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"ABSTRAKTugas akhir ini membahas mengenai metode Bayes dalam penaksiran parameter skala dari distribusi Nakagami menggunakan dua fungsi loss, yaitu Square Error Loss Function dan Precautionary Loss Function. Pada tugas akhir ini juga akan dicari Resiko Posterior dari masing-masing taksiran. Sebagai pembanding untuk taksiran dengan menggunakan metode Bayes, akan dicari juga taksiran parameter skala dari distribusi Nakagami menggunakan metode Maksimum Likelihood. Sebagai ilustrasi, akan dilakukan simulasi dengan data yang berdistribusi Nakagami ( ). Setelah taksiran telah didapatkan, akan dihitung Mean Square Error dari masing-masing taksiran. Hal tersebut dilakukan untuk mengetahui seberapa baik taksiran yang dihasilkan oleh metode Bayes.

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ABSTRACT

This paper discusses about Bayesian Method in estimating the scale parameter of Nakagami Distribution using two loss function, that is Square Error Loss Function and Precautionary Loss Function. This paper will also find the posterior risk from each of the estimator. As the comparison of the Bayesian estimate, the estimator using Maximum Likelihood method will also be considered. For the illustration, simulation with Nakagami distributed data ( ) will be performed. Once the estimate have been obtained, Mean Square Error on each estimate will be calculated. This is done to measure the performance of the estimate produced by Bayesian method."

"[ABSTRAKMenampilkan data seismic dalam bentuk probabilitas merupakan cara yang umum dilakukan untuk mengikutsertakan informasi ketidak-pastian dari pekerjaan pemetaan prospek hidrokarbon. Hal tersebut memberikan interpreter peluang untuk mengukur seberapa yakin mereka terhadap prospek yang sudah dibuat dengan memanfaatkan informasi nilai ?most-probable?. Pada sisi lain, ketersediaan pre-stack data sudah sangat umum dijumpai sehingga hal ini merubah cara pandang terhadap inversi seismic yang semula hanya dilakukan terhadap data post-stack menjadi inversi pre-stack. Hal tersebut memang beralasan karena dengan inversi pre-stack, interpreter tidak hanya dimungkinkan mendapatkan informasi litologi namun juga informasi tentang fluida.Aturan Bayes adalah merupakan bentuk lain dari probabilitas terkondisi, aturan ini telah banyak dimanfaatkan oleh berbagai disiplin ilmu seperti penginderaan jauh, peramalan cuaca, pemasaran dan ilmu medis untuk membantu dalam meminimalkan resiko saat pengambilan keputusan. Hal yang sama juga bias kita terapkan pada bidang ilmu bumi dimana keluaran dari proses inversi pre-stack dapat ditransformasi menjadi bentuk volum probabilitas dengan supervisi data sumuran.Penelitian ini menggunakan P-impedance dan VP/VS sebagai input karena kombinasi keduanya merupakan indikator yang baik untuk memisahkan litologi maupun hidrokarbon. Dengan menggunakan supervisi dari data sumuran kedua volume tersebut kemudian di transformasi menjadi bentuk kelas most-probable: (1) shale, (2) wet sand, (3) compacted sand, dan (4) hydrocarbon sand.

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ABSTRACTPresenting seismic data in probability form is common practice in order to assess the uncertainty in hydrocarbon prospecting. It gives interpreters the ability to measure how sure they are about prospect they dealing with by looking at most probable value. In another side pre-stack data is now commonly available; it changes the paradigm about seismic inversion from just post-stack inversion turn into pre-stack inversion. The reason is obvious, by inverting pre-stack data will allow interpreter to obtain not only lithology information but fluid as well.The Bayes? Rule is extension of conditional probability, it has been utilizes in many disciplines such us remote sensing, broadcasting, marketing and medical science to support in decision making. Bayes? Rule is used to revise a probability value based on additional information that is later obtained. The same concept can also be applied to help decision making in hydrocarbon prospect evaluation where the output of pre-stack inversion can be transformed to probability volume supervised by well log data.This study uses P-Impedance and VP/VS as inputs because their combination is good indicator of lithology and hydrocarbon. Using training set from well log the volumes then transformed into four most probable classes: (1) shale, (2) wet sand, (3) compacted sand, and (4) hydrocarbon sand.;Presenting seismic data in probability form is common practice in order to assess the uncertainty in hydrocarbon prospecting. It gives interpreters the ability to measure how sure they are about prospect they dealing with by looking at most probable value. In another side pre-stack data is now commonly available; it changes the paradigm about seismic inversion from just post-stack inversion turn into pre-stack inversion. The reason is obvious, by inverting pre-stack data will allow interpreter to obtain not only lithology information but fluid as well.The Bayes? Rule is extension of conditional probability, it has been utilizes in many disciplines such us remote sensing, broadcasting, marketing and medical science to support in decision making. Bayes? Rule is used to revise a probability value based on additional information that is later obtained. The same concept can also be applied to help decision making in hydrocarbon prospect evaluation where the output of pre-stack inversion can be transformed to probability volume supervised by well log data.This study uses P-Impedance and VP/VS as inputs because their combination is good indicator of lithology and hydrocarbon. Using training set from well log the volumes then transformed into four most probable classes: (1) shale, (2) wet sand, (3) compacted sand, and (4) hydrocarbon sand.;Presenting seismic data in probability form is common practice in order to assess the uncertainty in hydrocarbon prospecting. It gives interpreters the ability to measure how sure they are about prospect they dealing with by looking at most probable value. In another side pre-stack data is now commonly available; it changes the paradigm about seismic inversion from just post-stack inversion turn into pre-stack inversion. The reason is obvious, by inverting pre-stack data will allow interpreter to obtain not only lithology information but fluid as well.The Bayes? Rule is extension of conditional probability, it has been utilizes in many disciplines such us remote sensing, broadcasting, marketing and medical science to support in decision making. Bayes? Rule is used to revise a probability value based on additional information that is later obtained. The same concept can also be applied to help decision making in hydrocarbon prospect evaluation where the output of pre-stack inversion can be transformed to probability volume supervised by well log data.This study uses P-Impedance and VP/VS as inputs because their combination is good indicator of lithology and hydrocarbon. Using training set from well log the volumes then transformed into four most probable classes: (1) shale, (2) wet sand, (3) compacted sand, and (4) hydrocarbon sand.;Presenting seismic data in probability form is common practice in order to assess the uncertainty in hydrocarbon prospecting. It gives interpreters the ability to measure how sure they are about prospect they dealing with by looking at most probable value. In another side pre-stack data is now commonly available; it changes the paradigm about seismic inversion from just post-stack inversion turn into pre-stack inversion. The reason is obvious, by inverting pre-stack data will allow interpreter to obtain not only lithology information but fluid as well.The Bayes? Rule is extension of conditional probability, it has been utilizes in many disciplines such us remote sensing, broadcasting, marketing and medical science to support in decision making. Bayes? Rule is used to revise a probability value based on additional information that is later obtained. The same concept can also be applied to help decision making in hydrocarbon prospect evaluation where the output of pre-stack inversion can be transformed to probability volume supervised by well log data.This study uses P-Impedance and VP/VS as inputs because their combination is good indicator of lithology and hydrocarbon. Using training set from well log the volumes then transformed into four most probable classes: (1) shale, (2) wet sand, (3) compacted sand, and (4) hydrocarbon sand., Presenting seismic data in probability form is common practice in order to assess the uncertainty in hydrocarbon prospecting. It gives interpreters the ability to measure how sure they are about prospect they dealing with by looking at most probable value. In another side pre-stack data is now commonly available; it changes the paradigm about seismic inversion from just post-stack inversion turn into pre-stack inversion. The reason is obvious, by inverting pre-stack data will allow interpreter to obtain not only lithology information but fluid as well.The Bayes’ Rule is extension of conditional probability, it has been utilizes in many disciplines such us remote sensing, broadcasting, marketing and medical science to support in decision making. Bayes’ Rule is used to revise a probability value based on additional information that is later obtained. The same concept can also be applied to help decision making in hydrocarbon prospect evaluation where the output of pre-stack inversion can be transformed to probability volume supervised by well log data.This study uses P-Impedance and VP/VS as inputs because their combination is good indicator of lithology and hydrocarbon. Using training set from well log the volumes then transformed into four most probable classes: (1) shale, (2) wet sand, (3) compacted sand, and (4) hydrocarbon sand.]"

"ABSTRAKPresenting seismic data in probability form is common practice in order to assess the uncertainty in hydrocarbon prospecting. It gives interpreters the ability to measure how sure they are about prospect they dealing with by looking at most probable value. In another side pre-stack data is now commonly available; it changes the paradigm about seismic inversion from just post-stack inversion turn into pre-stack inversion. The reason is obvious, by inverting pre-stack data will allow interpreter to obtain not only lithology information but fluid as well.The Bayes’ Rule is extension of conditional probability, it has been utilizes in many disciplines such us remote sensing, broadcasting, marketing and medical science to support in decision making. Bayes’ Rule is used to revise a probability value based on additional information that is later obtained. The same concept can also be applied to help decision making in hydrocarbon prospect evaluation where the output of pre-stack inversion can be transformed to probability volume supervised by well log data.This study uses P-Impedance and VP/VS as inputs because their combination is good indicator of lithology and hydrocarbon. Using Menampilkan data seismic dalam bentuk probabilitas merupakan cara yang umum dilakukan untuk mengikutsertakan informasi ketidak-pastian dari pekerjaan pemetaan prospek hidrokarbon. Hal tersebut memberikan interpreter peluang untuk mengukur seberapa yakin mereka terhadap prospek yang sudah dibuat dengan memanfaatkan informasi nilai “most-probable”. Pada sisi lain, ketersediaan pre-stack data sudah sangat umum dijumpai sehingga hal ini merubah cara pandang terhadap inversi seismic yang semula hanya dilakukan terhadap data post-stack menjadi inversi pre-stack. Hal tersebut memang beralasan karena dengan inversi pre-stack, interpreter tidak hanya dimungkinkan mendapatkan informasi litologi namun juga informasi tentang fluida.Aturan Bayes adalah merupakan bentuk lain dari probabilitas terkondisi, aturan ini telah banyak dimanfaatkan oleh berbagai disiplin ilmu seperti penginderaan jauh, peramalan cuaca, pemasaran dan ilmu medis untuk membantu dalam meminimalkan resiko saat pengambilan keputusan. Hal yang sama juga bias kita terapkan pada bidang ilmu bumi dimana keluaran dari proses inversi pre-stack dapat ditransformasi menjadi bentuk volum probabilitas dengan supervisi data sumuran.Penelitian ini menggunakan P-impedance dan VP/VS sebagai input karena kombinasi keduanya merupakan indikator yang baik untuk memisahkan litologi maupun hidrokarbon. Dengan menggunakan supervisi dari data sumuran kedua volume tersebut kemudian di transformasi menjadi bentuk kelas most-probable: (1) shale, (2) wet sand, (3) compacted sand, dan (4) hydrocarbon sand.

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ABSTRACTPresenting seismic data in probability form is common practice in order to assess the uncertainty in hydrocarbon prospecting. It gives interpreters the ability to measure how sure they are about prospect they dealing with by looking at most probable value. In another side pre-stack data is now commonly available; it changes the paradigm about seismic inversion from just post-stack inversion turn into pre-stack inversion. The reason is obvious, by inverting pre-stack data will allow interpreter to obtain not only lithology information but fluid as well.The Bayes’ Rule is extension of conditional probability, it has been utilizes in many disciplines such us remote sensing, broadcasting, marketing and medical science to support in decision making. Bayes’ Rule is used to revise a probability value based on additional information that is later obtained. The same concept can also be applied to help decision making in hydrocarbon prospect evaluation where the output of pre-stack inversion can be transformed to probability volume supervised by well log data.This study uses P-Impedance and VP/VS as inputs because their combination is good indicator of lithology and hydrocarbon. Using training set from well log the volumes then transformed into four most probable classes: (1) shale, (2) wet sand, (3) compacted sand, and (4) hydrocarbon sand."

"Dalam pengujian hipotesis berganda, dilakukan pengujian lebih darisatu hipotesis, yang diuji pada satu waktu secara simultan. Apabila masingmasingpengujian dalam suatu family hipotesis mempunyai probabilitasmelakukan kesalahan tipe 1, maka secara keseluruhan pada pengujianhipotesis berganda akan terjadi penggandaan probabilitas kesalahan tipe 1.Probabilitas melakukan kesalahan tipe1 pada pengujian hipotesis bergandaakan semakin membesar seiring dengan meningkatnya jumlah pengujian.Untuk mengatasi hal itu, ada beberapa cara untuk mengukur kesalahan tipe1dalam family hipotesis diantaranya Family Wise Error Rate (FWER), FalseDiscovery Rate (FDR), dan positif False Discovery Rate (pFDR). Untukmengontrol kesalahan tersebut, diperlukan suatu metode sedemikiansehingga probabilitas kesalahan tipe 1 keseluruhan ≤ α. Pada tugas akhir ini,akan dibahas metode - metode pengujian untuk hipotesis berganda yaitumetode Bonferroni yang merupakan salah satu metode untuk FWER, metodeBenjamin-Hochberg untuk FDR yang memperbaiki Metode Bonferroni danmetode Storey untuk pFDR yang memperbaiki Metode Benjamin-Hochberg."

"Kinerja mahasiswa adalah bagian penting dari suatu perguruan tinggi. Hal ini dikarenakan salah satu kriteria  perguruan tinggi yang berkualitas didasarkan pada  prestasi akademik yang baik. Tahun pertama perkuliahan adalah periode mahasiswa untuk meletakkan dasar atau fondasi yang selanjutnya akan mempengaruhi keberhasilan akademik karena tahun pertama memainkan peran penting dalam membentuk sikap dan kinerja siswa di tahun-tahun berikutnya. Pada Penelitian ini, pendekatan Semi-supevised Learning digunakan dalam mengklasifikasi kinerja mahasiswa tahun pertama di Departemen Matematika, Universitas Indonesia. Kinerja Mahasiswa dibagi menjadi dua kategori, yaitu sedang dan tinggi. Sampel pada penelitian ini adalah 140 mahasiswa tahun pertama dengan menggunakan 27 fitur. Ada dua proses yang digunakan, yaitu proses clustering dan klasifiksi. Pada proses clustering, mahasiswa dibagi menjadi tiga cluster/kelompok menggunakan K-Means Clustering. Sedangkan dalam proses klasifikasinya menggunakan Naïve Bayes Classifier. Kinerja algoritma yang diusulkan menghasilkan nilai akurasi 96.67% dan sensitifitas 94.44%.

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Students performance is an essential part of a higher learning institution because one of the criteria for a high-quality university is based on its excellent record of academic achievements. The first- year of the lecture is the student period in laying the foundation that will affect academic success because first-year plays an important role in shaping the attitudes and performance of students in the following years. In this study, a semi-supervised learning approach is used to classify the performance of first-year students in the Department of Mathematics, Universitas Indonesia. Student performance will be divided into two categories, namely medium and high. The sample in this study consist of 140 first-year students with 27 features. There are two processes used i.e. clustering and the classification process. In the clustering process, the data is divided into three clusters using K-Means Clustering and the Naïve Bayes Classifier is chosen to classify it. The performance of the proposed algorithms is stated by accuracy and sensitivity value i.e. 96.67% and 94.44% respectively."

"[Letak geografis Indonesia berada di pertemuan dua lempeng yaitu Asia dan Australia, hal ini menyebabkan banyak terdapat gunung merapi. Selain itu pegunungan di Indonesia didominasi oleh gunung yang aktif dan berpotensi meletus sewaktu-waktu. Kondisi tersebut mengharuskan warga negara Indonesia, khususnya yang tinggal di sekitaran pegunungan berapi aktif tanggap akan bencana yang letusan gunung berapi. Berkembangnya sains dan teknologi informasi dapat memberi kontribusi dalam rangka mitigasi bencana geologi yang diakibatkan aktivitas vulkanik dalam bentuk aplikasi pendeteksi potensi bencana gunung merapi. Penelitian ini menghasilkan suatu aplikasi pendeteksi status gunung berapi. Data latih yang digunakan diambil dari situs Pusat Vulkanologi dan Mitigasi Bencana Gunung Berapi yang diklasifikasikan dalam tiga status yaitu normal, siaga, dan waspada. Pengklasifikasi Bayesian digunakan karena merupakan metode berbasis probabilitas yang sederhana namun handal. Berdasarkan hasil pengujian dengan jumlah data latih terbesar mencapai tingkat akurasi 90%. Dengan demikian dapat dikatakan bahwa aplikasi pendeteksi potensi bencana gunung berapi memiliki performa yang baik dalam mengklasifikasi status gunung berapi., Letak geografis Indonesia berada di pertemuan dua lempeng yaitu Asia dan Australia, hal ini menyebabkan banyak terdapat gunung merapi. Selain itu pegunungan di Indonesia didominasi oleh gunung yang aktif dan berpotensi meletus sewaktu-waktu. Kondisi tersebut mengharuskan warga negara Indonesia, khususnya yang tinggal di sekitaran pegunungan berapi aktif tanggap akan bencana yang letusan gunung berapi. Berkembangnya sains dan teknologi informasi dapat memberi kontribusi dalam rangka mitigasi bencana geologi yang diakibatkan aktivitas vulkanik dalam bentuk aplikasi pendeteksi potensi bencana gunung merapi. Penelitian ini menghasilkan suatu aplikasi pendeteksi status gunung berapi. Data latih yang digunakan diambil dari situs Pusat Vulkanologi dan Mitigasi Bencana Gunung Berapi yang diklasifikasikan dalam tiga status yaitu normal, siaga, dan waspada. Pengklasifikasi Bayesian digunakan karena merupakan metode berbasis probabilitas yang sederhana namun handal. Berdasarkan hasil pengujian dengan jumlah data latih terbesar mencapai tingkat akurasi 90%. Dengan demikian dapat dikatakan bahwa aplikasi pendeteksi potensi bencana gunung berapi memiliki performa yang baik dalam mengklasifikasi status gunung berapi.]"

"This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics."