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"Distribusi Transmuted Generalized Rayleigh merupakan distribusi kontinu yang dapat memodelkan data dengan hazard rate berbentuk naik, turun, atau bathtub. Distribusi Transmuted Generalized Rayleigh merupakan generalisasi dari distribusi Generalized Rayleigh yang dibentuk dengan menggunakan metode quadratic rank transmutation maps. Distribusi Generalized Rayleigh sendiri merupakan generalisasi dari distribusi Rayleigh yaitu yang diperoleh dengan memangkatkan distribusi Rayleigh dengan parameter baru. Sedangkan distribusi Rayleigh adalah distribusi yang dibentuk dari distribusi Normal dengan menggunakan teknik fungsi distribusi. Pada penelitian ini, akan dibahas tentang pembentukan distribusi Rayleigh, Generalized Rayleigh, dan Transmuted Generalized Rayleigh, fungsi kepadatan probabilitas, fungsi distribusi, fungsi survival, fungsi hazard, dan karakteristik-karakteristik dari ketiga distribusi tersebut. Penaksiran parameter menggunakan metode maksimum likelihood. Sebagai ilustrasi, akan digunakan data tentang tinggi gelombang untuk memperjelas penggunaan ketiga distribusi.

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Transmuted Generalized Rayleigh distribution is a continued distribution which can model data with increase, decrease, or baththub hazard rate. Transmuted Generalized Rayleigh distribution is derived from generalization of Generalized Rayleigh distribution which built using quadratic rank transmutation maps method. Generalized Rayleigh distribution itself is generalization of Rayleigh distribution, that is a power to new parameter from Rayleigh distribution. Rayleigh distribution is derived from Normal distribution which built using distribution function technique. In this research, we will discuss how to construct Rayleigh, Generalized Rayleigh, and Transmuted Generalized Rayleigh distribution, probability distribution function, cumulative distribution function, survival function, hazard function, and characteristrics of these distributions. Finally, height of waves data are used to explain about use of the third distributions."

"Prediksi harga saham merupakan hal yang sangat penting bagi investor karena sangat berguna untuk menentukan nilai masa depan dari suatu perusahaan yang sahamnya sedang diperdagangkan di bursa efek. Investor akan mendapatkan keuntungan yang besar dengan prediksi yang tepat, sebaliknya investor akan mendapatkan kerugian jika prediksi yang digunakan tidak tepat. Pada skripsi ini, akan dibahas pembuatan model prediksi Adaptive Neuro Fuzzy Inference System ANFIS dengan menggunakan variabel indikator teknikal terbaik berdasarkan Support Vector Regression SVR yang dilihat dari kecenderungan data historis saham 25 perusahaan dari sub sektor Bank, sektor Keuangan, yang tercatat di Bursa Efek Indonesia. Melalui metode ini, akan didapatkan nilai akurasi model yang cukup baik sedemikian sehingga dapat menjadi rekomendasi bagi investor dalam melakukan prediksi harga saham berdasarkan variabel indikator teknikal terpilih.

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Forecasting stock price has become an important issue for stock investors because it is very useful to determine the future value of a company whose its share are traded on the stock exchange. Investors will get a profit with a sharp predictions, otherwise they will get loss if the predictions is inappropriately used. This undergraduate thesis will study how to make a model prediction Adaptive Neruo Fuzzy Inference System ANFIS using the best technical indicators. These technical indicators chosen by using Support Vector Regression SVR referred from the tendencies of stock time series data for 25 companies of Banking sub sector, Financial sector, that listed on Indonesian Stock Exchange. Through this method, analyst will get the value of the model rsquo s accuracy, that is good enough. So that it could be a recommendation for investors for forecasting the stock prices using this method with the selected technical indicators."

"ABSTRAKPenaksir merupakan statistik dari sampel yang digunakan untuk menaksir parameter populasi. Salah satu kriteria penaksir yang baik adalah penaksir dengan presisi yang tinggi. Dalam teori sampling, auxiliary information sering digunakan untuk meningkatkan presisi penaksir. Auxiliary information yang tersedia dalam bentuk kualitatif disebut auxiliary attribute. Nursel Koyuncu memperkenalkan keluarga penaksir mean populasi yang dapat memanfaatkan auxiliary attribute beserta parameternya. Mean square error dari keluarga penaksir Nursel Koyuncu akan dirumuskan. Akan ditunjukkan jika penaksir mean populasi yang dibentuk dari keluarga penaksir Nursel Koyuncu lebih efisien dibandingkan mean sampel. Pembuktian teori ini akan didukung dengan contoh numerik dengan menggunakan data asli.

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ABSTRACTEstimator is a statistic of the samples used to estimate population parameters. One of a good estimator criteria is the estimator has high precision. In sampling theory, auxiliary information often being used to improve the precision of the estimator. Auxiliary information which provided in qualitative form is called auxiliary attribute. Nursel Koyuncu was introducing family of mean population estimator that can utilize the auxiliary attribute and its parameters. The mean square error of the Nursel Koyuncu family estimator will be formulated. Will be indicated that the adjuster is formed from the Nursel Koyuncu rsquo s family of mean population estimator is more efficient than the sample mean. Proof of this theory would be supported by the numerical example using original data. "

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ABSTRAK

Nama                   : Tiffany Chandra

Program Studi     : Psikologi Profesi, Peminatan Psikologi Pendidikan

Judul Tesis           : Pengaruh Math Anxiety Terhadap Math Achievement yang Dimediasi

oleh Metacognitive Knowledge pada Siswa Kelas V Sekolah Dasar

 

 

Penelitian ini bertujuan untuk mengetahui apakah terdapat pengaruh math anxiety terhadap math achievement yang signifikan melalui masing-masing dimensi metacognitive knowledge ataupun secara keseluruhan. Data penelitian dikumpulkan dari 370 siswa-siswi kelas V di lima SD yang berada di wilayah Jakarta Selatan. Pengumpulan data dilakukan menggunakan kuesioner yang terdiri dari alat ukur Math Achievement Test, Modified Abbreviated Math Anxiety Scale (mAMAS), serta Metacognitive Knowledge in Mathematics Questionnaire (MKMQ). Berdasarkan hasil uji parallel multiple mediation analysis yang dilakukan, diketahui bahwa math anxiety berpengaruh signifikan terhadap math achievement melalui peran metacognitive knowledge sebagai mediator. Selain itu, math anxiety juga berpengaruh signifikan tehadap math achievement baik melalui dimensi metacognitive knowledge of self (a₁b₁ = -0,08, BCa CI [-0,11, -0,05]) maupun melalui metacognitive knowledge of task (a­₂b₂ = -0,05, BCa CI [-0,08, -0,03]). Kendati demikian, pengaruh math anxiety terhadap math achievement ternyata tidak signifikan saat dimediasi oleh metacognitive knowledge of strategy (a₃b₃ = -0,01, BCa CI [-0,02, 0,01]). Adapun hasil dan limitasi penelitian dikaji lebih mendalam pada bagian diskusi.

 

Kata kunci:

Prestasi matematika, kecemasan matematika, metacognitive knowledge, siswa kelas V

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ABSTRACT

Name                           : Tiffany Chandra

Study Programme       : Master Programme, Psychology in Education

Judul Tesis                  : The Relationships between Fifth Graders’ Math Anxiety and

Math Achievement Mediated by Metacognitive Knowledge

 

 

This study aimed to determine whether there is a significant effect of math anxiety on math achievement through each dimension of metacognitive knowledge, as well as through metacognitive knowledge itself. Data were collected from 370 fifth graders in five different elementary schools at South Jakarta. Data were collected through a set of questionnaires consisting of Math Achievement Test, Modified Abbreviated Math Anxiety Scale (mAMAS), and Metacognitive Knowledge in Mathematics Questionnaire (MKMQ). Based on the results of the parallel multiple mediation analysis, it was found that math anxiety significantly affected math achievement through the role of metacognitive knowledge as the mediator. Math anxiety also has a significant effect on math achievement both through metacognitive knowledge of self (a₁b₁ = -0.08, BCa CI [-0.11, -0.05]) and through the metacognitive knowledge of task (a­₂b₂ = -0.05, BCa CI [-0.08, -0.03]). However, the effect of math anxiety on math achievement was not significant when mediated by metacognitive knowledge of strategy (a₃b₃ = -0.01, BCa CI [-0.02, 0.01]). The results and limitations of this study are examined deeper in the discussion section.

 

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"This unique textbook/​reference presents unified coverage of bioinformatics topics relating to both biological sequences and biological networks, providing an in-depth analysis of cutting-edge distributed algorithms, as well as of relevant sequential algorithms. In addition to introducing the latest algorithms in this area, more than fifteen new distributed algorithms are also proposed. Topics and features: Reviews a range of open challenges in biological sequences and networks, beginning with an informal description of the problem before defining it formally Describes in detail both sequential and parallel/​distributed algorithms for each problem, briefly discussing software packages if there are any available Suggests approaches for distributed algorithms as possible extensions to sequential algorithms, when the distributed algorithms for the topic are scarce Proposes a number of new distributed algorithms in each chapter, to serve as potential starting points for further research Concludes each chapter with self-test exercises, a summary of the key points, a comparison of the algorithms described, and a literature review This clearly-written and easy to follow work is ideal as a textbook for graduate and senior undergraduate students of computer science and biology, and as a self-study guide for any interested reader with a basic background in discrete mathematic s and algorithms. Researchers in bioinformatics will also find the book to be a useful reference on this subject. Dr. K. Erciyes is Rector of Izmir University, Turkey, where he also serves as a professor in the Computer Engineering Department. His other publications include the Springer title Distributed Graph Algorithms for Computer Networks."

"Tesis ini bertujuan untuk menganalisis stabilitas model Brennan-Schwartz dan menggunakannya sebagai panduan dalam menganalisis tingkat bunga. Stabilitas penting untuk menggambarkan ketahanan suatu model terhadap gangguan pada nilai awal ataupun parameter modelnya. Pada Tesis ini akan dibahas dua cara untuk menentukan stabilitas stokastik, yaitu stabilitas stokastik asimtotik dan stabilitas mean-square. Kriteria-kriteria stabilitas yang diperoleh dapat digunakan sebagai panduan untuk memilih parameter sehingga model menjadi tahan terhadap gangguan. Akan tetapi, pada kenyataannya parameter model Brennan-Schwartz tidak diketahui nilainya sehingga perlu dilakukan penaksiran terlebih dahulu. Pada Tesis ini, metode yang digunakan dalam menaksir parameter model Brennan-Schwartz adalah metode Maximum Likelihood dan dilanjutkan secara iteratif menggunakan Algoritma Nelder-Mead. Dalam implementasi, taksiran parameter diperoleh melalui penerapan konsep perubahan measure. Hasil implementasi menunjukkan bahwa solusi model Brennan-Schwartz cukup baik dalam menggambarkan pergerakan tingkat bunga bulanan dari suatu zero-coupon bond dengan maturity time 5 tahun periode Januari tahun 1982 hingga Februari 2011 yang datanya diunduh dari www.bankofengland.co.uk.

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This thesis aims to analyze the stability of the Brennan-Schwartz model and use it as a guideline to analyze interest-rate. Stability is important to describe resistance of the model to the perturbation in the initial state or parameters of the model. Two ways to define stochastic stability will be considered in this thesis: stochastically asymptotically stable and mean-square stability. These stability criteria can be used as guidelines for selecting parameters that make the model resistant to the perturbation. However, Brennan-Schwartz model requires estimation of parameters whose values are unknown. In this thesis, the method which is used to estimate parameters of Brennan-Schwartz model is the maximum likelihood estimation method and will be continued iteratively using the Nelder-Mead Algorithm. In the application, parameter estimators are obtained by applying change of measure concept. Implementations show that Brennan-Schwartz model is good enough to approximate the real data of monthly interestrate from a zero-coupon bond with maturity time of 5 years: January, 1982 - February, 2011 in which data is downloaded from www.bankofengland.co.uk."

"Pertidaksamaan Hermite-Hadamard merupakan pertidaksamaan yang melibatkan integral yang berlaku pada fungsi konveks. Pertidaksamaan Hermite-Hadamard-Fej r merupakan perumuman dari pertidaksamaan Hermite-Hadamard dengan memberi bobot sebuah fungsi dengan syarat-syarat tertentu. Pengembangan dari pertidaksamaan Hermite-Hadamard-Fej r selanjutnya dapat berupa perumuman dari pertidaksamaan tersebut yang berlaku untuk integral fraksional. Pada penelitian ini dibahas mengenai bentuk-bentuk pertidaksamaan tipe Hermite-hadamard-Fej r yang berlaku untuk fungsi terturunkan dengan mutlak dari fungsi turunannya konveks melalui integral fraksional Riemann-Liouville. Penelitian ini merupakan studi literatur dari hasil yang sudah ada. Pertidaksamaan pada hasil yang diperoleh menunjukkan eksistensi dari pertidaksamaan tipe Hermite-Hadamard yang berlaku untuk jenis fungsi yang sama.

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Hermite Hadamard inequality is an integral inequality holds for convex function. Hermite Hadamard Fej r inequality is the generalization of Hermite Hadamard inequality by giving a weight such a function with certain criterions. The next developed version of Hermite Hadamard Fej r inequality might be it's generalization holds for fractional integral. This study is about Hermite Hadamard Fej r type inequalities for differentiable mappings whose derivatives in absolute value are convex via fractional integral. This research is literature study by results that already exist. The obtained inequalities provided existence of Hermite Hadamard type inequalities for the same type functions."