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Abstrak :
Since year 1997 up to 1999, 64 banks in Indonesia have been liquidated/closed by Central Bank and numbers of bank have followed banking re-capitalization program or under control of Indonesian Banking Restructuring Agency/OKRA. It brings Indonesia into a big economic problem because Bank's role in Indonesian economy was very important. Indonesia becomes a country which has a big burden and very hard to recover from the crisis. That is the reason that the author intends to explore more precisely the condition of Indonesian Banking. This research is designed to make a model that can be used to predict Bank Failure with case study in Indonesian Banking 1997 - 1999. This research purpose is to differentiate good bank and failure bank, so the result of this research will contribute to banking knowledge in Indonesia and will help the Indonesian to select a good bank. This research uses Multiple Logistic Regression Model as a methodology with more than 2 (two) independent variables. The model had been chosen since the type of dependent variable is binary (Good bank or Failure bank) and allows us to use 'dummy data' that can not be possible in conventional model. The numbers of data sample in this research were 144 banks : 81 failure banks and 63 good bank. Data used in the model was the last published financial statement before the bank failure. The dependent variable is failure/good bank and there are 16 independent variables related to Capital, Asset Quality, Earning/ Profitability, Liquidity and Efficiency. The model was tested by determination coefficient Cox & Snell R Squared and Nagelkerke R Squared. The test shows that the model is significant (0.6 & 0.8). it means that independent variable used in the model has significant correlation to dependent variable. Kolmogorov Smimov Test shows that the model be able to differentiate failure bank and good bank. Hosmer & Lemeshow Test proves that the prediction of the model is fit with the actual data. The above tests summarize that the model can be used to predict and differentiate between failure bank and good bank.

Abstrak :
Turbin gas adalah suatu alat yang memanfaatkan gas sebagai fluida untuk memutar turbin dengan pembakaran internal sehingga mampu memutar generator untuk menghasilkan listrik. Turbin gas memiliki tingkat bahaya yang besar, sehingga perlu dilakukan penelitian untuk menganalisis seberapa besar potensi kegagalan komponen-komponennya. Jika sebuah mesin atau peralatan mengalami kerusakan, maka seluruh fungsi akan terhenti. Oleh karena itu, aktivitas preventive maintenace dibutuhkan untuk mencegah kerusakan dan meminimasi downtime. Tahapan penelitian ini dimulai dengan menentukan komponen kritis menggunakan diagram pareto. Kemudian memvisualisasikan data-data yang didapat. Lalu, menentukan nilai parameter shape (β), parameter scale (η), reabilitas, MTTF (Mean Time to Failure), dari komponen-komponen kritis. Terakhir merekomendasikan jadwal preventive maintenance. Dalam penelitian ini pengolahan dan analisis data dilakukan melalui Big Data Analytics menggunakan R Software diharapkan kedepannya dapat dikembangkan menjadi sebuat aplikasi yang terintegrasi untuk mengimpor dan menganalisis data historis (data base), memudahkan untuk memprediksi kegagalan secara real time, memprediksi kegagalan sebelum muncul, dan dapat mengawasi equipment secara run on live. Berdasarkan hasil pengolahan data yang telah dilakukan, ditemukan bahwa ada 8 komponen kritis, Penentuan keandalan yang dilakukan dengan bantuan R software dengan menggunakan distribusi weibull menunjukkan saat 43.830 jam operasional atau 5 tahun, komponen yang memiliki keandalan paling rendah adalah Actuator dengan nilai sebesar 0,799. Keandalan sistem pada saat 43.830 jam atau 5 tahun adalah 0,866, nilai ini digolongkan sebagai kuat. Hasil dari evaluasi nilai parameter shape (β), menunjukan 7 dari 8 komponen di kategorikan IFR (Increasing Failure Rate) kegagalan ini diakibatkan oleh beberapa faktor seperti penuaan, korosi, gesekan, sehingga di sebut fase pengausan (wearout), dan solusi yang tepat untuk membuat rekomendasi jadwal preventive maintenance dengan T=80%. ......Gas turbine is one tool that uses gas as a fluid to turn turbines with internal combustion so that it is able to turn generators to produce electricity. Gas turbines have a high level of danger, so research needs to be done to increase the high potential level of its components. If the machine is damaged, all functions will stop. Therefore, preventive activities are needed to prevent damage and minimize downtime. The stages of this research began by determining the critical components using pareto diagrams. Then visualize the data obtained. Then, determine the value of the form parameter (β), parameter scale (η), reliability, MTTF (Mean Time to Failure), from the critical components. Last scheduled preventative maintenance schedule. In this research, processing and analyzing data done through Big Data Analytics using R Software is expected to be developed in the future into an integrated application to facilitate and analyze historical data (databases), facilitate to predict in real time, predict changes before they appear, and Can keep running equipment directly. Based on the results of data processing that has been done, found that there are 8 critical components, Determination which is done with the help of R software using Weibull distribution shows when 43,830 operational hours or 5 years, the component that adds the lowest is the Actuator with a value of 0.799. The current system value of 43,830 hours or 5 years is 0.866, this value is classified as strong. The results of the evaluation of the form parameter values (β), showed 7 out of 8 components categorized as IFR (Increased Failure Rate) this improvement was caused by several factors such as aging, corrosion, friction, so it was called the wearout phase, and the solution needed for make a preventive maintenance schedule recommendation with T = 80%.