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Ekstraksi Fitur Produktivitas Dinamis Berdasarkan Topik Ilmiah Untuk Klasterisasi Peneliti
Get PDFPengelompokkan peneliti seringkali menggunakan informasi tekstual yang terdapat pada artikel ilmiah peneliti, contohnya judul, abstrak, dan kata kunci sehingga menghasilkan kelompok peneliti dengan kemiripan informasi tekstual pada artikel ilmiah mereka. Pengelompokkan peneliti juga seringkali menggunakan jumlah publikasi dan sitasi sehingga menghasilkan kelompok peneliti yang memiliki jumlah publikasi dan sitasi yang cenderung sama. Berdasarkaan kedua metode di atas, penelitian ini mencoba untuk menganalisis penggunaan topik artikel ilmiah pada proses ekstraksi fitur produktivitas. Fitur ini merupakan fitur yang didapatkan melalui penghitungan kinerja peneliti berdasarkan jumlah publikasi dan sitasi. Hasil ekstraksi fitur akan digunakan untuk klasterisasi peneliti menggunakan metode K-Means++. Sebelum data peneliti diklasterisasi, terlebih dahulu data peneliti dianalisis untuk menghilangkan kemungkinan adanya outlier. Evaluasi hasil klaster dilakukan dengan mempertimbangkan nilai Sum Squared Error dan Silhouette. Hasilnya, klaster optimal didapatkan dengan nilai K sama dengan 8 dan nilai silhouette sama dengan 0.15396. Kemudian, hasil klaster dianalisis untuk dapat memberikan label terhadap masing-masing klaster dengan mempertimbangkan topik artikel ilmiah, jumlah publikasi dan jumlah sitasi.
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Researcher clustering often uses textual information contained in scientific articles, for example titles, abstracts, and keywords, resulting in groups of researchers with similar textual information from their scientific articles. Researchers clustering also often uses the number of publications and citations, resulting in groups of researchers who tend to have the same number of publications and citations. Based on the two methods above, this study attempts to analyze the use of scientific article topics in the productivity feature extraction process. This feature is a feature obtained through calculating the performance of researchers based on the number of publications and citations. The results of feature extraction will be used for clustering researchers using the K-Means++ method. Before it is clustered, the researcher data must be analyzed first to eliminate the possibility of outliers. Evaluation of cluster results is carried out by considering the Sum Squared Error and Silhouette values. As a result, the optimal cluster is obtained with a K value equal to 8 and a silhouette value equal to 0.15396. Then, the results of the clusters are analyzed to be able to label each cluster by considering the topic of scientific articles, number of publications and number of citations
Implementasi Klasifikasi Opini Review Film Menggunakan Support Vector Machine Dengan Particle Swarm Optimization
Get PDFPertumbuhan pesat jumlah pengguna internet dan jaringan
sosial telah membuka jalan untuk mengakses dan menganalisis
opini para penggunanya. Analisis maksud opini apakah termasuk
positif atau negatif merupakan hal yang mudah bagi manusia.
Namun, analisis semua opini secara manual menjadi hal yang
mustahil karena jumlah opini yang meningkat secara besarbesaran.
Oleh karena itu, penggalian opini diperlukan untuk
menganalisis opini secara otomatis dan dapat mengambil
informasi yang berguna.
Pada tugas akhir ini, sistem yang diimplementasikan berupa
sistem yang mampu melakukan klasifikasi opini review film. Sistem
menganalisis apakah opini termasuk opini posistif atau negatif.
Tahap pertama adalah ekstraksi fitur dengan pemrosesan teks.
Tahap ini memroses dokumen menjadi fitur, dimulai dari case
folding, tokenization, stopwords removal, dan pembobotan TFIDF.
Tahap kedua adalah klasifikasi menggunakan metode SVM.
Namun, untuk meminimalkan jumlah fitur yang sangat banyak
dilakukan penambahan proses seleksi fitur dengan metode PSO.
Tahap ketiga adalah evaluasi. Kinerja sistem dievaluasi dengan
menggunakan metode confusion matrix.
Uji coba pada tugas akhir ini menggunakan data opini review
film berjumlah 2000 dokumen. Untuk metode validasi, sistem
menggunakan metode k-fold cross validation. Hasil uji coba
menyimpulkan bahwa sistem dapat melakukan klasifikasi opini
review film cukup baik. Nilai akurasi klasifikasi SVM dengan nilai k pada cross validation sama dengan 5 sebesar 50.40% meningkat
menjadi 60.85% setelah ditambahkan seleksi fitur menggunakan
metode PSO. Penggunaan sentimen untuk pemilihan fitur juga
sangat berpengaruh
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The rapid growth of Internet users and social networking has
opened the way to access and analyze the opinions of its users.
Analysis of whether the opinion is positif or negatif is an easy thing
for humans. However, the analysis of all manually opinion
becomes impossible because of the number of opinions increased
massively. Therefore, opinion mining is required for automatically
analyze the opinion and can retrieve useful information
In this thesis, the system is implemented in the form of a system
that is able to classify the opinions of movie reviews. System
analyzes whether the opinion includes positive or negative
opinion. The first stage is the extraction of features with text
processing. This phase process the document into a feature,
starting from folding case, tokenization, stopwords removal and
TF-IDF weighting. The second stage is classification using SVM
method. However, to minimize the number of features that are very
much, the addition of feature selection using PSO method is
needed. The third stage is evaluation. Performance of system was
evaluated using the confusion matrix.
The trial in this thesis using a 2000 opinion movie review
documents. For validation of methods, systems using the k-fold
cross validation. The trial results concluded that the system can
classify opinion of review films quite well. SVM classification
accuracy value with k for cross validation equal to 5 at 50.40%
increase to 60.85% after being added feature selection using the method of PSO. Use of sentiment for feature selection is also very
influential
Ekstraksi Fitur Produktivitas Dinamis berdasarkan Topik Artikel Ilmiah untuk Klasterisasi Peneliti
Get PDFPengelompokkan peneliti seringkali menggunakan informasi tekstual yang terdapat pada artikel ilmiah peneliti, contohnya judul, abstrak, dan kata kunci sehingga menghasilkan kelompok peneliti dengan kemiripan informasi tekstual pada artikel ilmiah mereka. Pengelompokkan peneliti juga seringkali menggunakan jumlah publikasi dan sitasi sehingga menghasilkan kelompok peneliti yang memiliki jumlah publikasi dan sitasi yang cenderung sama. Berdasarkaan kedua metode di atas, penelitian ini mencoba untuk menganalisis penggunaan topik artikel ilmiah pada proses ekstraksi fitur produktivitas. Fitur ini merupakan fitur yang didapatkan melalui penghitungan kinerja peneliti berdasarkan jumlah publikasi dan sitasi. Hasil ekstraksi fitur akan digunakan untuk klasterisasi peneliti menggunakan metode K-Means++. Sebelum data peneliti diklasterisasi, terlebih dahulu data peneliti dianalisis untuk menghilangkan kemungkinan adanya outlier. Evaluasi hasil klaster dilakukan dengan mempertimbangkan nilai Sum Squared Error dan Silhouette. Hasilnya, klaster optimal didapatkan dengan nilai K sama dengan 8 dan nilai silhouette sama dengan 0.15396. Kemudian, hasil klaster dianalisis untuk dapat memberikan label terhadap masing-masing klaster dengan mempertimbangkan topik artikel ilmiah, jumlah publikasi dan jumlah sitasi
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
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
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