Hasil Pencarian

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Hasil Pencarian

Ditemukan 81622 dokumen yang sesuai dengan query
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Iwan Ariawan
Depok: Jur. Biost. & Kependudukan FKM UI, 1996
001.422 5 IWA a
Buku Teks SO  Universitas Indonesia Library
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Silverman, David
London: Sage, 1993
001.422 5 SIL i
Buku Teks SO  Universitas Indonesia Library
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Hengky Latan
Alfabeta : Bandung , 2014
004.77 LAT a
Buku Teks SO  Universitas Indonesia Library
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Miles, Matthew B.
"This Third Edition of Miles &​ Huberman's classic qualitative text has been updated and streamlined by Johnny Saldaña. This new edition presents the fundamentals of research design and data management, followed by five distinct methods of analysis: exploring, describing, ordering, explaining, and predicting. Comprehensive and authoritative, this classic title has been elegantly revised for a new generation of qualitative researchers"
California: SAGE Publications, 2014
001.42 MIL q
Buku Teks SO  Universitas Indonesia Library
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Kohler, Ulrich
Texas: Stata Press, 2009
004 Koh d
Buku Teks  Universitas Indonesia Library
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Rela Sabtiana
"Badan Pusat Statistik Kabupaten Kaur merupakan satuan kerja di bawah Badan Pusat Statistik Republik Indonesia yang bertanggung jawab melaksanakan kegiatan statistik di wilayah Kabupaten Kaur Provinsi Bengkulu. Meskipun Badan Pusat Statistik Kabupaten merupakan satuan kerja terkecil di bawah Badan Pusat Statistik Republik Indonesia, namun Badan Pusat Statistik Kabupaten menyumbang peran besar dalam pencapaian tujuan Badan Pusat Statistik untuk meningkatkan kualitas data. Hal ini disebabkan oleh peran Badan Pusat Statistik Kabupaten sebagai tombak dalam pengumpulan data langsung ke responden dan sekaligus sebagai pengolah dan diseminasi data. Sebagai contoh adalah pelaksanaan Survei Sosial Ekonomi Nasional yang tengah berlangsung pada semester I tahun 2019 saat penyusunan penelitian ini. Dari survei ini diperoleh permasalahan yaitu terdapat ketidaklengkapan, ketidakkonsistenan isian dan ketidaktepatan harga pada Modul Kor dan Konsumsi Pengeluaran saat entri data dalam aplikasi. Begitu pula saat pasca entri masih ditemukan ketidakkonsistensian dan ketidaktepatan isian. Untuk mengatasi permasalahan ini dilakukan evaluasi tingkat kematangan manajemen kualitas data menggunakan kerangka kerja Manajemen Kualitas Data Loshin. Hasil yang diperoleh menunjukkan bahwa tingkat kematangan berada pada kisaran 2 dan 3. Dari delapan dimensi, terdapat empat dimensi yang belum memenuhi target yang diharapkan yaitu harapan kualitas data, protokol kualitas data, standar data, dan teknologi. Selain itu, hasil dari pengukuran kualitas data statistik menggunakan kerangka kerja European Statistical System menunjukkan bahwa total skor yang dicapai adalah 5.7 dari target yang diharapkan sebesar 9.4. Dari hasil penelitian ini selanjutnya disusun rekomendasi peningkatan kualitas data.

The BPS-Statistics of Kaur Regency is a work unit under the BPS-Statistics of the Republic of Indonesia which is responsible for carrying out statistical activities in the regency area, precisely the Regency of Kaur, Bengkulu Province. Although the Regency Statistics Agency is the smallest work unit, the BPS-Statistics of Kaur Regency contributes a large role in achieving the goals of the BPS-Statistics of Republic of Indonesia to improve data quality. This is due to the role of the Regency Statistics Agency as a spearhead in collecting data directly to respondents and at the same time as data processors. An example is the implementation of the National Socio-Economic Survey which was taking place in the first semester of 2019 during the preparation of this study. From this survey, there are problems, namely there are incompleteness, inconsistency in the contents and inaccuracy of the price range in the Cor Module and Expenditure Consumption during data entry in the application. Likewise, inconsistencies and inaccuracies are found after post entries. To overcome this problem, an evaluation of the maturity level of data quality management using the Loshin’s Data Quality Management was done. The results indicate that the maturity level is in the range of 2 and 3. Of the eight dimensions, there are four dimensions that have not met the expected targets, namely expectations of data quality, data quality protocols, data standards, and technology. In addition, the results of measuring the quality of statistical data using the European Statistical System indicate that the total score achieved is 5.7 of the expected target of 9.4. From the results of this study, recommendations were made for improving data quality."
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Jakarta: Fakultas Ilmu Komputer Universitas Indonesia, 2019
TA-pdf
UI - Tugas Akhir  Universitas Indonesia Library
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Putri Marlina
"Bagan kendali berdasarkan median dan median absolute deviation merupakan modifikasi dari bagan kendali Shewhart, ketika ada sedikit penyimpangan asumsi kenormalan pada sampel. Modifikasi ini dilakukan dengan menggunakan penaksir-penaksir robust, median sampel dan median absolute deviation sampel, dalam pembuatan batas-batas kendali pada bagan kendali yang akan digunakan. Median sampel digunakan sebagai penaksir untuk mean proses, dan median absolute deviation sampel digunakan sebagai penaksir untuk standar deviasi proses.
Bagan kendali berdasarkan median dan median absolute deviation ini terdiri dari dua bagan, yaitu bagan kendali untuk mengawasi mean proses dan bagan kendali untuk mengawasi standar deviasi proses. Sebagai contoh penerapan, studi ini menggunakan data sampel berdistribusi normal standar. Dari contoh penerapan tersebut diperoleh hasil bahwa bagan kendali berdasarkan median dan median absolute deviation dapat digunakan sebagai alternatif dari bagan kendali Shewhart, ketika sampel yang digunakan telah terkontaminasi outlier yang bukan merupakan kesalahan atau dengan kata lain terdapat penyimpangan asumsi kenormalan pada sampel.

Control chart based on the median and median absolute deviation is a modification of the Shewhart control chart, when there are slight deviations assuming normality in the sample. This modification is done by using robust estimators, the sample median and median absolute deviation of samples, in the constructing of the control limits on control charts to be used. Sample median is used as an estimator for the process mean and sample median absolute deviation is used as an estimator for the standard deviation of the process.
Control chart based on median and median absolute deviation consists of two charts, the control chart to monitor the process mean and the control chart to monitor standard deviation of the process. As an example of the application, this study uses the standard normal distribution of sample data. It showed that the control chart based on the median and median absolute deviation can be used as an alternative to the Shewhart control chart, when the sample used was contaminated with outliers that are not a mistake or in other words, there are deviations assuming normality in the sample.
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Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2012
S42903
UI - Skripsi Open  Universitas Indonesia Library
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Woodward, M. (Mark)
"Highly praised for its broad, practical coverage, the second edition of this popular text incorporated the major statistical models and issues relevant to epidemiological studies. Epidemiology: Study Design and Data Analysis, Third Edition continues to focus on the quantitative aspects of epidemiological research. Updated and expanded, this edition shows students how statistical principles and techniques can help solve epidemiological problems. New to the Third EditionNew chapter on risk scores and clinical decision rules New chapter on computer-intensive methods, including the bootstrap, permutation tests, and missing value imputationNew sections on binomial regression models, competing risk, information criteria, propensity scoring, and splinesMany more exercises and examples using both Stata and SASMore than 60 new figures After introducing study design and reviewing all the standard methods, this self-contained book takes students through analytical methods for both general and specific epidemiological study designs, including cohort, case-control, and intervention studies. In addition to classical methods, it now covers modern methods that exploit the enormous power of contemporary computers. The book also addresses the problem of determining the appropriate size for a study, discusses statistical modeling in epidemiology, covers methods for comparing and summarizing the evidence from several studies, and explains how to use statistical models in risk forecasting and assessing new biomarkers. The author illustrates the techniques with numerous real-world examples and interprets results in a practical way. He also includes an extensive list of references for further reading along with exercises to reinforce understanding. Web ResourceA wealth of supporting material can be downloaded from the book's CRC Press web page, including:Real-life data sets used in the textSAS and Stata programs used for examples in the textSAS and Stata programs for special techniques coveredSample size spreadsheet "--
"Preface This book is about the quantitative aspects of epidemiological research. I have written it with two audiences in mind: the researcher who wishes to understand how statistical principles and techniques may be used to solve epidemiological problems and the applied statistician who wishes to find out how to apply her or his subject in this field. A practical approach is used; although a complete set of formulae are included where hand calculation is viable, mathematical proofs are omitted and statistical nicety has largely been avoided. The techniques described are illustrated by example, and results of the applications of the techniques are interpreted in a practical way. Sometimes hypothetical datasets have been constructed to produce clear examples of epidemiological concepts and methodology. However, the majority of the data used in examples, and exercises, are taken from real epidemiological investigations, drawn from past publications or my own collaborative research. Several substantial datasets are either listed within the book or, more often, made available on book's web site for the reader to explore using her or his own computer software. SAS and Stata programs for most of the examples, where appropriate, are also provided on this web site. Finally, an extensive list of references is included for further reading. I have assumed that the reader has some basic knowledge of statistics, such as might be obtained from a medical degree course, or a first-year course in statistics as part of a science degree. Even so, this book is self-contained in that all the standard methods necessary to the rest of the book are reviewed in Chapter 2. From this base, the text goes through analytical methods for general and specific epidemiological study designs"
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Boca Raton: Taylor and Francis, 2014
614.407 2 WOO e
Buku Teks SO  Universitas Indonesia Library
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Chielsin Ko
"Sekretariat Dewan Pertimbangan Presiden memerlukan pengolahan dan pengelolaan data yang berkualitas untuk menunjang peran memberikan dukungan teknis dan administrasi kepada Dewan Pertimbangan Presiden. Permasalahan yang dihadapi organisasi adalah belum adanya kebijakan dan manajemen data serta sistem-sistem silo yang belum terintegrasi, mengakibatkan duplikasi, inkonsistensi, dan kesalahan data. Untuk menyelesaikan permasalahan tersebut, penelitian ini mengukur tingkat kematangan manajemen data master yang ada di organisasi menggunakan metode MD3M Spruit-Pietzka. Hasil pengukuran tingkat kematangan kemudian dianalisa untuk merumuskan strategi peningkatan tingkat kematangan manajemen master data di Sekretariat Dewan Pertimbangan Presiden. Hasil pengukuran menunjukkan bahwa tingkat kematangan MDM di Sekretariat Dewan Pertimbangan Presiden adalah 1. Hal ini menunjukkan sudah adanya kesadaran dan upaya awal untuk mengatur manajemen data master di dalam organisasi. Target tingkat kematangan MDM adalah 3, dengan topik kualitas data dan perlindungan data sebagai prioritas perbaikan dari organisasi. Penelitian ini juga menghasilkan strategi untuk meningkatkan tingkat kematangan MDM melalui analisis kesenjangan antara tingkat kematangan dengan target tingkat kematangan. Program pengembangan data master direncanakan berjalan secara bertahap selama dua tahun.

The Secretariat of the Presidential Advisory Council requires quality data processing and management to support the role of providing technical and administrative support to the Presidential Advisory Council. The problems faced by organization are non-existent of policies and management related to data as well as unintegrated silo systems, resulting in duplication, inconsistency, and data errors. To solve this problem, this study measures the current master data management maturity level in the organization using the MD3M Spruit-Pietzka method. The results of the measurement of the maturity level then are analyzed to formulate a strategy to improve the maturity level of master data management at the Secretariat of the Presidential Advisory Council. The result of the assessment showed that MDM maturity level at Secretariat of Presidential Advisory Council is 1. This means that organization already has basic awareness in the management of master data. Organization’s target MDM maturity level is 3, with data quality and data protection as improvement priorities. This research also produces a strategy to increase the maturity level of MDM through gap analysis between the maturity level and the target maturity level. The master data development program is planned to run in stages over two years."
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Depok: Fakultas Ilmu Komputer Universitas Indonesia, 2021
TA-pdf
UI - Tugas Akhir  Universitas Indonesia Library
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Cryer, Jonathan
Boston: PWS-Kent, 1991
519.5 CRY s
Buku Teks SO  Universitas Indonesia Library
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