GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Operator Dependent Choice of Prostate Cancer Biopsy Has Limited Impact on a Gene Signature Analysis for the Highly Expressed Genes IGFBP3 and F3 in Prostate Cancer Epithelial Cells
    Public Library of Science (PLoS) ; 2014
    In:  PLoS ONE Vol. 9, No. 10 ( 2014-10-8), p. e109610-
    Details
    In: PLoS ONE, Public Library of Science (PLoS), Vol. 9, No. 10 ( 2014-10-8), p. e109610-
    Type of Medium: Online Resource
    ISSN: 1932-6203
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.1371/journal.pone.0109610
    DOI: 10.1371/journal.pone.0109610.g001
    DOI: 10.1371/journal.pone.0109610.g002
    DOI: 10.1371/journal.pone.0109610.t001
    DOI: 10.1371/journal.pone.0109610.s001
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2014
    detail.hit.zdb_id: 2267670-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    A comparative assessment on gene expression classification methods of RNA-seq data generated using next-generation sequencing (NGS)
    Narra T ; 2022
    In:  Narra J Vol. 2, No. 1 ( 2022-04-01)
    Details
    In: Narra J, Narra T, Vol. 2, No. 1 ( 2022-04-01)
    Abstract: Next-generation sequencing or massively parallel sequencing have revolutionized genomic research. RNA sequencing (RNA-Seq) can profile the gene-expression used for molecular diagnosis, disease classification and providing potential markers of diseases. For classification of gene expressions, several methods that have been proposed are based on microarray data which is a continuous scale or require a normal distribution assumption. As the RNA-Seq data do not meet those requirements, these methods cannot be applied directly. In this study, we compare several classifiers including Logistic Regression, Support Vector Machine, Classification and Regression Trees and Random Forest. A simulation study with different parameters such as over dispersion, differential expression rate is conducted and the results are compared with two mRNA experimental datasets. To measure predictive accuracy six performance indicators are used: Percentage Correctly Classified, Area Under Receiver Operating Characteristic (ROC) Curve, Kolmogorov Smirnov Statistics, Partial Gini Index, H-measure and Brier Score. The result shows that Random Forest outperforms the other classification algorithms.
    Type of Medium: Online Resource
    ISSN: 2807-2618
    URL: Article
    DOI: 10.52225/narra.v2i1.60
    Language: Unknown
    Publisher: Narra T
    Publication Date: 2022
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Willingness-to-pay for a hypothetical Ebola vaccine in Indonesia: A cross-sectional study in Aceh
    F1000 Research Ltd ; 2023
    In:  F1000Research Vol. 8 ( 2023-6-14), p. 1441-
    Details
    In: F1000Research, F1000 Research Ltd, Vol. 8 ( 2023-6-14), p. 1441-
    Type of Medium: Online Resource
    ISSN: 2046-1402
    URL: Journal
    URL: Article
    DOI: 10.12688/f1000research
    DOI: 10.12688/f1000research.20144.3
    Language: English
    Publisher: F1000 Research Ltd
    Publication Date: 2023
    detail.hit.zdb_id: 2699932-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    Predictions of Indonesia Economic Phenomena Based on Online News Using Random Forest
    Politeknik Ganesha ; 2022
    In:  SinkrOn Vol. 7, No. 2 ( 2022-04-27), p. 532-540
    Details
    In: SinkrOn, Politeknik Ganesha, Vol. 7, No. 2 ( 2022-04-27), p. 532-540
    Abstract: Economic growth in the first quarter of 2021 based on YoY (Year on Year) is around -0.74%. This figure caused the Indonesian economy to recession after contracting four times since the second quarter of 2020. With positive and negative growth in the value of GDP for each category based on the business sector each quarter, can do future economic growth modelling. The prediction results can be used as an early warning for the government on factors that can maximize and factors that must improve. This study aims to predict the state of economic growth in the next quarter using  Random Forest classification. Random Forest combines tree classification and bagging by resampling the data, which reduces the variance of the final model, which is for low variance overfitting. The data used in this study was scrapped from January 2021 to March 2021 on 5 Indonesian online news portals, namely Kompas, Antara, Okezone, Detik, and Bisnis. The independent variable is online news based on GDP category. The dependent variable results from data labelling on each news, up or down, carried out by the Directorate of Balance Sheet of BPS. Based on the calculations with cross-validation of 10, the modelling results obtained 96.51% accuracy, 97% precision, and 97% recall. The random forest method is good for predicting economic growth in the next quarter, namely the second quarter of 2021. Incorrectly predicted only three categories of GDP were: the construction category, the transportation and warehousing category, and the company service category
    Type of Medium: Online Resource
    ISSN: 2541-2019 , 2541-044X
    URL: Issue
    URL: Article
    DOI: 10.33395/sinkron.v7i2
    DOI: 10.33395/sinkron.v7i2.11401
    Language: Unknown
    Publisher: Politeknik Ganesha
    Publication Date: 2022
    detail.hit.zdb_id: 3068693-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Klasifikasi Tutupan Lahan Berdasarkan Random Forest Algorithm Menggunakan Cloud Computing Platform
    Politeknik Statistika STIS ; 2022
    In:  Jurnal Aplikasi Statistika & Komputasi Statistik Vol. 14, No. 1 ( 2022-03-13), p. 1-12
    Details
    In: Jurnal Aplikasi Statistika & Komputasi Statistik, Politeknik Statistika STIS, Vol. 14, No. 1 ( 2022-03-13), p. 1-12
    Abstract: Statistik pertanian merupakan salah satu data yang vital di dunia dan memiliki kontribusi besar terhadap pencapaian tujuan program Sustainable Development Goals (SDGs). Dalam SDGs, perhatian terhadap ketahanan pangan difokuskan pada indikator kunci kedua yaitu nol kelaparan (SDG 2). Ketersediaan data tutupan lahan yang akurat diperlukan sebagai data dasar untuk luasan baku sawah yang akan digunakan untuk mengukur tingkat ketahanan pangan. Pemetaan tanaman membutuhkan pemrosesan dan pengelolaan data citra satelit dengan volume yang sangat besar dan tidak terstruktur yang mengarah pada permasalahan Geo Big Data dan menuntut teknologi dan sumber daya baru yang mampu menangani citra satelit dalam jumlah besar. Secara khusus, munculnya sumber daya cloud computing, seperti Google Earth Engine telah mengatasi masalah Geo Big Data ini. Kami menggunakan algoritma Random Forest (RF) pada platform Google Earth Engine (GEE) di Kota Jakarta Utara pada tahun 2019 untuk mengklasifikasikan tutupan lahan. Hasil penelitian menunjukkan bahwa overall accuracy (OA)
    Type of Medium: Online Resource
    ISSN: 2615-1367 , 2086-4132
    URL: Issue
    URL: Article
    DOI: 10.34123/jurnalasks.v14i1
    DOI: 10.34123/jurnalasks.v14i1.383
    Language: Unknown
    Publisher: Politeknik Statistika STIS
    Publication Date: 2022
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    Sentiment Analysis on Overseas Tweets on the Impact of COVID-19 in Indonesia
    Institut Pertanian Bogor ; 2021
    In:  Indonesian Journal of Statistics and Its Applications Vol. 5, No. 2 ( 2021-06-30), p. 304-313
    Details
    In: Indonesian Journal of Statistics and Its Applications, Institut Pertanian Bogor, Vol. 5, No. 2 ( 2021-06-30), p. 304-313
    Abstract: This study aims to conduct analysis to determine the trend of sentiment on tweets about Covid-19 in Indonesia from the Twitter accounts overseas on big data perspective. The data was obtained from Twitter in the period of April 2020, with the word query "Indonesian Corona Virus" from foreign user accounts in English. The process of retrieving data comes from Twitter tweets by crawling the text using Twitter's API (Application Programming Interface) by employing Python programming language. Twitter was chosen because it is very fast and easy to spread through status updates from and among the user accounts. The number of tweets obtained was 8,740 in text format, with a total engagement of 217,316. The data was sorted from the tweets with the largest to smallest engagement, then cleaned from unnecessary fonts and symbols as well as typo words and abbreviations. The sentiment classification was carried out by analytical tools, extracting information with text mining, into positive, negative, and neutral polarity. To sharpen the analysis, the cleaned data was selected only with the largest engagement until those with 100 engagements; then was grouped into 30 sub-topics to be analyzed. The interesting facts are found that most tweets and sub-topics were dominated by the negative sentiment; and some unthinkable sub-topics were talked by many users.
    Type of Medium: Online Resource
    ISSN: 2599-0802
    URL: Article
    DOI: 10.29244/ijsa.v5i2p304-313
    Language: Unknown
    Publisher: Institut Pertanian Bogor
    Publication Date: 2021
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 7
    Online Resource
    Online Resource
    Development of Automated Environmental Data Collection System and Environment Statistics Dashboard
    Institut Pertanian Bogor ; 2021
    In:  Indonesian Journal of Statistics and Its Applications Vol. 5, No. 2 ( 2021-06-30), p. 314-325
    Details
    In: Indonesian Journal of Statistics and Its Applications, Institut Pertanian Bogor, Vol. 5, No. 2 ( 2021-06-30), p. 314-325
    Abstract: Environmental data such as pollutants, temperature, and humidity are data that have a role in the agricultural sector in predicting rainfall conditions. In fact, pollutant data is common to be used as a proxy to see the density of industry and transportation. With this need, it is necessary to have automated data from outside websites that are able to provide data faster than satellite confirmation. Data sourced from IQair, can be used as a benchmark or confirmative data for weather and environmental statistics in Indonesia. Data is taken by scraping method on the website. Scraping is done on the API available on the website. Scraping is divided into 2 stages, the first is to determine the location in Indonesia, the second is to collect statistics such as temperature, humidity, and pollutant data (AQI). The module used in python is the scrapy module, where the crawling is effective starting from May 2020. The data is recorded every three hours for all regions of Indonesia and directly displayed by the Power BI-based dashboard. We also illustrated that AQI data can be used as a proxy for socio-economic activity and also as an indicator in monitoring green growth in Indonesia.
    Type of Medium: Online Resource
    ISSN: 2599-0802
    URL: Article
    DOI: 10.29244/ijsa.v5i2p314-325
    Language: Unknown
    Publisher: Institut Pertanian Bogor
    Publication Date: 2021
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 8
    Online Resource
    Online Resource
    INCORPORATING COMPLEX SURVEY DESIGN FOR ANALYSING THE DETERMINANT OF WOMEN IN REPRODUCTIVE AGE PARTICIPATION IN FAMILY PLANNING PROGRAM IN INDONESIA
    Universitas Pattimura ; 2023
    In:  BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol. 17, No. 3 ( 2023-09-30), p. 1401-1410
    Details
    In: BAREKENG: Jurnal Ilmu Matematika dan Terapan, Universitas Pattimura, Vol. 17, No. 3 ( 2023-09-30), p. 1401-1410
    Abstract: Data generated from complex survey are often treated as un-weighted simple random samples by analyst. This is unfortunate because everyone has different probability to be selected as sample in each stage of the complex survey design. Fail taking it into account will have serious impact in parameter and variance estimation. This paper aims to examining relationship between participation in family planning program and socio demographic status of women in reproductive age in Indonesia used data from latest Indonesian’s Demographic and Health Survey (IDHS). IDHS employs a multi stage stratified sampling design, thus there are a number of weights included in public-use IDHS datasets to account for this complex sample design. We found that the complex design features of the IHDS increased the variance estimates of the estimated parameters in the logistic regression models by about 1.325 – 1.88 times, compared to a simple random sampling. Therefore, using variance estimated from un-weighted simple random samples would lead to wrong conclusion of the significance parameter suggested by the model. The result also found that all of socio demographics variables used as predictors are significant. Thus, women with moderate education, unemployment, exposed by media, living in rural community and wealthy, have spouse that have moderate education and have a job tend to participate in family planning program.
    Type of Medium: Online Resource
    ISSN: 2615-3017 , 1978-7227
    URL: Issue
    URL: Article
    DOI: 10.30598/barekengvol17iss3year2023
    DOI: 10.30598/barekengvol17iss3pp1401-1410
    Language: Unknown
    Publisher: Universitas Pattimura
    Publication Date: 2023
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 9
    Online Resource
    Online Resource
    PENINGKATAN LITERASI DATA MENUJU INDONESIA 4.0
    Institut Teknologi Indonesia ; 2020
    In:  Empowerment in the Community Vol. 1, No. 1 ( 2020-01-31), p. 37-
    Details
    In: Empowerment in the Community, Institut Teknologi Indonesia, Vol. 1, No. 1 ( 2020-01-31), p. 37-
    Abstract: The Indonesian government has set 10 national priorities to face the Industrial Revolution 4.0 which is accompanied by an integrated roadmap known as Making Indonesia 4.0. Achieving these targets requires collaboration among the stakeholders (government institutions, associations and industry players, and academics).One of the key determinants of competitiveness in the Industry 4.0 era, is human capital including data literacy capability to analyze data generated from various technological devices becomes an information and policy, especially for the government.Politeknik Statistika STIS lecturers have held a series of activities to increase data literacy and big data technology for central and local governments, industry players, research institutions and universities. The activities were carried out in the form of a series of seminars, as well as workshops attended by decision makers of central and local governments, researchers and lecturers from various universities in Indonesia. As a result of this series of activities, the participants not only had an understanding of the importance of the data but were also able to use data to improve their evidence-based policies.Keywords: data literacy, evidence-based policy, big data, industry 4.0
    Type of Medium: Online Resource
    ISSN: 2580-3549
    URL: Issue
    URL: Article
    DOI: 10.31543/ecj.v1i1
    DOI: 10.31543/ecj.v1i1.369
    Language: Unknown
    Publisher: Institut Teknologi Indonesia
    Publication Date: 2020
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 10
    Online Resource
    Online Resource
    ANALISIS FAKTOR-FAKTOR YANG MEMENGARUHI KETAHANAN INTERVAL KELAHIRAN ANAK PERTAMA
    Politeknik Statistika STIS ; 2020
    In:  Seminar Nasional Official Statistics Vol. 2019, No. 1 ( 2020-05-13), p. 535-544
    Details
    In: Seminar Nasional Official Statistics, Politeknik Statistika STIS, Vol. 2019, No. 1 ( 2020-05-13), p. 535-544
    Abstract: Perencanaan keluarga merupakan hal yang penting dalam pencapaian Sustainable Development Goals. Tingginya angka kelahiran di negara berkembang biasanya tidak diimbangi dengan perencanaan keluarga yang baik sehingga mengakibatkan banyak masalah yang lebih serius. Tujuan ketiga SDGs yaitu good health and wealth being bahkan menyoroti secara serius masalah kematian ibu dan bayi. Tingkat fertilitas diukur dengan angka fertilitas total (TFR). Papua merupakan provinsi dengan TFR tertinggi kedua di Indonesia. Tingginya TFR merupakan implikasi dari umur melahirkan pertama yang terlalu muda. Untuk menurunkan TFR Provinsi Papua, perlu dilakukan kebijakan dan program khususnya yang berkaitan dengan interval kelahiran pertama. Tujuan penelitian ini adalah untuk mengetahui faktor-faktor yang memengaruhi interval kelahiran pertama di Provinsi Papua. Penelitian ini menggunakan data sekunder dari Survei Demografi dan Kesehatan Indonesia (SDKI) 2017 sebagai wujud pemanfaatan data official statistics untuk memantau pencapaian SDGs. Variabel yang dicakup meliputi faktor wanita dan faktor rumah tangga. Menggunakan pengujian log-rank, didapatkan bahwa terdapat perbedaan ketahanan yang signifikan antarkategori variabel tingkat pendidikan dan status kesejahteraan. Melalui permodelan semiparametrik menggunakan Cox-Proportional Hazard, variabel tingkat pendidikan, status kesejahteraan, dan status migrasi wanita berpengaruh signifikan terhadap interval kelahiran anak pertama. Wanita dengan pendidikan SMP ke atas, memiliki kesejahteraan tinggi, dan bukan migran memiliki risiko yang lebih tinggi untuk melahirkan anak pertama lebih cepat daripada wanita dalam kategori referensi masing-masing.
    Type of Medium: Online Resource
    ISSN: 2722-1970
    URL: Issue
    URL: Article
    DOI: 10.34123/semnasoffstat.v2019i1
    DOI: 10.34123/semnasoffstat.v2019i1.24
    Language: Unknown
    Publisher: Politeknik Statistika STIS
    Publication Date: 2020
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...