Materi

11ARIMA and Forecasting [.pdf] [57.4 KB]
Mata kuliah : Econometrics 2; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D ; Fakultas : Ekonomi (Program Magister Double Degree PPIE FEUI); Universitas Indonesia
ARIMA and Forecasting ? We have learned linear models and their characteristics, like: AR(p), MA(q), ARMA(p,q) and ARIMA (p,d,q). ? The important thing that we have to know in developing the models are determining the orders of p, q and d. It turns out that the process of deciding p, q and d is not easy. We need kinds of arts and subjectivity. ? It is true that the identification process can be guided by observing autocorrelation function or partial autocorrelation function. However, the experience from t
12Application of ARIMA Models [.pdf] [71.2 KB]
Mata kuliah : Econometrics 2; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D ; Fakultas : Ekonomi (Program Magister Double Degree PPIE FEUI); Universitas Indonesia
Application of ARIMA Models We have learned how to model using ARIMA Stages: 1. Verify whether the data we are analyzing is a stationary data using ACF or other methods 2. If the data is not stationer, make them stationer by differencing up to d times until stationer. 3. If the data is stasioner determine p, order of AR, and q, order of MA, using PACF and ACF, respectively. Spike on PACF indicates order of p; while spike on ACF indicates order of q 4. Estimate ARIMA (p,d,q). 5. Diagnostic Test: check wh
13Faktor-Faktor Yang Perlu Diperhatikan Pada Pemasangan Gigi Tiruan Lepas Untuk Mencegah Terjadinya Candidiasis [.ppt] [1,497.1 KB]
Materi Pembicara GDI II 2008 di Hailai Ancol Jakarta drg. Sri Hardjanti Sp.Pros(K)FKG UI
14CARIOGRAM Evaluasi Resiko Karies [.exe] [1,651.7 KB]
Evaluasi Resiko Karies drg. Anton Rahardjo MKM. PhD FK UI
15URINARY TRACT INFECTION [.pdf] [1,081.5 KB]
URINARY TRACT INFECTION Dr.Budiman Bela Departemen Mikrobiologi FK UI
M ardias tuti H Wahid and Budiman Bela D epartment of M icrobiology, Faculty of M edicine University of Indones ia 1 E ssentials of D iagnosis: A cute Cystitis-Urethritis: Women and girls older than 2 years A cute onset dyuria, increased frequency of urination Pyuria > 5-10 erythrocytes/ high power field of centrifuged urine or positive leukocyte esterase test Positive urine culture (1000-100,000 colony forming units/ ) for E. mL coli, other Enterobacgteriaceae, enterococci, or Staphylococcus sapr
16Rules for BST Deletion [.pdf] [277.6 KB]
Materi : Rules for BST Deletion; Dosen : Dr. Ir. Petrus Mursanto M.Sc.; Fakultas Ilmu Komputer Universitas Indonesia
Rules for BST Deletion Men-delete X dari BST: 1. Jika X adalah leaf, tinggal delete 2. Jika X punya satu anak, ganti dengan anak tsb. 3. Jika X punya dua anak, ganti nilainya dgn predecessor secara inorder. Red-Black Tree Deletion Men-delete X dari RB Trees: 1. Jika X adalah merah? No problem 2. Jika X adalah hitam? Jika X bukan root, akan mengubah jumlah node hitam salah satu path ke leaf. Red-Black Trees Red-Black Trees Tujuan Top-Down Deletion Men-delete X dari RB Trees: Usahakan X adalah leaf mera
17Brute-Force Recursive Version [.pdf] [202.9 KB]
Materi : Brute-Force Recursive Version; Dosen : Dr. Ir. Petrus Mursanto M.Sc.; Fakultas Ilmu Komputer Universitas Indonesia
Brute-Force Recursive Version Coin = 1, 5, 7 Change = 10 1+minCoin(9) 1+minCoin(9) 1+minCoin(8) 1+minCoin(8) 5+minCoin(4) 5+minCoin(4) 7+minCoin(2) 7+minCoin(2) 1+minCoin(4) 1+minCoin(4) minCoin(10) minCoin(10) 5+minCoin(5) 5+minCoin(5) 5+minCoin(0) 5+minCoin(0) X 1+minCoin(2) 1+minCoin(2) 7+minCoin(3) 7+minCoin(3) X X 9-Mar-04 IKI10100 - PM 1 Pelajari algoritma ini dan ubah ke bentuk recursive! for loop Version coinUsed[0]=0; lastCoin[0]=1; for(int cents = 1; cents <= change; cents++) { int minCoins =
18Binary Search [.pdf] [223.4 KB]
Materi : Binary Search; Dosen : Dr. Ir. Petrus Mursanto M.Sc.; Fakultas Ilmu Komputer Universitas Indonesia
BinarySearch low high Running Time of BinarySearch n = 16 Unit time yang dibutuhkan oleh proses BinarySearch berbanding logarithmic (basis 2) dengan input size log2 (input size) mid n=8 n=4 n=2 n=1 int binarySearch(int a[ ], int x) { int low = 0, high = a.length ? 1, mid; while (low <= high) { mid = (low + high) / 2; if( a[mid] < x ) low = mid + 1; else if ( a[mid] > x ) high = mid ? 1; else return mid; } return ?1; // x not found } 02-Mar-04 IKI10100 - PM O (log N) 2 1 02-Mar-04 IKI10100 - PM In
19Function Curve [.pdf] [219.9 KB]
Materi : Function Curve; Dosen : Dr. Ir. Petrus Mursanto M.Sc.; Fakultas Ilmu Komputer Universitas Indonesia
Function Curve Contoh: O(1) vs O(N2) FunctionA(int mat[][]) { ....... ....... 1000000 lines of code ....... ....... } FunctionB(int mat[][]) { x= # columns in mat; y= # rows in mat; ..... for(i=0;i
20Algorithm Analysis [.pdf] [238.7 KB]
Materi kuliah : Algorithm Analysis; Dosen : Dr. Ir. Petrus Mursanto M.Sc.; Fakultas Ilmu Komputer Universitas Indonesia
Algorithm Analysis ? Kita perlu memproses jumlah data yang sangat besar. ? Harus diyakinkan bahwa program berhenti dalam batas waktu yang wajar (reasonable) ? Tidak terikat pada programming language atau bahkan metolodologi (mis. Procedural vs OO) 19-Feb-04 IKI10100 - PM 4-1 Program Attributes ? ? ? ? ? ? ? Reliable code Easy modification Faster code Ease of use Total development time Total development cost Mean space and time taken IKI10100 - PM 4-2 19-Feb-04 Algoritma ? Suatu set instruksi yang harus
Pertama Terakhir (Hal. 275)     Prev Next   |  Halaman: 1 2 3 4 5