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"Time series big data dynamically changes the size, and, unfortunately, it may be difficult to curate the enormous amount of data due to the processing capacity and storage size. This big data allows researcher to iterate on the model millions of times over. To execute a regression on several billion rows of data on a distributed network, the resource capacity regarding large volumes of data and its distributed environment must be considered. Algorithms must be real-time based data awareness. Moreover, analyzing big data sources requires the data to be pre-processed rather than immediately collected and analyzed. This pre-processing approach for the big data sources helps minimize the amount of collected data by extracting insights. It analyzes big data quicker and is cost-effective for storage space. Hence, in this research, an approximation method for analyzing regression problems in a big data stream with parallelism is proposed. The partitioning method for huge data stream helps reduce the computing time and required space, and the speed-up can improve the processing time. The performance evaluation of concurrent regression model is first executed by massive online analysis (MOA) simulation. Then, to validate the approximation method, the results performed by our proposed method are compared to those results collected from the simulation. The comparisons show evenly between the two methods."

"Untuk menekan kerugian sekecil mungkin, lazimnya perusahaan asuransi akan mengambil suatu jumlah tertentu sebagai jaminan atas risiko yang ia tutup (cover) dan jumlah ini adalah yang disebut dengan retensi. Metode batas retensi yang ditetapkan oleh perusahaan selama ini memang memberikan nilai keamanan bagi perusahaan, tetapi dari segi biaya reasuransinya mengurangi pendapatan/keuntungan perusahaan, cara ini sangat lemah. Metode ini sering berakibat tingkat retensi sebagai batas yang dicadangkan melebihi jumlah klaim. Metode solusi Teorema Rosenthal mencoba untuk menjawab hal itu dengan tujuan minimalisasi kerugian, sekaligus mengevaluasi tingkat retensi perusahaan. Pendekatan Rosenthal menjawab efektifitas dari taksiran total klaim dalam satu peristiwa, metode umum yang biasa digunakan asuransi syariah. Setelah dilakukan uji Chi-square dan analisis varians, taksiran retensi Rosenthal untuk tertanggung individu lebih baik daripada retensi yang selama ini ditetapkan perusahaan Sehingga perusahaan Asuransi Syariah "X" disarankan untuk mengevaluasi dan merubah batas retensi yang selama ini ditetapkan untuk tertanggung individu yang telah ditentukan, dengan menghitung metode Rosenthal Approximation. Pada penelitian kali ini Teorema yang dikembangkan Rosenthal tidak dapat digunakan untuk mencari batas retensi untuk tertanggung grup perusahaan.......In order to minimize the loss as minimum as possible, an insurance company will usually take a certain amount as the collateral for the risk it cover. This amount is called retention. Indeed, the retention limit method which is specified by the company up to present may provide a security value to the company. However, viewed from the aspect of reinsurance cost, it may decrease the revenue/profit received by the company. Therefore, this method proved to be inefficient. This method frequently results in the retention level, as the reserved limit, to exceed the value of the claim. The Theorem Rosenthal solution method seek to solve the problem with the intention of minimizing the loss as well as to respond to the effectiveness of the estimated total claim for an incident, a general method usually applied by the syariah insurance_ After performing a Chi-square test and variance analysis, the estimated Rosenthal retention for the individual sufferer proved to be better than the retention previously specified by the company. Therefore, the Syariah Insurance "X" is recommended to evaluate and modify the retention limit previously specified by the individual sufferer, by calculating the Rosenthal approximation method. In this research, the Theorem developed by Rosenthal may not be applied to find the retention limit for the company group sufferer."