ix. MAINTENANCE TIME INTERVAL DETERMINATION AND. FINANCIAL
COMPARISON ANALYSIS OF GAS TURBINE. AUXILIARY COMPONENT.
banyak cara yang dilakukan oleh perusahaan untuk meminimalisasi terjadinya
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PERATURAN PERATURAN BANGUNAN 28. BAB IV: TEMA & KONSEP ...
Jurusan Arsitektur FTSP-ITS pada tanggal 29 Januari 2006. Nilai : ... Mengetahui.
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as.
Identifikasi model yang diperoleh dengan Algoritma Genetika ini nantinya akan ...
Genetika memiliki nilai MSE yang lebih rendah dibandingkan dengan metode.
iii. A DETECTION MODEL DEVELOPMENT FOR. FINANCIAL CRISIS IN
INDONESIA WITH DATA. MINING APPROACH. Name. : Belinda Aprilia. NRP.
iii
A DETECTION MODEL DEVELOPMENT FOR FINANCIAL CRISIS IN INDONESIA WITH DATA MINING APPROACH Name NRP Department Supervisor Co-Supevisor
: Belinda Aprilia : 2508 100 079 : Industrial Engineering : Naning Aranti Wessiani, ST. MM : Prof. Ir. Budi Santosa, M.S., Ph.D
Abstract Financial crisis is a situation that leads to a reduction of a country's foreign exchange reserves significantly and the local currency against foreign currency. One of the financial crisis in the world that a lot of attention is the financial crisis that occurred in countries in Asia, including Indonesia, in mid-1997. The impact of the 1997 financial crisis for Indonesia is very great, such as GDP growth decreased by 13%, rising inflation rate reached about 70%, rising levels of unemployment and poverty, as well as the banking crisis. Given the tremendous negative impact of financial crisis, many international institutions to develop an Early Warning System (EWS), which is a model that seeks to detect the possibility of a crisis in a country. In general, the model results that have been developed so far is less satisfactory for the case of financial crisis in Indonesia. This is because generally the model developed in the form of a generic model (global model). Therefore, this study tried to develop a model of early detection of financial crisis in Indonesia using data mining approaches, the method of Support Vector Machine (SVM). By using the 24 indicators for the early economic quartile data from 1990 to 2010, researcher conducted a variable selection and model development of the detection of financial crisis in Indonesia. Based on the variable selection performed with Linear-Programming Support Vector Machine (LP-SVM), the beginning of the 24 variables there are only 17 variables that
iv really affect detection of financial crisis. While the development of models with SVM polynomial kernel function was found that the combination of kernel parameters p = 6 and penalty c = 10 gives the best classification accuracy. Key Word: Financial Crisis in Indonesia, Early Warning System, Data Mining, Support Vector Machine
