1,720,959 research outputs found
Pemodelan Jumlah Kasus Kusta di Kabupaten Mojokerto dan Kabupaten Jombang Tahun 2019 Menggunakan Regresi Zero-Inflated Poisson Inverse Gaussian
Full text linkKusta atau lepra adalah penyakit yang menyerang berbagai bagian tubuh diantaranya saraf dan kulit yang disebabkan oleh infeksi bakteri Mycobacterium leprae. Jawa Timur merupakan provinsi dengan jumlah penderita kusta tertinggi di Indonesia hingga tahun 2019 sebanyak 3.306 kasus. Stigma negatif masyarakat terhadap penderita kusta menyebabkan munculnya perkampungan kusta di dusun Sumberglagah, Kabupaten Mojokerto. Selain adanya kampung kusta, pada Kabupaten Mojokerto juga terdapat rumah sakit kusta terbesar di Jawa Timur yang menjadi pusat pengobatan kusta baik di Kabupaten Mojokerto maupun di daerah sekitarnya seperti Kabupaten Jombang. Penelitian ini menggunakan data jumlah kasus kusta di Kabupaten Mojokerto dan Kabupaten Jombang tahun 2019 sebagai variabel respon dan enam variabel lainnya sebagai variabel prediktor. Data jumlah kasus kusta tersebut memiliki proporsi nilai nol sebesar 30,77%, lalu nilai mean sebesar 2,179 serta varians sebesar 6,625. Hal ini mengindikasikan bahwa adanya extra zeros serta terdapat pelanggaran asumsi equidispersi. Regresi Zero Inflated-Poisson Inverse Gaussian (ZIPIG) merupakan metode pengembangan regresi yang mampu menangani overdispersi serta extra zeros pada variabel respon data observasi. Faktor yang berpengaruh signifikan terhadap jumlah kasus kusta berdasarkan hasil pemodelan regresi ZIPIG yaitu persentase pelayanan kesehatan untuk penduduk usia lanjut (X6).
=====================================================================================================
Leprosy is a disease that attacks various parts of the body, including the nerves and skin, which is caused by infection of Mycobacterium leprae bacteria. East Java is the province with the highest number of leprosy sufferers in Indonesia until 2019 with 3,306 cases. Society’s negative stigma towards people with leprosy has led to the emergence of a leprosy village in Sumberglagah village, Mojokerto Regency. In addition, there is the largest leprosy hospital in East Java which is located in Mojokerto Regency and becomes leprosy center treatment both in Mojokerto Regency and also in surrounding areas such as Jombang Regency. This study uses data of the number of leprosy cases in Mojokerto and Jombang districts in 2019 as response variables and six other variables as prediktor variables. Zero Inflated-Poisson Inverse Gaussian Regression (ZIPIG) is a regression development method that is able to handle overdispersion and extra zeros on the response variable of the observation data. The number of leprosy cases data has a proportion of zeros value namely 30,77%, then the mean value is 2,179 and the variance is 6.625. This indicates that there are extra zeros and there is a violation of the equidispersion assumption. The factor that significantly influences the number of leprosy cases based on the results of ZIPIG regression modeling is percentage of health services for the elderly population (X6)
Penaksiran Parameter dan Pengujian Hipotesis pada Model Regresi Bivariate Zero-Inflated Negative Binomial
No full textModel statistika yang dapat digunakan untuk mengatasi under/overdispersi dan excess zero secara bersamaan pada data cacahan diantaranya adalah Bivariate Zero-Inflated Negative Binomial Regression (BZINBR). Model BZINBR memiliki kelebihan yaitu tidak mensyaratkan nilai varians yang sama dengan nilai rata-rata pada variabel respon, serta terdapat parameter dispersi yang berguna untuk menggambarkan variasi dari data. Model BZINBR dapat diterapkan pada data cacahan yang terdiri atas dua variabel respon. Penelitian ini berfokus pada penaksiran parameter model BZINBR tipe II menggunakan metode Maximum Likelihood Estimation (MLE) dengan iterasi numerik Berndt–Hall–Hall–Hausman (BHHH) serta pengujian hipotesis secara serentak dan parsial dengan menggunakan Maximum Likelihood Ratio Test (MLRT). Model BZINBR selanjutnya dikembangkan dan diaplikasikan untuk studi kasus pada data jumlah kematian ibu hamil dan jumlah kematian ibu nifas di Karesidenan Pekalongan Provinsi Jawa Tengah tahun 2017. Berdasarkan data diketahui bahwa persentase nilai nol pada data kematian ibu hamil yaitu sebesar 74,73% dan pada data kematian ibu nifas yaitu sebesar 65,93%. Persentase nilai nol yang berlebih dapat mengindikasikan adanya extra zeros serta terdapat pelanggaran asumsi equidispersi yaitu under/overdispersi. Hasil penelitian menunjukkan bahwa pemodelan BZINBR terbaik yaitu pemodelan dengan melibatkan variabel exposure. Selanjutnya melalui pengaplikasian model BZINBR terbaik didapatkan hasil bahwa seluruh variabel prediktor berpengaruh signifikan terhadap jumlah kematian ibu hamil dan jumlah kematian ibu nifas, selain itu diketahui pula parameter dispersi pada model BZINBR tipe II mampu menangani adanya overdispersi pada data jumlah kematian ibu hamil dan jumlah kematian ibu nifas di Karesidenan Pekalongan Provinsi Jawa Tengah.
==================================================================================================================================
The statistics model that can be used to overcome under/overdispersion and excess zero simultaneously in count data is Bivariate Zero-Inflated Negative Binomial Regression (BZINBR). BZINBR model has the advantages that it does not require the same variance value as the average value of the response variables, and there is dispersion parameter to describe the variation of the data. BZINBR model can be applied to count data that consist of two response variables. This study focuses on the parameters estimation of the BZINBR type II model using the Maximum Likelihood Estimation (MLE) method with Berndt–Hall–Hall–Hausman (BHHH) numerical iterations and also simultaneous and partial hypothesis testing using the Maximum Likelihood Ratio Test (MLRT). The BZINBR model is then developed and applied to case studies on data about the number of pregnant women deaths and the number of postpartum maternal deaths in the Pekalongan Residency of Central Java Province in 2017. Based on the data,it is known that the percentage of zero values in the data about pregnant women deaths is 74.73% and in postpartum maternal deaths is 65.93%. Excess percentage of zeros can indicate extra zeros and there is a violation of the equidispersion assumption, namely under/overdispersion. The results showed that the best BZINBR modeling is the model with exposure variables. Furthermore, through the application of the best BZINBR model, it is found that all predictor variables has significant effects on the number of pregnant women deaths and the number of postpartum maternal deaths. In addition, it is also known that the dispersion parameter in the BZINBR type II model is able to handle overdispersion in data about the number of pregnant women deaths and the number of postpartum maternal deaths in the Pekalongan Residency, Central Java Province
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Comparison of Lexicon-Based Methods and Bidirectional Encoder Representations for Transformers Models in Sentiment Analysis of Government Debt Market Movements
Full text linkThe State Budget of Indonesia (APBN) is the main tool for implementing fiscal policies and serves as a budgeting guideline for development execution in Indonesia. One of the funding sources in budget financing is Debt Financing, which consists of Government Securities (SBN) issuance and Loans. Overall, SUN contributes IDR 5,824.34 trillion, highlighting its significant proportion in debt financing. Understanding public sentiment toward SUN is essential in developing effective government policies. This research conducts sentiment analysis on tweets from the social media X over the past 7.75 years to assess public perception and propose strategic recommendations. The aim of this research is to compare the BERT model and the Lexicon-Based method to determine which achieves the highest accuracy in sentiment analysis. The findings can help the government develop strategies for issuing SUN, especially in improving public involvement and investor trust. This research method is based on a deep learning pre-trained Bidirectional Encoder Representations from Transformers (BERT) model, specifically IndoBERT, with fine-tuning, and a Lexicon-Based approach utilizing the InSet lexicon. The results of this research are as follows: on the overall tweet dataset, the BERT model with optimal hyperparameters outperformed the Lexicon-Based method, achieving an accuracy of 70.28% compared to 55.77%. Similarly, on an annual basis, BERT exhibited higher accuracy than the Lexicon-Based method, except in 2021. Public sentiment on SUN in social media X is categorized as 49% positive, 30% neutral, and 21% negative. These findings indicate a generally favorable perception of SUN but also highlight areas for improvement in public communication. Overall, the BERT model demonstrates superior performance over the Lexicon-Based method. Considering the opportunities available, the government could leverage social media through Key Opinion Leaders and enhance transparency in explaining policies such as Tapera. This approach could maximize public participation in investing in SUN in Indonesia
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
- …
