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"ABSTRAKPergerakan tingkat bunga merupakan salah satu faktor yang perlu diperhatikan dalam berinvestasi. Untuk menentukan nilai tingkat bunga pada waktu tertentu sebaiknya investor memiliki pengetahuan tentang pergerakan tingkat bunga. Pergerakan tingkat bunga dapat direpresentasikan oleh model tingkat bunga dalam bentuk persamaan diferensial stokastik. Model tingkat bunga pada tesis ini adalah model CARMA (2,1) dengan . Dalam implementasi, digunakan 2 buah data tingkat bunga harian zero-coupon bond dengan masa jatuh tempo 5 tahun yaitu periode 2 Maret 2009 sampai dengan 26 Februari 2010 yang bersifat tidak stasioner dan data periode 1 Agustus 2011 sampai dengan 31 Oktober 2011 yang bersifat stasioner. Estimasi parameter model CARMA (2,1) dilakukan dengan cara menggunakan hasil estimasi parameter proses ARMA (2,1) yang ditransformasikan ke dalam proses CARMA (2,1) berdasarkan suatu proposisi. Hasil implementasi menggunakan data yang stasioner menunjukkan bahwa estimasi nilai parameter yang diperoleh dapat merepresentasikan cukup baik pergerakan data historis tingkat bunga yang digunakan.

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ABSTRACTThe dynamics of interest rates are cause for concern on investment. To determine the interest rate at a certain time, the investors should have knowledge about the dynamics of interest rates. The dynamics of interest rates can be represented by an interest rate model which is a stochastic differential equation (SDE). The interest rate model used in this thesis is CARMA (2,1) model with . In the implementation, we use two periods of daily interest rate data for zero-coupon bond with five years maturity date. They are non-stationary data for the period from March 2, 2009 to February 26, 2010, and stationary data from August 1, 2011 to October 31, 2011. Estimation of CARMA(2,1) parameters is obtained by applying the parameter estimation of ARMA(2,1) process and then transforming it into CARMA(2,1) process based on a proposition. The results of implementation using stationary data show that the parameters obtained can represent the historical interest rate data quite well."

"Tingkat mortalitas merupakan salah satu indikator dalam kemajuan bidang kesehatan dan untuk membantu mengidentifikasi kelompok masyarakat yang diutamakan menerima program kesehatan serta pembangunan khusus. Tingkat mortalitas juga dapat digunakan untuk menunjukkan tingkat kesejahteraan dan kualitas hidup suatu negara. Selain itu tingkat mortalitas juga berperan dalam penetapan harga premi (pricing) dan perhitungan cadangan manfaat (valuation) untuk polis asuransi, produk anuitas, serta berperan dalam manajemen risiko aktuaria dan program pensiun. Mengingat tingkat mortalitas merupakan variabel acak yang berubah dari waktu ke waktu dan nilainya berada pada interval (0,1), maka diperlukan suatu model untuk dapat meramalkan tingkat mortalitas di masa depan. Salah satu model yang memiliki potensi untuk dapat memodelkan dan meramalkan tingkat mortalitas adalah model Beta Autoregressive Moving Average (βARMA). Model βARMA merupakan pengembangan dari regresi beta di mana error modelnya mengikuti proses Autoregressive Moving Average (ARMA). Pada penelitian ini akan dibahas mengenai implementasi model βARMA dalam memodelkan dan juga meramalkan tingkat mortalitas. Data yang digunakan adalah data tingkat mortalitas tahunan Indonesia dari tahun 1960 hingga 2020 dengan trend menurun dan data tingkat mortalitas bulanan akibat kecelakaan kerja di Rio Grande do Sul dari Januari 2000 hingga Desember 2017 yang bersifat stasioner. Model βARMA terbaik untuk kedua data dipilih berdasarkan nilai Akaike’s Information Criterion (AIC) terkecil kemudian dilakukan peramalan untuk enam periode selanjutnya. Keakuratan peramalan diukur berdasarkan Root Mean Square Error (RMSE). Pada data tingkat mortalitas tahunan Indonesia, diperoleh nilai RMSE sebesar 0.0001, sementara pada data tingkat mortalitas bulanan akibat kecelakaan kerja di Rio Grande do Sul, diperoleh nilai RMSE sebesar 0.0226.

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The mortality rate is one of the indicators of progress in the health sector and to help identify groups of people who are prioritized to receive special health and development programs. The mortality rate can also be used to indicate the level of welfare and quality of life of a country. In addition, the mortality rate also plays a role in pricing premiums and calculating the benefit reserve (valuation) for insurance policies and annuity products, as well as playing a role in actuarial risk management and pension programs. Considering that the mortality rate is a random variable that changes from time to time and the value is in the interval (0,1), a model is needed to be able to forecast the mortality rate in the future. One model that has the potential to be able to model and forecast mortality rates is the Beta Autoregressive Moving Average (βARMA) model. The βARMA model is a development of beta regression where the error model follows the Autoregressive Moving Average (ARMA) process. In this study, we will discuss the implementation of the βARMA model in modeling and forecasting mortality rates. The data used are Indonesia's annual mortality rate data from 1960 to 2020 with a decreasing trend and the monthly mortality rate data due to work accidents in Rio Grande do Sul from January 2000 to December 2017 which is stationary. The best βARMA model for both data is selected based on the smallest Akaike's Information Criterion (AIC) value then a forecast is made for the next six periods. Forecasting accuracy is measured based on Root Mean Square Error (RMSE). In Indonesia's annual mortality rate data, the RMSE value is 0.0001, while in the monthly mortality rate data due to work accidents in Rio Grande do Sul, the RMSE value is 0.0226."

"Latar belakang: Nefrotoksisitas merupakan penurunan fungsi ginjal yang disebabkan oleh zat toksin atau beracun. Prevalensi nefrotoksisitas adalah 18% - 27% di Amerika Serikat dan 5% dari pasien pascakemoterapi di RSUP Dr. Sardjito. Ekstrak lunasin memiliki fungsi sebagai antioksidan dan dan antikanker yang diperlukan penelitian efek toksisitas terhadap ginjal tikus Sprague Dawley (SD) untuk memeriksa keamanannya.Metode: Penelitian ini menggunakan metode eksperimental in vivo dengan sampel ginjal tikus Sprague Dawley (SD). Tikus diberikan dosis lunasin masing-masing dengan konsentrasi 250 mg/kgBB, 500 mg/kgBB, dan 750 mg/kgBB yang selanjutnya diterminasi serta dibentuk preparat histopatologi jaringan ginjal dengan diberikan pewarnaan HE. Proses berikutnya dilakukan pengamatan melalui mikroskop dengan perbesaran 400 kali pada diameter tubulus. Pengukuran pada satu preparat dengan mengambil 5 tubulus dan masing-masing tubulus membentuk 5 garis saling memotong untuk mengukur diameter. Menggunakan aplikasi Indomicro View untuk mengolah data yang nantinya akan dimasukkan ke Microsoft Excel untuk pengumpulan data.Hasil: Pemberian ekstrak lunasin memiliki hasil yang berbeda terhadap diameter tubulus ginjal tikus Sprague Dawley (SD). Ginjal normal tanpa pemberian ekstrak lunasin memiliki rerata diameter adalah 31,325 μm. Pada pemberian dosis 250 mg/kgBB rerata diameter adalah 31,985 μm, pada dosis 500 mg/kgBB rerata diameter adalah 33,91 μm, dan pada dosis 750 mg/kgBB rerata diameter adalah 32,02 μm. Dengan hasil yang dimiliki tidak bermakna dan tidak signifikanKesimpulan: Berdasarkan penelitian yang dilaksanakan terdapat kenaikan diameter tubulus ginjal tikus Sprague Dawley (SD) dengan pemberian dosis ekstrak lunasin sebanyak 250 mg/kgBB dan 500 mg/kgBB, dan terdapat penurunan diameter tubulus pada pemberian lunasin dengan dosis 750 mg/kgBB. Hasil yang diperoleh tidak signifikan terhadap data yang diukur.

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Introduction: Nephrotoxicity is a decrease in kidney function caused by toxic or toxic substances. The prevalence of nephrotoxicity is 18% - 27% in the United States and 5% of post-chemotherapy patients at Dr. Sardjito. The lunasin extract has antioxidant and anticancer functions which are needed to study its toxicity effect on the kidneys of Sprague Dawley (SD) rats.

Method: This study used an in vivo experimental method with Sprague Dawley (SD) rat kidney samples. The rats were terminated and histopathological preparations were made using HE staining. Furthermore, the sample was given a dose of lunasin with a concentration of 250 mg/kgBW, 500 mg/kgBW, and 750 mg/kgBW, respectively. The next process was observed through a microscope with a magnification of 400 times on the diameter of the tubules. Measurement on one preparation by taking 5 tubules and each tubule forming 5 lines to measure diameter. Using the Indomicro View application to process data which will later be entered into Microsoft Excel for data collection.

Result: The administration of lunasin extract had different results on the kidney tubule diameter of Sprague Dawley (SD) rats. Normal kidney without lunasin extract had a mean diameter of 31.325 μm. At a dose of 250 mg/kg the average diameter was 31.985 μm, at a dose of 500 mg/kg the average diameter was 33.91 μm, and at a dose of 750 mg/kg the average diameter was 32.02 μm.

Conclusion: Based on the research carried out, there was an increase in the diameter of the kidney tubules of Sprague Dawley (SD) rats with the administration of lunasin extract doses of 250 mg/kgBW and 500 mg/kgBW, and there was a decrease in tubular diameter with the administration of lunasin at a dose of 750 mg/kgBW. The results in the form of increases and decreases obtained are not significant to the measured data."

"This book treats methods inspired by attempts to understand the seemingly non-mathematical question of bacterial chemotaxis and their application in other environments. The text presents significant generalizations on existing stochastic averaging theory developed from scratch and necessitated by the need to avoid violation of previous theoretical assumptions by algorithms which are otherwise effective in treating these systems. Coverage is given to four main topics. Stochastic averaging theorems are developed for the analysis of continuous-time nonlinear systems with random forcing, removing prior restrictions on nonlinearity growth and on the finiteness of the time interval. The new stochastic averaging theorems are usable not only as approximation tools but also for providing stability guarantees. Stochastic extremum-seeking algorithms are introduced for optimization of systems without available models. Both gradient- and Newton-based algorithms are presented, offering the user the choice between the simplicity of implementation (gradient) and the ability to achieve a known, arbitrary convergence rate (Newton). The design of algorithms for non-cooperative/adversarial games is described. The analysis of their convergence to Nash equilibria is provided. The algorithms are illustrated on models of economic competition and on problems of the deployment of teams of robotic vehicles. Bacterial locomotion, such as chemotaxis in E. coli, is explored with the aim of identifying two simple feedback laws for climbing nutrient gradients. Stochastic extremum seeking is shown to be a biologically-plausible interpretation for chemotaxis. For the same chemotaxis-inspired stochastic feedback laws, the book also provides a detailed analysis of convergence for models of nonholonomic robotic vehicles operating in GPS-denied environments. The book contains block diagrams and several simulation examples, including examples arising from bacterial locomotion, multi-agent robotic systems, and economic market models. Stochastic averaging and extremum seeking will be informative for control engineers from backgrounds in electrical, mechanical, chemical and aerospace engineering and to applied mathematicians. Economics researchers, biologists, biophysicists and roboticists will find the applications examples instructive."

"ABSTRAKDalam pengangkutan laut, risiko bahaya laut bagi kapal dan muatan membutuhkan penyelesaian disebut general average. Saat ini, di Indonesia setidaknya terdapat dua pilihan hukum terkait general average yaitu menurut Kitab Undang-Undang Hukum Dagang (KUHD) dan York-Antwerp Rules 1994 (YAR 1994). Tesis ini membahas perbedaan pengaturan mengenai general average pada KUHD dan YAR 1994, serta pengaturan mana yang lebih memberikan perlindungan hukum bagi pemilik kapal dan pemilik muatan dalam general average. Penelitian yang menggunakan metode yuridis normatif ini, menyimpulkan terdapat perbedaan ruang lingkup biaya atau kerugian yang termasuk dalam general average, dan prinsip-prinsip umum dari general average. Selain itu, pengaturan general average pada YAR 1994 lebih memberikan perlindungan hukum bagi pemilik kapal dan muatan dalam pengangkutan laut, karena alasan ruang lingkup biaya atau kerugian; dan alasan prinsip-prinsip umum general average.

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ABSTRACT

In sea transport, with the perils of the sea to the vessel and cargo requires settlement called general average. Currently, in Indonesia consisted at least two legal options related to the general average which according to the Indonesian Commercial Code (ICC) and the York-Antwerp Rules 1994 (YAR 1994). The thesis discusses the differences in rules of general average between ICC and YAR 1994, as well as which rules gives more legal protection for the vessel owners and the cargo owners in general average. The research using normative juridical method concluded that is a difference in the scope of costs or losses are included in the general average, and the general principles of general average. In addition, the rule of general average under YAR 1994 giving more legal protection to the owner of the vessel and cargo in sea transport by reason of the scope of costs or losses; and general principles of general average."

"Perubahan pada jadwal pengiriman dan jumlah produk yang dilakukan oleh pelanggan di perusahaan make-to-order menyebabkan kegiatan produksi terhambat pada perusahaan suatu perusahaan kemasan fleksibel. Hal ini menyebabkan sering terjadinya perubahan jadwal produksi serta ketidaktersediaan bahan baku untuk proses produksi. Maka dari itu, peramalan diajukan untuk menanggulangi ketidakpastian permintaan. Penerapan peramalan permintaan dilakukan untuk melakukan Perencanaan Kebutuhan Bahan. Tujuan dari penerapan perencanaan kebutuhan bahan baku adalah dapat memastikan ketersediaan bahan baku serta dapat mengurangi jumlah persediaan. Dalam penelitian ini, metode ARIMA, Holt-Winter’s, dan Jaringan Syaraf Tiruan diajukan untuk meramalkan permintaan pelanggan. Objek pada penelitian ini adalah 3 produk pada perusahaan objek penelitian. Dengan penerapan perencanaan kebutuhan bahan baku, setiap kebutuhan bahan baku untuk permintaan selama bulan oktober hingga desember 2019 dapat terpenuhi. Dalam penelitian ini, ditemukan bahwa penerapan perencanaan kebutuhan bahan baku dapat memenuhi kebutuhan permintaan namun dapat meningkatkan jumlah persediaan dan biaya persediaan yang dikeluarkan. Maka dari itu, peramalan dapat dikatakan berhasil memprediksi permintaan untuk menanggulangi ketidakpastian dan perencanaan kebutuhan bahan baku dapat dijadikan opsi untuk diterapkan pada perusahaan make-to-order dengan permintaan yang tidak pasti.

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Alteration of order quantiy and delivery schedule by the customer is disrupting the production activity in a make-to-order type production corporation. The uncertainty of quantity and schedule creates a frequent occurence of production rescheduling and material stock out for production activity. Forecasting is proposed to tackle the uncertainty of the order quantity and schedule. Material Requirements Planning is used to determine the schedule and quantity needed for each material. The object of this research is three products with the highest order frequency and quantity. In this study, the proposed forecasting methods are Autoregressive Integrated Moving Average (ARIMA), Holt-Winter’s method, and Artificial Neural Network. These method will be compared to know which method are the best for each product by considering the error measurement of the methods. After implementing Material Requirements Planning for the actual demand of october until december 2019. These methods can fullfill every material needed for each product. The findings in this study is Material Requirements Planning can provide the requirements for production while increasing the inventory level and inventory cost. This proves that these methods can be applied as an option for make-to-order production company with an uncertain quantity and schedule of order."

"ABSTRAKPenelitian ini membuktikan pernyataan Dodd dan Favaro 2006 bahwa hasil batting average yang lebih bervariasi dalam industri dibandingkan antar industri. Batting average yang menjadi pengukuran kemampuan perusahaan mengelola three tensions dianalisis pada perusahaan LQ45 dan dibandingkan satu sama lain berdasarkan industri dan pergantian chief executive officer dan dihubungkan dengan total shareholder return. Dari hasil penelitian yang dilakukan, penelitian ini menunjukkan hasil yang searah dengan pernyataan Dodd dan Favaro 2006 bahwa hasil batting average lebih bervariasi dalam industri dibandingkan antar industri, namun tidak memiliki hubungan dengan total shareholder return. Penelitian ini juga menunjukkan perbedaan hasil batting average pada pergantian chief executive officer, yang menunjukkan bahwa variasi batting average yang lebih besar dalam industri disebabkan oleh perbedaan strategi masing-masing perusahaan.

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ABSTRACTThis study proves Dodd and Favaro 2006 statement that the batting average results are more varied within the industry than between industries. Batting averages which indicate the company 39 s ability to manage the three tensions is analyzed at the LQ45 company and compared with each other based on the industry, chief executive officer turnover, and total shareholder return. Based on result on the research conducted, this study shows that the results in line with Dodd and Favaro 2006 statement that the results of the batting average is more varied in the industry than between industries, but in contrast do not have a relationship with TSR. This study also shows differences in the results of the batting average on chief executive officer turnover, which indicates that the variation batting average greater in the industry due to the differences in each company 39 s strategy. "

"Model Kumaraswamy Autoregressive Moving Average (KARMA) merupakan suatu model runtun waktu yang digunakan untuk data runtun waktu yang terbatas pada interval tertentu (a,b) dan diasumsikan mengikuti distribusi Kumaraswamy. Distribusi Kumaraswamy adalah distribusi yang memiliki dua shape parameter, yaitu dan yang menyebabkan distribusi ini memiliki keanekaragaman bentuk grafik fungsi densitas probabilitas seperti unimodal, fungsi naik, fungsi turun, dan fungsi konstan. Pada praktiknya, distribusi ini sering diaplikasikan pada berbagai bidang seperti bidang hidrologi, kesehatan, ekonomi, dan lain-lain. Model KARMA dibentuk dari regresi Kumaraswamy dengan asumsi error model mengikuti proses ARMA. Pada model KARMA, median variabel respon dihubungkan dengan variabel-variabel prediktor (regresor) menggunakan sebuah fungsi penghubung yang monoton, kontinu, dan dapat diturunkan. Metode estimasi parameter model KARMA adalah Conditional Maximum Likelihood Estimation (CMLE) karena dalam proses estimasi diperlukan distribusi bersyarat dari periode sebelumnya. Model KARMA selanjutnya diaplikasikan pada data tingkat mortalitas bulanan akibat kecelakaan kerja di Rio Grande do Sul, Brazil dari Januari 2000 hingga Desember 2017 karena data tingkat mortalitas merupakan data yang terbatas pada interval (0,1). Model KARMA terbaik untuk data dipilih berdasarkan nilai Akaike’s Information Criterion (AIC) terkecil kemudian dilakukan peramalan untuk enam periode selanjutnya. Pada data tingkat mortalitas bulanan akibat kecelakaan kerja di Rio Grande do Sul, digunakan model terbaik KARMA(3,3) dengan nilai MAPE sebesar 19.0988%.

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The Kumaraswamy Autoregressive Moving Average (KARMA) model is a time-series model used for time-series data that is limited to a certain interval (a,b) and is assumed to follow the Kumaraswamy distribution. The Kumaraswamy distribution is a distribution that has two shape parameters, namely and which causes this distribution to have a diverse of graphic forms of probability density functions such as unimodal, increasing functions, decreasing functions, and constant functions. In practice, this distribution is often applied to various fields such as hydrology, health, economics, and other fields. The KARMA model is formed from Kumaraswamy regression assuming the error model follows the ARMA process. In the KARMA model, the median of response variable is linked to the predictor variables (regressor) using a monotonous, continuous, and derivable connecting function. The method used for parameter estimation in KARMA model is Conditional Maximum Likelihood Estimation (CMLE) because a conditional distribution of previous periods is required in the estimation process. The KARMA model will then be applied to monthly mortality rates due to occupational accidents in Rio Grande do Sul, Brazil from January 2000 to December 2017 data because mortality rate data is bounded to the interval (0.1). The best KARMA model for the data was selected based on Akaike's smallest Information Criterion (AIC) values and then forecasted for the next six periods. In the data on the monthly mortality rate due to work accidents in Rio Grande do Sul, a MAPE value of 19.0988% was obtained."