I T U N G D E N G A N M E N G G U N A K A N T I N D A K A NK E T I D A K U R S A B A S A N D A R I S E T I A P P E M I S A H A NI N I D A P A T D I H I T U N G D E N G A N M E N G G U N A K A N T I N D A K A NK E T I D A K U R S A B A S A N D A R I S E T I A P P E M I S A H A N IMPURITY METRICS

10 PRINSIP PEMISAHAN NODE Node root dibagi untuk mendapatkan keuntungan info maksimum. Peningkatan node di pohon menyebabkan overfitting. Pemisahan berlanjut sampai masing- masing daun murni (salah satu kemungkinan hasil ) Pemangkasan juga dapat dilakukan yang berarti penghapusan cabang yang menggunakan fitur yang sangat penting. Indeks Gini ≅ Entropi Jika distribusi seragam, entropi adalah 1

11 A C T I V I T YA C T I V I T Y P A R E N T D A T A S E T --- > 4 0 I T E M D I F I T U R 1 D A N 4 0 I T E M D I F I T U R 2 A N A KA N A K1 → 3 0I T E MI T E MF I T U RF I T U R1D A ND A N1 01 0I T E MI T E MD ID I F I T U RF I T U R2 A N A KA N A K2 → 1 0I T E MI T E MF I T U RF I T U R1D A ND A N3 03 0I T E MI T E MD ID I F I T U RF I T U R2 SPLIT A K U M P U L A N D A T A --- > 4 0 I T E M D I F I T U R 1 D A N 4 0 I T E M D I F I T U R 2 A N A K 1 → 2 0 I T E M D I F I T U R 1 D A N 4 0 I T E M D I F I T U R 2 A N A K 2 → 2 0 I T E M F I T U R 1 D A N 0 I T E M D I F I T U R 2 SPLIT B A C T I V I T Y

12 I E ( D P ) = 1 − 4 0 / 8 0 = 1 − 0. 5 = 0. 5I E ( D P ) = 1 − 4 0 / 8 0 = 1 − 0. 5 = 0. 5 B : I E ( D L E F T ) = 1 − 4 0 / 6 0 = 1 − 2 3 = 1 / 3B : I E ( D R I G H T ) = 1 − 2 0 / 2 0 = 1 − 1 = 0B : I G E = 0. 5 − 6 0 / 8 0 × 1 3 − 2 0 / 8 0 × 0 = 0. 5 − 0. 2 5 − 0 = 0. 2 5B : I E ( D L E F T ) = 1 − 4 0 / 6 0 = 1 − 2 3 = 1 / 3B : I E ( D R I G H T ) = 1 − 2 0 / 2 0 = 1 − 1 = 0B : I G E = 0. 5 − 6 0 / 8 0 × 1 3 − 2 0 / 8 0 × 0 = 0. 5 − 0. 2 5 − 0 = 0. 2 5 CLASSIFICATION ERROR =1−MAXPJ

13 I G ( D P ) = 1 −(( 4 0 / 8 0 ) 2 +4 0 / 8 0 ) 2 ) = 1 −( 0. 5 2 + 0. 5 2 ) = 0. 5I G ( D P ) = 1 −(( 4 0 / 8 0 ) 2 +4 0 / 8 0 ) 2 ) = 1 −( 0. 5 2 + 0. 5 2 ) = 0. 5 A : I G ( D L E F T ) = 1 −(( 3 0 / 4 0 ) 2 +( 1 0 / 4 0 ) 2 ) = 1 −( 9 / 1 6 + 1 / 1 6 ) = 3 8 = 0. 3 7 5 A : I G ( D R I G H T ) = 1 −(( 1 0 / 4 0 ) 2 +( 3 0 / 4 0 ) 2 ) = 1 −( 1 / 1 6 + 9 / 1 6 ) = 3 8 = 0. 3 7 5 A : I G = 0. 5 − 4 0 / 8 0 × 0. 3 7 5 − 4 0 / 8 0 × 0. 3 7 5 = 0. 1 2 5 B : I G ( D L E F T ) = 1 −(( 2 0 / 6 0 ) 2 +( 4 0 / 6 0 ) 2 ) = 1 −( 9 / 1 6 + 1 / 1 6 ) = 1 − 5 9 = 0. 4 4 B : I G ( D R I G H T ) = 1 −(( 2 0 / 2 0 ) 2 +( 0 / 2 0 ) 2 ) = 1 −( 1 + 0 ) = 1 − 1 = 0 B : I G = 0. 5 − 6 0 / 8 0 × 0. 4 4 − 0 = 0. 5 − 0. 3 3 = 0. 1 7 GINI=1−∑P2 J

14 I H ( D P ) =−( 0. 5 L O G 2 ( 0. 5 ) + 0. 5 L O G 2 ( 0. 5 ) ) = 1 A : I H ( D L E F T ) =−( 3 0 / 4 0 L O G 2 ( 3 0 / 4 0 ) + 1 0 / 4 0 L O G 2 ( 1 0 / 4 0 ) ) = 0. 8 1 A : I H ( D R I G H T ) =−( 1 0 / 4 0 L O G 2 ( 1 0 4 0 ) + 3 0 / 4 0 L O G 2 ( 3 0 / 4 0 ) ) = 0. 8 1 A : I G H = 1 − 4 0 / 8 0 × 0. 8 1 − 4 0 / 8 0 × 0. 8 1 = 0. 1 9 B : I H ( D L E F T ) =−( 2 0 / 6 0 L O G 2 ( 2 0 / 6 0 ) + 4 0 / 6 0 L O G 2 ( 4 0 / 6 0 ) ) = 0. 9 2 B : I H ( D R I G H T ) =−( 2 0 / 2 0 L O G 2 ( 2 0 / 2 0 ) + 0 ) = 0 B : I G H = 1 − 6 0 / 8 0 × 0. 9 2 − 2 0 / 8 0 × 0 = 0. 3 1 ENTROPY=−∑PJ LOG2PJ

15 ENTROPIBERSKALA=ENTROPI/ 2 INDEKSGINIBERPERANTARA NILAI- NILAIKETIDAKMUNGHARANBERBOHONG ANTARAKESALAHANKLASIFIKASI DANENTROPI. PERBANDINGAN SEMUA METRIKS IMPURITAS

16 MUDAHDIMENGERTI,DITAFSIRKAN,DIVISUALISASIKAN. INIEFEKTIFUNTUKDIGUNAKANDALAMHASILDATANUMERIKDAN KATEGORIS. MEMBUTUHKANSEDIKITUSAHADARIPENGGUNAUNTUKPERSIAPAN DATA. HUBUNGANNONLINEARANTARAPARAMETERTIDAKMEMPENGARUHI POHONKINERJA. MAMPUMENANGANIATRIBUTYANGTIDAKRELEVAN(GAIN=0) PROPRO

17 DAPATMEMBUATPOHONKOMPLEKSDENGANKEDALAMAN MAKSMAKSIMUM. TIDAKSTABILKARENAVARIASIKECILDALAMDATAINPUTDAPAT POHONYANGSAMASEKALIBERBEDAUNTUKDIHASILKAN. KARENAINIADALAHALGORITMASERAKAH,MUNGKINTIDAK MENEMUKANPOHONTERBAIKSECARAGLOBALUNTUKKUMPULANDATA. KONTRAKONTRA

18 P E N G K L A S I F I K A S IA N S A M B E LM E N G G U N A K A NB A N Y A KM O D E LP O H O NK E P U T U S A N.D A P A TD I G U N A K A NU N T U KK L A S I F I K A S IA T A UR E G R E S I.A K U R A S ID A NK E P E N T I N G A NV A R I A B E LI N F O R M A S ID I B E R I K A ND E N G A NH A S I L N Y A.P E N G K L A S I F I K A S IA N S A M B E LM E N G G U N A K A NB A N Y A KM O D E LP O H O NK E P U T U S A N.D A P A TD I G U N A K A NU N T U KK L A S I F I K A S IA T A UR E G R E S I.A K U R A S ID A NK E P E N T I N G A NV A R I A B E LI N F O R M A S ID I B E R I K A ND E N G A NH A S I L N Y A. RANDOM FOREST

19 B I A R K A NJ U M L A HK A S U SP E L A T I H A NM E N J A D IN, D A N J U M L A HV A R I A B E LD A L A MP E N G K L A S I F I K A S IA D A L A H M. J U M L A HMV A R I A B E LI N P U TD I G U N A K A NU N T U K M E N E N T U K A NK E P U T U S A ND IS I M P U LP O H O N ; M H A R U SJ A U HL E B I HS E D I K I TD A R I P A D AM. P I L I HS E TP E L A T I H A NU N T U KP O H O NI N ID E N G A N M E M I L I H N K A L I D E N G A N P E N G G A N T I A N D A R I S E M U A K A S U S P E L A T I H A N N Y A N G T E R S E D I A. G U N A K A N S I S A U N T U K M E M P E R K I R A K A N K E S A L A H A N P O H O N, D E N G A N M E M P R E D I K S I K E L A S M E R E K A. U N T U K S E T I A P N O D E P O H O N, P I L I H V A R I A B E L M S E C A R A A C A K D I M A N A U N T U K M E N D A S A R K A N P E M I S A H A NP E M I S A H A N K E P U T U S A NP A D AN O D EI T U.M E N G H I T U N GT E R B A I KB E R D A S A R K A NK E P U T U S A NP A D AN O D EI T U.M E N G H I T U N GT E R B A I KB E R D A S A R K A N V A R I A B E LMI N ID A L A MS E TP E L A T I H A N.S E T I A PP O H O NT U M B U HS E P E N U H N Y AD A NT I D A KD I P A N G K A S.V A R I A B E LMI N ID A L A MS E TP E L A T I H A N.S E T I A PP O H O NT U M B U HS E P E N U H N Y AD A NT I D A KD I P A N G K A S. ALGORITMA

20 RANDOM FOREST FLOW CHAT

21 INIMENGHASILKANPENGKLASIFIKASIDANPEMBELAJARANYANG SANGATAKURATCEPAT INIBERJALANSECARAEFISIENPADABASISDATABESAR. INIDAPATMENANGANIRIBUANVARIABELINPUTTANPAPENGHAPUSAN VARIABEL. INIMENGHITUNGKEDEKATANANTARAPASANGANKASUSYANGDAPAT DIGUNAKANDALAMPENGELOMPOKAN,MENEMUKANOUTLIERATAU ( DENGANPENSKALAAN)MEMBERIKANTAMPILANDATAYANGMENARIK. INIMENAWARKANMETODEEKSPERIMENTALUNTUKMENDETEKSI INTERAKSIVARIABEL. KEUNTUNGANKEUNTUNGAN

22 RANDOMFORESTMENGGUNAKANINDEKSGINIYANGDIAMBILDARICART SISTEMPEMBELAJARANUNTUKMEMBANGUNPOHONKEPUTUSAN. INDEKSGINIIMPURITYNODEADALAHUKURANYANGPALINGSERING DIPILIHUNTUKMASALAHTIPEKLASIFIKASI.JIKASETDATATBERISI CONTOHDARIKELASN, GINIINDEX,GINI( T)DIDEFINISIKANSEBAGAI: DIMANAPJADALAHFREKUENSIRELATIFKELASJDIT. GINI INDEX

23 JIKASETDATATDIBAGIMENJADIDUASUBSETT 1DANT 2DENGAN UKURANN 1DANN 2MASING- MASING,INDEKSGINIDARISPLIT DATABERISICONTOHDARIKELASN,INDEKSGINI ( T)DIDEFINISIKANSEBAGAI: **NILAIATRIBUTYANGMENYEDIAKANSPLITGINI( T)TERKECILDIPILIH UNTUKDIBAGI SIMPUL. GINI INDEX

24 A C A KD I P I L I HY A N GM E N A R I KK E L U A RS E C A R AA C A KP E N G U M P U L A NA C A KD I P I L I HY A N GM E N A R I KK E L U A RS E C A R AA C A KP E N G U M P U L A N S A M P E LD A R IK U M P U L A ND A T AP E L A T I H A NS E M E N T A R AM E M P E R T A H A N K A NS A M P E LD A R IK U M P U L A ND A T AP E L A T I H A NS E M E N T A R AM E M P E R T A H A N K A N D I S T R I B U S ID I S T R I B U S I K E L A S.K E L A S. S E M U AV A R I A B E LI N P U TT I D A KD I A N G G A PK A R E N AK O M P U T A S IS E M U AV A R I A B E LI N P U TT I D A KD I A N G G A PK A R E N AK O M P U T A S I D I T E N T U K A N.Y A N GS A N G A TB E S A RD A NP E L U A N GT I N G G IU N T U KO V E RF I T T I N G.D I T E N T U K A N.Y A N GS A N G A TB E S A RD A NP E L U A N GT I N G G IU N T U KO V E RF I T T I N G. C A R AK E R J AA L G O R I T M A :C A R AK E R J AA L G O R I T M A : 1.B E N I H1.B E N I H 2. D E N G A NK U M P U L A ND A T AY A N GD I P I L I HI N I,S E K U M P U L A NA T R I B U TA C A KD A R IK U M P U L A ND A T AA S L ID I P I L I HB E R D A S A R K A NP E N G G U N AN I L A IY A N G2. D E N G A NK U M P U L A ND A T AY A N GD I P I L I HI N I,S E K U M P U L A NA T R I B U TA C A KD A R IK U M P U L A ND A T AA S L ID I P I L I HB E R D A S A R K A NP E N G G U N AN I L A IY A N G PENGOPERASIAN RANDOM FOREST

25 C A R AK E R J AA L G O R I T M A : 3.. D A L A MK U M P U L A ND A T AD IM A N AMA D A L A HJ U M L A HT O T A LI N P U TA T R I B U T D A L A MS E TD A T A, H A N Y AA T R I B U TR Y A N GD I P I L I HS E C A R AA C A KU N T U K S E T I A PP O H O ND IM A N AR < M. 4.. A T R I B U TD A R IS E TI N IM E N C I P T A K A NY A N GT E R B A I KS P L I TM E N G G U N A K A N I N D E K SG I N IU N T U KM E N G E M B A N G K A NP O H O NK E P U T U S A NM O D E L. P R O S E S M A S I N G - M A S I N GC A B A N GS A M P A IK O N D I S IP E N G H E N T I A NM A S I N G - M A S I N GC A B A N GS A M P A IK O N D I S IP E N G H E N T I A N B A H W AD A U NN O D EY A N GT E R L A L UK E C I LU N T U KB A H W AD A U NN O D EY A N GT E R L A L UK E C I LU N T U K I N ID I U L A N GU N T U KY A N GM E N Y A T A K A ND I P I S A H K A N.I N ID I U L A N GU N T U KY A N GM E N Y A T A K A ND I P I S A H K A N. PENGOPERASIAN RANDOM FOREST

26 C O N T O HD IB A W A HI N IM E N U N J U K K A NK O N S T R U K S IP O H O NM E N G G U N A K A NS E TD A T AR I N G K A S.H A N Y AD U AD A R IE M P A TA T R I B U TA S L IY A N GD I P I L I HU N T U KK O N S T R U K S IP O H O NI N I.C O N T O HD IB A W A HI N IM E N U N J U K K A NK O N S T R U K S IP O H O NM E N G G U N A K A NS E TD A T AR I N G K A S.H A N Y AD U AD A R IE M P A TA T R I B U TA S L IY A N GD I P I L I HU N T U KK O N S T R U K S IP O H O NI N I. CONTOH

27 P E R T A M AP E R T A M AY A N GY A N GA K A NA K A ND I B A G ID I B A G IA D A L A HA D A L A H A S U M S I K A NB A H W AA T R I B U TH O M E _T Y P EA T R I B U T.A S U M S I K A NB A H W AA T R I B U TH O M E _T Y P EA T R I B U T. K E M U N G K I N A NP E M I S A H A NU N T U KH O M E _ T Y P ED IS E B E L A HK I R IR E N T A N G N O D ED A R I6 <= X < 3 1, D IM A N AXA D A L A HN I L A IS P L I T. S E M U AN I L A IL A I N N Y AD IS E T I A PS P L I TM E M B E N T U KN O D EA N A KS E M U AN I L A IL A I N N Y AD IS E T I A PS P L I TM E M B E N T U KN O D EA N A K Y A N GT E P A T.K E M U N G K I N A NP E M I S A H A NU N T U KH O M E _T Y P EA T R I B U TD IS E TD A T AY A N GT E P A T.K E M U N G K I N A NP E M I S A H A NU N T U KH O M E _T Y P EA T R I B U TD IS E TD A T A M E N G A M B I LS P L I TP E R T A M A,I N D E K SG I N ID I H I T U N GS E B A G A IB E R I K U T :M E N G A M B I LS P L I TP E R T A M A,I N D E K SG I N ID I H I T U N GS E B A G A IB E R I K U T : H O M E _T Y P EH O M E _T Y P E <= 6,<= 6, H O M E _T Y P EH O M E _T Y P E <= 1 0,<= 1 0, H O M E _T Y P EH O M E _T Y P E <= 1 5, H O M E _T Y P E1 5, H O M E _T Y P E 3 0,3 0, D A ND A N H O M E _T Y P EH O M E _T Y P E 3 1.3 1. CONTOH

28 P A R T I S IS E T E L A HB I N E RS P L I TP A D AH O M E _T Y P E<= 6O L E HH U T A NA C A KP A R T I S IS E T E L A HB I N E RS P L I TP A D AH O M E _T Y P E<= 6O L E HH U T A NA C A K K E M U D I A NG I N I( D 1 ),G I N I( D 2 ),D A NG I N I S P L I TD I H I T U N GS E B A G A IB E R I K U T :K E M U D I A NG I N I( D 1 ),G I N I( D 2 ),D A NG I N I S P L I TD I H I T U N GS E B A G A IB E R I K U T :

29

30 B E R I K U T N Y A,K U M P U L A ND A T AP A D AH O M E _T Y P E<= 1 0B E R I K U T N Y A,K U M P U L A ND A T AP A D AH O M E _T Y P E<= 1 0 D IL A N G K A HD I P I S A H K A ND A ND I T A B U L A S I K A ND A L A MT A B E L.D IL A N G K A HD I P I S A H K A ND A ND I T A B U L A S I K A ND A L A MT A B E L. P A R T I S IS E T E L A HB I N A R YS P L I TP A D AH O M E _T Y P E<= 1 0D E N G A NR A N D O MF O R E S T :P A R T I S IS E T E L A HB I N A R YS P L I TP A D AH O M E _T Y P E<= 1 0D E N G A NR A N D O MF O R E S T : K E M U D I A NK E M U D I A NG I N IG I N I G I N I( D 1 ),( D 2 ),G I N I( D 1 ),( D 2 ), D A NG I N IS P I L TD I H I T U N GS E B A G A IB E R I K U T :D A NG I N IS P I L TD I H I T U N GS E B A G A IB E R I K U T : N O M O RA T R I B U TR E K A M A NN O M O RA T R I B U TR E K A M A N 0N 1=20N 1=2 H O M E _T Y P EH O M E _T Y P EH O M E _T Y P EH O M E _T Y P E N O L ( 0 ) S A T U ( 1 ) N = 5 <= 1 02 > 1 0 1212 CONTOH

31

32 T A B U L A S IN I L A IK E M U N G K I N A NT A B U L A S IN I L A IK E M U N G K I N A N I N D E K SG I N IU N T U KH O M E _T Y P EA T R I B U TD IS E M U AP E M I S A H A N.I N D E K SG I N IU N T U KH O M E _T Y P EA T R I B U TD IS E M U AP E M I S A H A N. S P L I TH O M E _T Y P E<=1 0M E M I L I K IN I L A IT E R E N D A HG I N IN I L A IT U M P A HS P L I TH O M E _T Y P E<=1 0M E M I L I K IN I L A IT E R E N D A HG I N IN I L A IT U M P A H

33 D IR A N D O MF O R E S T,P E R P E C A H A ND IM A N AI N D E K SG I N IT E R E N D A HD I P I L I HP A D AN I L A IP E M I S A H.D IR A N D O MF O R E S T,P E R P E C A H A ND IM A N AI N D E K SG I N IT E R E N D A HD I P I L I HP A D AN I L A IP E M I S A H. S E J A KN I L A I - N I L A IH O M E _T Y P ES E J A KN I L A I - N I L A IH O M E _T Y P E N A M U N,A T R I B U TT E R U SM E N E R U SD IA L A M,T I T I KN A M U N,A T R I B U TT E R U SM E N E R U SD IA L A M,T I T I K T E N G A HS E T I A PP A S A N G A NN I L A IB E R T U R U T -T U R U TD I P I L I HS E B A G A IT I T I KP E M I S A HT E N G A HS E T I A PP A S A N G A NN I L A IB E R T U R U T -T U R U TD I P I L I HS E B A G A IT I T I KP E M I S A H T E R B A I KT E R B A I KD A L A MD A L A MC O N T O HC O N T O H K A M I,K A M I, T E R B A I K.P E R P E C A H A NO L E HK A R E N AT E R B A I K.P E R P E C A H A NO L E HK A R E N A I T U,B E R A D AD IH O M ET Y P E=I T U,B E R A D AD IH O M ET Y P E= ( 1 0 + 1 5 ) / 2 = 1 2. 5D A NB U K A ND IH O M E_T Y P E <=1 0.P O H O NK E P U T U S A NS E T E L A HP E M I S A H A NP E R T A M AD I T U N J U K K A ND A L A M :( 1 0 + 1 5 ) / 2 = 1 2. 5D A NB U K A ND IH O M E_T Y P E <=1 0.P O H O NK E P U T U S A NS E T E L A HP E M I S A H A NP E R T A M AD I T U N J U K K A ND A L A M : CONTOHCONTOH

34 P R O S E D U RI N ID I U L A N G IU N T U KA T R I B U TY A N GT E R S I S AD A L A MS E TD A T A.D A L A MC O N T O HI N I,N I L A II N D E K SG I N IP R O S E D U RI N ID I U L A N G IU N T U KA T R I B U TY A N GT E R S I S AD A L A MS E TD A T A.D A L A MC O N T O HI N I,N I L A II N D E K SG I N I K E D U AA T R I B U TN I L A IT E R E N D A HK E D U AA T R I B U TN I L A IT E R E N D A H S A L A R YD I H I T U N G.I N D E K SG I N ID I P I L I HS A L A R YD I H I T U N G.I N D E K SG I N ID I P I L I H S E B A G A IY A N GT E R B A I KB A G IA T R I B U T.P O H O NK E P U T U S A NA K H I RY A N GD I T U N J U K K A ND A L A M :S E B A G A IY A N GT E R B A I KB A G IA T R I B U T.P O H O NK E P U T U S A NA K H I RY A N GD I T U N J U K K A ND A L A M : CONTOHCONTOH

35 A T U R A NK E P U T U S A NU N T U KP O H O NK E P U T U S A NY A N GD I I L U S T R A S I K A NA D A L A H :A T U R A NK E P U T U S A NU N T U KP O H O NK E P U T U S A NY A N GD I I L U S T R A S I K A NA D A L A H : <=1 2, 5,M A K AN I L A IK E L A S<=1 2, 5,M A K AN I L A IK E L A S J I K AH O M E _T Y P EA D A L A H0.J I K AH O M E _T Y P EJ I K AH O M E _T Y P EA D A L A H0.J I K AH O M E _T Y P E A D A L A H>1 2, 5A D A L A H>1 2, 5 A D A L A H3 / 4,M A K AN I L A IK E L A SA D A L A H3 / 4,M A K AN I L A IK E L A S J I K AH O M E _T Y P EA D A L A H>1 2, 5J I K AH O M E _T Y P EA D A L A H>1 2, 5 D A NG A J IA D A L A H1.D A NG A J ID A NG A J IA D A L A H1.D A NG A J I A D A L A H1,M A K AK E L A SN I L A I N Y AA D A L A H0.A D A L A H1,M A K AK E L A SN I L A I N Y AA D A L A H0. CONTOHCONTOH

36 I N IA D A L A HK O N S T R U K S IP O H O NT U N G G A LM E N G G U N A K A NC A R TI N IA D A L A HK O N S T R U K S IP O H O NT U N G G A LM E N G G U N A K A NC A R T A L G O R I T M A.L E B A HA C A KM E N G I K U T IH A LI N IS A M AA L G O R I T M A.L E B A HA C A KM E N G I K U T IH A LI N IS A M A M E T O D O L O G ID A NM E M B A N G U NB E B E R A P AP O H O NU N T U KM E T O D O L O G ID A NM E M B A N G U NB E B E R A P AP O H O NU N T U K H U T A NH U T A NM E N G G U N A K A NM E N G G U N A K A NS E K U M P U L A NS E K U M P U L A NA T R I B U TA T R I B U T Y A N GB E R B E D A.Y A N GB E R B E D A. H U T A NH U T A NA C A KA C A K M E N G G U N A K A NB A G I A ND A R IS E TP E L A T I H A NU N T U KM E N G H I T U N GT I N G K A TK E S A L A H A NM O D E LD E N G A NE S T I M A S IK E S A L A H A NB A W A A N,E S T I M A S IK E S A L A H A NO U TO F - B A G( O O B ).M E N G G U N A K A NB A G I A ND A R IS E TP E L A T I H A NU N T U KM E N G H I T U N GT I N G K A TK E S A L A H A NM O D E LD E N G A NE S T I M A S IK E S A L A H A NB A W A A N,E S T I M A S IK E S A L A H A NO U TO F - B A G( O O B ).

37 TERIMA KASIH S U H E N G K I C5602202004 TEK