Judul: Penentuan Sebaran Total Kerugian Asuransi Kendaraan
Abstrak: Kerugian agregat merupakan total kerugian pemegang polis yang harus ditanggung oleh perusahaan asuransi dalam periode waktu tertentu. Kerugian agregat dapat dihitung dengan menentukan sebaran total kerugian pada periode sebelumnya. Karya ilmiah ini menggunakan Loss Distribution Approach (LDA) yaitu metode untuk menghitung sebaran total kerugian dengan mempertimbangkan banyaknya klaim dan besarnya klaim yang dihitung dengan model sebaran compound. Metode rekursif Panjer digunakan untuk mendapatkan model sebaran compound, kemudian diperoleh nilai Value at Risk (VaR) dan Expected Shortfall (ES). Untuk data klaim asuransi kendaraan di Australia periode 2004-2005, diperoleh banyaknya klaim menyebar Zero Truncated binomial negatif dan besarnya klaim menyebar invers Gauss, lognormal, dan Pareto. Untuk menghitung VaR dan ES, sebaran yang paling baik untuk besar klaim adalah sebaran invers Gauss karena paling mendekati VaR dan ES empiris., Aggregate loss is the total loss of the policyholder that must be paid by the insurance company within a certain period of time. Aggregate losses can be calculated by determining the distribution of total losses in the previous period. This scientific work uses the Loss Distribution Approach (LDA), which is a method for calculating the distribution of total losses by considering the number of claims and the amount of claims calculated using the compound distribution model. Panjer recursive method is used to obtain a compound distribution model, and then the Value at Risk (VaR) and Expected Shortfall (ES) values are obtained. For vehicle insurance claims data in Australia for the period 2004-2005, the number of claims have a Zero Truncated negative binomial distribution and the amount of claims have inverse Gaussian, lognormal, and Pareto distribution. To calculate VaR and ES, the best distribution for the amount of claims is the inverse Gaussian distribution because it is closest to the empirical VaR and ES.
Keyword: Expected Shortfall, kerugian agregat, metode rekursif Panjer, , Value at Risk

Judul: Penentuan Sebaran Kerugian Agregat Menggunakan Metode Fast Fourier Transform
Abstrak: Kerugian agregat adalah total kerugian yang dapat dihitung dengan pendekatan sebaran gabungan (compound distribution), baik secara analitik maupun numerik. Penentuan sebaran kerugian agregat dilakukan untuk memodelkan klasifikasi kerugian agregat yang meliputi penghitungan Value at Risk (VaR) dan Expected Shortfall (ES). Secara analitik, penentuan sebaran gabungan menggunakan metode fungsi karakteristik itu rumit. Sebagai alternatif, penentuan sebaran gabungan dihitung menggunakan metode fast Fourier transform. Dari data klaim dataCar di package R, diperoleh banyaknya klaim mempunyai sebaran Zero Truncated Negative Binomial (ZTNB) serta besarnya klaim mempunyai sebaran Inverse Gaussian (IG) dan sebaran Lognormal (LN). Sebaran compound ZTNB–IG lebih baik memodelkan besar VaR, nilai harapan, dan ragam data dibandingkan sebaran compound ZTNB–LN. Sedangkan untuk ES, kedua sebaran compound dapat digunakan untuk menghampiri ES empirik pada tingkat kepercayaan 97%., Aggregate loss is total losses that can be calculated with a compound distribution approach, both analytically and numerically. Determination of the aggregate loss distribution is carried out to model the classification of aggregate losses which includes the calculation of Value at Risk (VaR) and Expected Shortfall (ES). Analytically, the determination of the compound distribution using the characteristic function method is complicated. Alternatively, the determination of the compound distribution can be calculated using the fast Fourier transform method. From the dataCar claim data in package R, frequency claims have a Zero Truncated Negative Binomial (ZTNB) distribution and severity claims have an Inverse Gaussian (IG) distribution and a Lognormal (LN) distribution. The compound ZTNB–IG distribution better models the size of VaR, expectation value, and variance from the data compared to the compound ZTNB–LN distribution. Meanwhile for the ES, the two compound distributions can be used to approximate the empirical ES at the 97% confidence level.
Keyword: aggregate loss, characteristic function method, ES, fast Fourier transform method, VaR

Judul: Apatite Growth Optimation on Chicken Mucouse by Fourier Transform Infrared (FTIR) Characterization
Abstrak: Some experiment had been conducted to grow apatite from supersaturated solution of calcium phosphate ions on chicken mucouse. The calcium and phosphate ions were originated from Ca(NO3)2 and (NH4)2HPO4. Carbonate ions from (NH4)2CO3 was also used as starting solution. Experiment was carried out within about 5 hours at temperature 370 C and pH 7. Value of Ca+ : PO4 3- was 1.67, while molar ratio of CO3 2- : PO4 3- was 0.5 : 1. Molarity of molar ratio CO3 2- : PO4 3- variated approximately 0.15 : 0.30, 0.51 : 1.02, and 0.90 : 1.80. Mass of mucouse powder was constant 0.75 gram. The analysis result of Fourier Transform Infrared (FTIR) showed that the precipitation was apatite carbonate type A and B. In these experiment it was found out that the mucouse reduce the crystallinity of apatite precipitant, and the growth of apatite crystal was optimum at solution with molar ratio CO3 2- : PO4 3- about 0.15 : 0.30.
Keyword: