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Hasil Pencarian

Ditemukan 199 dokumen yang sesuai dengan query
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Besag, Frank P.
Beverly Hills: Sage, 1985
519.5 BES s
Buku Teks  Universitas Indonesia Library
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Anto Dajan
Jakarta: LP3S, 1991
001.422 ANT p
Buku Teks  Universitas Indonesia Library
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Abrami, Philip C.
Boston: Allyn and Bacon, 2001
519.5 ABR s
Buku Teks  Universitas Indonesia Library
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Dubois, Edward N.
New York: McGraw-Hill, 1979
519.5 DUB e
Buku Teks  Universitas Indonesia Library
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Wallraven, Douglas W.
New York: CRC Press, 2012
519.57 WAL e
Buku Teks  Universitas Indonesia Library
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Bernadia Puspasari
"Mesin Penerjemah digunakan untuk menerjemahkan teks dari suatu bahasa ke bahasa lain secara otomatis. Mesin Penerjemah Statistik adalah Mesin Penerjemah yang menggunakan pendekatan statistik dalam proses menerjemahkan teks. Penelitian dilakukan dengan menggunakan Mesin Penerjemah Statistik berdasarkan frase yang memanfaatkan korpus dwibahasa paralel sebagai data pelatihannya.
Korpus dwibahasa Indonesia - Jepang yang digunakan berupa koleksi dokumen Kitab Suci, artikel berita, dan percakapan sehari-hari dengan jumlah keseluruhan kalimat sebanyak 24.365 kalimat. Koleksi dokumen dalam bahasa Jepang tersedia dalam dua macam bentuk tulisan, yaitu Kanji atau Romaji. Penelitian dilakukan pada korpus dwibahasa tanpa faktor tambahan dan korpus yang menggunakan perangkat bahasa tambahan, yaitu lema.
Dari hasil penelitian, didapati bahwa kinerja penerjemahan teks Indonesia - Jepang menggunakan Mesin Penerjemah Statistik berdasarkan frase pada penelitian ini, nilai akurasi tertinggi berdasarkan BLEU score mencapai 0,2027. Nilai akurasi tertinggi tersebut didapatkan pada jenis dokumen artikel berita tanpa faktor tambahan dengan model bahasa 5-gram. Sedangkan penambahan perangkat bahasa lema pada korpus pelatihan menurunkan kinerja dari Mesin Penerjemah Statistik berdasarkan frase.

Machine Translation translates text from one language to another automatically. Statistical Machine Translation uses statistical approach to translate text. This research uses phrase-based Statistical Machine Translation system. We use Indonesian ? Japanese bilingual corpora as the training data which consist of holy writings, news article, and daily conversation with total of 24.365 sentences. Japanese document collections are written in Kanji and Romaji.
This research uses unfactored training corpora and factored training corpora (lemma). The highest accuracy based on evaluation of the translation result is 0.2027 in BLEU score which is the score of news article document written in Romaji using 5-gram language model. Factored training corpora (lemma) decreases the performance of the machine translation system."
Depok: Fakultas Ilmu Komputer Universitas Indonesia, 2009
S-Pdf
UI - Skripsi Open  Universitas Indonesia Library
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Miftahu Rahmatika
"Badan Pusat Statistik (BPS) merupakan lembaga pemerintahan non-kementerian yang berwenang menyelenggarakan statistik dasar. Proses bisnis utama statistik BPS, yang dikenal dengan SBFA (Statistical Business Framework and Architecture) dan merujuk pada standar proses bisnis statistik internasional GSBPM (Generic Statistical Business Process Model), memiliki delapan fase (specify need, design, build, collect, process, analyze, disseminate, dan evaluate). Metadata diperlukan di setiap fase tersebut untuk meningkatkan efisiensi dan efektifitas dalam proses bisnis statistik. Dengan ditandatanganinya Peraturan Presiden Nomor 39 Tahun 2019 tentang SDI (Satu Data Indonesia) dan semakin bertambahnya produk statistik BPS, maka BPS perlu bergegas untuk meningkatkan pengelolaan metadatanya. Inisiatif BPS untuk membangun sebuah Metadata Management System (MMS) menemui kendala karena metadata kegiatan statistik saat ini tidak lengkap dan belum sesuai standar GSIM (Generic Statistical Information Model). Tujuan utama penelitian ini adalah untuk memberikan rekomendasi model data metadata statistik yang sesuai dengan proses bisnis statistik BPS dan standar metadata yang ditetapkan untuk mendukung pembangunan MMS. Penentuan obyek informasi metadata dilakukan dengan penyelarasan obyek informasi yang diperoleh dari proses bisnis BPS terhadap spesifikasi GSIM. Obyek informasi tersebut kemudian dimodelkan sampai dengan level physical data model, agar mudah diimplementasikan ke dalam MMS. Dari hasil penyelarasan dengan spesifikasi GSIM, diperoleh enam puluh tiga obyek informasi metadata statistik. Physical data model yang dihasilkan telah memenuhi kriteria umum pemodelan data serta mencakup elemen metadata untuk memenuhi kebutuhan pengguna dan standar SDI, sehingga dapat mendukung pembangunan MMS.

Statistics Indonesia (BPS) is a non-ministerial government agency authorized to conduct offical statistics. BPSs main business process statistics, known as the SBFA (Statistical Business Framework and Architecture) and refer to an international statistical business process standard called GSBPM (Generic Statistical Business Process Model), has eight phases (specify need, design, build, collect, process, analyze, disseminate, and evaluate). Metadata is needed in each of these phases to improve efficiency and effectiveness in statistical business processes. With the signing of Presidential Regulation No. 39 of 2019 concerning SDI (Satu Data Indonesia) and the increasing of BPS statistical products number, BPS needs to hurry to improve the management of its metadata. The BPS initiative to build a Metadata Management System (MMS) faces obstacles because the statistical activities metadata is currently incomplete and not in accordance with the GSIM (Generic Statistical Information Model) standard. The main objective of this study is to provide a statistical metadata data model recommendation that is in line with BPSs statistical business processes and metadata standards set to support MMS development. The determination of metadata information objects is carried out by aligning information objects obtained from BPS business processes to GSIM specifications. The information object is then modeled up to the level of the physical data model, so that it is easily implemented into MMS. From the results of alignment with the GSIM specifications, sixty-three statistical metadata information objects were obtained. The physical data model has met the general criteria for data modeling and includes metadata elements to meet user needs and SDI standards, so that it can support MMS development"
Depok: Fakultas Ilmu Komputer Universitas Indonesia, 2020
TA-Pdf
UI - Tugas Akhir  Universitas Indonesia Library
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Conway, Malcolm
"You need hard data to make your next move, whether youre carrying out a training needs assessment or planning to launch a new product, but do you know how to get that data? Moreover, do you know that the data you collect fits your needs and is valid? This Infoline enables you to collect data using electronic tools such as email and the Web. It explains several data collection methods, including Web surveys, electronic interviewing, and traditional secondary research using online resources. The seven-step research model presented also gets you data that is appropriate, useful, and validated, enabling you to save time and effort by searching with the right method and in the right place the first time."
Alexandria, VA: [American Society for Training and Development Press;, ], 2004
e20438682
eBooks  Universitas Indonesia Library
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Agostino Di Ciaccio, editor
"The book includes 45 papers from a selection of the 156 papers accepted for presentation and discussed at the conference on “Advanced statistical methods for the analysis of large data-sets.”"
Berlin: [, Springer-Verlag ], 2012
e20418930
eBooks  Universitas Indonesia Library
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"This volume contains a selection of chapters based on papers to be presented at the Fifth Statistical Challenges in Modern Astronomy Symposium. The symposium will be held June 13-15th at Penn State University. Modern astronomical research faces a vast range of statistical issues which have spawned a revival in methodological activity among astronomers. The Statistical Challenges in Modern Astronomy V conference will bring astronomers and statisticians together to discuss methodological issues of common interest. Time series analysis, image analysis, Bayesian methods, Poisson processes, nonlinear regression, maximum likelihood, multivariate classification, and wavelet and multiscale analyses are all important themes to be covered in detail. Many problems will be introduced at the conference in the context of large-scale astronomical projects including LIGO, AXAF, XTE, Hipparcos, and digitized sky surveys.;"
New York: [Springer Science, ], 2013
e20419566
eBooks  Universitas Indonesia Library
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