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"Project planning is among the most critical factors to the success of a construction project. In project planning, cost and schedule are closely interrelated, because they share a lot of common data in their planning processes. Therefore, the integration of cost and schedule functions has been an attractive issue in construction project planning. Many researchers have emphasized the benefits of this integration and several different methodologies combining cost and schedule data have been provided. However, the results of the previous studies are not comprehensive enough to fulfill the requirements of project planning. This paper presents a model-based planning system that employs Building Information Model (BIM), Object Sequencing Matrix (OSM), and Genetic Algorithms (GAs) to obtain an optimal crew assignment under resource and workspace constraints. The purposes are to evaluate the project costs and optimize the temporal distribution of resources in project planning. A computer implementation called Cost/Schedule Integrated Planning System (CSIPS) is also developed to verify the feasibility of the proposed approach."

"Triglyceride hydrolysis using lipase has been proposed as a novel method to produce raw materials in food and cosmetic industries such as diacylglycerol, monoacylglycerol, glycerol and fatty acid. In order to design a reactor for utilizing this reaction on industrial scale, constructing a kinetic model is important. Since the substrates are oil and water, the hydrolysis takes place at oil-water interface. Furthermore, the triglyceride has three ester bonds, so that the hydrolysis stepwise proceeds. Thus, the reaction mechanism is very complicated. The difference between the interfacial and bulk concentrations of the enzyme, substrates and products, and the interfacial enzymatic reaction mechanism should be considered in the model."

"Harga saham selalu berfluktuasi setiap waktu. Hal ini merupakan salah satu bentuk ketidakpastian yang terjadi di pasar saham. Risiko akibat dari ketidakpastian ini dapat diminimalisir dengan peramalan harga saham yang dibentuk dengan model linier. Salah satu model linier yang dapat digunakan dalam peramalan harga saham adalah model hybrid ARFIMA-GARCH. Model ini merupakan gabungan dari model ARFIMA dan model GARCH yang sudah pernah digunakan oleh penelitian sebelumnya dan disinyalir memiliki kekurangan dan kelebihan masing-masing. Model ARFIMA baik digunakan untuk peramalan jangka panjang, namun memiliki kendala, yakni adanya volatilitas yang terjadi dalam jangka panjang. Hal ini dapat diatasi oleh model GARCH yang baik digunakan dalam volatilitas pada harga saham dalam jangka panjang. Model GARCH merupakan pengembangan dari model Autoregressive Conditional Heterocedastic (ARCH), di mana model ini menggunakan volatilitas pada data sebelumnya. Berikutnya, model ARFIMA dan GARCH yang digabungkan menjadi model hybrid ARFIMA-GARCH akan digunakan pada penelitian ini untuk peramalan harga saham LQ45. Pada penelitian ini, hanya dua saham dari indeks saham LQ45 yang akan diprediksi harga sahamnya, yakni saham dengan volatilitas harga terkecil dan terbesar. Data harga saham yang digunakan adalah harga penutupan harian saham BBCA (PT Bank Central Asia Tbk.) dan TKIM (PT Tjiwi Kimia Paper Factory Tbk.) pada periode tahun 2017-2021. Hasil peramalan dengan model ARFIMA-GARCH akan dievaluasi nilai erornya menggunakan Root Mean Square Error dan Mean Absolute Percentage Error. Berikutnya, nilai akurasi ini akan dibandingkan dengan nilai akurasi peramalan menggunakan model ARFIMA. Pada akhirnya, diperoleh hasil bahwa peramalan harga saham dengan model ARFIMA-GARCH lebih baik dibandingkan dengan model ARFIMA.

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Stock price always fluctuate all the time. This is one form of buffer that occurs in the stock market. The risk resulting from this buffer can be minimized by forecasting stock prices using linear model. One of the linear model that can be used in stock price forecasting is the hybrid ARFIMA-GARCH model. This model is a combination of the ARFIMA model and GARCH model which have been used in previous studies and allegedly each models have advantages and disadvantages. ARFIMA model is good for long-term forecasting, but has a problem, which is the volatility that occurs in the long term. This can be resolved by GARCH model which is good for volatility in stock prices, even for a long term data. GARCH model is a development of Autoregressive Conditional Heterocedastic (ARCH) model, where this model uses volatility in previous data. Furthermore, the ARFIMA and GARCH models are combined into the hybrid ARFIMA-GARCH model which will be used in this study for forecasting LQ45 stock prices. In this study, only two stock prices from LQ45 stock index that will be forecast, stocks which have the smallest and largest price volatility. The price data used is the daily closing price of BBCA (PT Bank Central Asia Tbk.) and TKIM (PT Tjiwi Kimia Paper Factory Tbk.) in 2017- 2021. The ARFIMA-GARCH model forecasting results will be evaluated by using the Root Mean Square Error and Mean Absolute Percentage Error. Next, this error accuracy value will be compared with the forecasting accuracy value using ARFIMA model. In the end, our hypothesis is that the stock price forecasting with ARFIMA-GARCH model is better than ARFIMA model."

"The growing interest in bonds has created a need for a method to evaluate performance of bond portfolios. The method of evaluation is called fixed income performance attribution. This paper will present fixed income performance attribution using the Campisi Model, how to use it and how to analyze the results. PT XYZ, a professional fund managing company, will be used for the test case."

"Model BaFe12O19 telah dikembangkan menggunakan pendekatan Mean Field pada Hamiltonian Heisenberg. Parameter exchange interaction dihitung dengan memanfaatkan informasi posisi-posisi Fe3+ dan nilai suhu Curie Barium hexaferrite. Model ini memberikan nilai magnetisasi saturasi sebesar 79 emu/gram dan koersivitas 0.7 Tesla untuk BaFe12O19 pada suhu kamar. BaFe12􀀀xMnxO19 (x=0,1,2,4), BaFe12􀀀xTixO19 (x=0,1,2), BaFe12􀀀2xMnxTixO19 (x=0,1,2) mengalami penurunan nilai magnetisasi saturasi, remanen, dan koersivitas seiring meningkatnya konsentrasi Mn/Ti.

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A model of BaFe12O19 has been developed based on Heisenberg Hamiltonian within Mean Field approximation. The exchange interaction parameter was calculated using the information of Curie temperature and positions of Fe3+ ions in Barium hexaferrite. This model at room temperature gives a saturation magnetization value about 79 emu/gram and coercivity about 0.7 Tesla for BaFe12O19. BaFe12􀀀xMnxO19 (x=0,1,2,4), BaFe12􀀀xTixO19 (x=0,1,2), BaFe12􀀀2xMnxTixO19 (x=0,1,2) show a decreasing trend in saturation magnetization, remanence, and coercivity with increasing x value."

"Kontrak asuransi jiwa dapat digambarkan dengan model Markov, yaitu multistate model yang melibatkan proses Markov dimana keadaan yang dialami oleh pemegang polis pada masa mendatang hanya bergantung pada keadaan terakhir yang terjadi sebelumnya dan waktu (usia polis asuransi) atau usia pemegang polis. Dipandang dengan model Markov, manfaat asuransi dibedakan menjadi dua yaitu manfaat transisi dan sojourn benefit. Bentuk eksplisit dari cadangan premi dengan menggunakan model Markov dapat diperoleh dari solusi persamaan diferensial Thiele. Pada praktik asuransi, terdapat manfaat asuransi yang bergantung cadangan premi baik manfaat transisi yang bergantung cadangan premi maupun sojourn benefit yang bergantung cadangan premi, contohnya nilai tunai dan biaya pengelolaan cadangan premi. Dengan melakukan modifikasi manfaat pada persamaan diferensial Thiele dan menerapkan teorema Cantelli pada persamaan diferensial tersebut, dapat diperoleh bentuk eksplisit dari cadangan premi untuk manfaat yang bergantung cadangan premi.

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Life insurance contract can be described using Markovian model, that is multistate model which involve Markovian process in which state experienced by policy holder in the future depends only on last state which happen previously and time (policy's age) or policy holder's age. To be regarded with Markovian model, insurance benefits are divided into transition benefit and sojourn benefit. Explicit form from premium reserve with using Markovian model can be attained from the solution of Thiele differential equation. In the real insurance practice, there are premium reserve-dependent benefits, both premium reserve-dependent transition benefits and premium reserve-dependent sojourn benefits, as example: cash value and premium reserve management cost. By doing certain benefit modification on Thiele differential equation and by applying Cantelli theorem on that differential equation, can be attained explicit form from premium reserve for premium reserve-dependent benefits."