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"Multiple dirichlet series, L-functions and automorphic forms gives the latest advances in the rapidly developing subject of Multiple Dirichlet Series, an area with origins in the theory of automorphic forms that exhibits surprising and deep connections to crystal graphs and mathematical physics. As such, it represents a new way in which areas including number theory, combinatorics, statistical mechanics, and quantum groups are seen to fit together. The volume also includes papers on automorphic forms and L-functions and related number-theoretic topics. "

"[This text provides a concise introduction to the world of automorphic forms using two approaches, the classic elementary theory and the modern point of view of adeles and representation theory. The reader will learn the important aims and results of the theory by focussing on its essential aspects and restricting it to the 'base field' of rational numbers., This text provides a concise introduction to the world of automorphic forms using two approaches, the classic elementary theory and the modern point of view of adeles and representation theory. The reader will learn the important aims and results of the theory by focussing on its essential aspects and restricting it to the 'base field' of rational numbers.]"

"Partitions, q-series, and modular forms contains a collection of research and survey papers that grew out of a Conference on partitions, q-series and modular forms at the University of Florida, Gainesville in March 2008. It will be of interest to researchers and graduate students that would like to learn of recent developments in the theory of q-series and modular and how it relates to number theory, combinatorics and special functions."

"Pada tugas akhir ini akan dibahas suatu generalisasi dari distribusi Dirichlet yangdisebut Dirichlet process. Distribusi Dirichlet merupakan distribusi atas vektor,dimana elemen dari vektor tersebut merupakan bilangan-bilangan diantara 0 dan 1dan dapat dianggap sebagai nilai probabilitas. Sehingga, distribusi Dirichlet dapatdianggap sebagai distribusi atas vektor probabilitas. Sedangkan Dirichlet processdidefinisikan sebagai suatu distribusi atas probability measure atau disebut jugadengan probability set function. Untuk suatu ruang terukur  ,  B , probabilitymeasure P yang didefinisikan pada -algebra Bdisebut berdistribusi Dirichletprocess jika untuk suatu partisi terukur B1, B2, …, Bk dari , vektor random(P(B1), P(B2), …, P(Bk)) berdistribusi Dirichlet. Probability measure P tersebutdapat dianggap sebagai suatu distribusi probabilitas di . Dan untukmenghasilkan barisan variabel random yang berdistribusi P dimana P berdistribusiDirichlet process, digunakan suatu teori barisan Polya.Bentuk eksplisit dariprobability measure P yang berdistribusi Dirichlet process dijelaskan melalui theSethuraman construction of Dirichlet process. Pada tugas akhir ini, penerapan dariDirichlet process adalah dalam penentuan distribusi prior pada regresi binersemiparametrik."

"ABSTRAKEkstraksi topik merupakan tugas utama dalam penambangan teks sebagai upaya mengeluarkan informasi yang terpendam dalam teks secara heuristik. Proses ini dilakukan lewat pemodelan topik yakni sebuah proses mengidentifikasi topik- topik yang ada dalam sebuah objek teks atau menurunkan pola-pola tersembunyi dalam sebuah korpus teks. Dalam penelitian ini pemodelan topik diaplikasikan pada data teks berbahasa Indonesia menggunakan modul program bernama Gensim dalam bahasa pemrograman Python. Dataset terdiri dari 93 dokumen berita daring Kompas dengan beragam klasifikasi. Jumlah topik optimal yang diperoleh diuji menggunakan machine learning clustering k-means. Dalam proses penelitian ini ternyata diperlukan suatu mekanisma umpanbalik manual untuk mereduksi noise agar diperoleh pemodelan topik yang lebih baik. Hasil uji memperlihatkan teknik Latent Dirichlet Allocation LDA yang telah ditingkatkan / dimodifikasi LDA as LSI memiliki koherensi topik yang jauh lebih baik dibanding teknik LDA saja dalam penelitian ini: 0.94 dibanding 0.34 . Koherensi yang tinggi mengindikasikan bahwa topik hasil pemodelan ini merupakan topik yang dapat dijelaskan dengan sedikit label.

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ABSTRACTTopic extraction is main task in text mining as an effort to dig buried information within text heuristically. This process is done through topic modeling, a process to identify topics within text object or to derive hidden patterns in a text corpus. In this research, topic modeling is applied to Indonesian language texts using Gensim module in Python programming language. The dataset consists of 93 online news documents from Indonesian national newspaper, Kompas, with several different classifications. The identified optimum number of topics k is visualized using clustering machine learning k means. In the process of this research turned out to need a mechanism of manual feedback for noise reduction in order to get better topic modeling. The test results show that enhanced modified Latent Dirichlet Allocation LDA as LSI has a much better topic coherence than LDA technique alone in this study 0.94 compared to 0.34 . High coherence indicates that topics resulting from this topic modeling is a topic that can be explained with few labels. "