1,720,954 research outputs found
Augmentasi Teks Berbasis Prefiks dan Ensemble Learning untuk Mengatasi Keterbatasan Data dalam Analisis Sentimen
No full textAnalisis sentiment dalam Bahasa Indonesia sering menghadapi tantangan keterbatasan data, yang menyebabkan risiko data menjadi bias, overfitting, atau underfitting model machine learning. Untuk mengatasi masalah tersebut, penelitian ini mengusulkan pendekatan berbasis ensemble learning yang mengintegrasikan Logistic Regression (LR), Naïve Bayes (NB), dan Support Vector Machine (SVM) dengan menggunakan teknik hard voting. Selain itu, augmentasi teks berbasis prefiks “me-“, “ter-“, “di-“, dan “ber-“ diterapkan untuk meningkatkan variasi dan kuantitas data yang dirancang untuk meningkatkan kemampuan generalisasi dan akurasi dalam analisis sentimen untuk data berbahasa Indonesia yang terbatas. Penelitian ini bertujuan untuk meningkatkan akurasi model ensemble learning untuk sentimen analisis. Augmentasi teks berbasis prefiks dilakukan untuk meningkatkan jumlah dataset dengan cara menambahkan imbuhan untuk kata kerja yang telah diidentifikasi melalui POS Tagging. Cara ini dapat menghasilkan dataset yang lebih variatif tanpa mengubah makna kalimat asli. Ensemble learning yang digunakan berbasis stacking untuk mengkombinasikan kekuatan masing-masing model kemudian menggunakan metode hard voting dalam pembobotan dengan tujuan hasil prediksi akhir ditentukan berdasarkan mayoritas suara. Hasil pengujian menunjukkan bahwa augmentasi teks berbasis prefiks dan model ensemble learning mampu meningkatkan akurasi model sebesar 91,29%. Dibandingkan dengan model yang diuji tanpa data augmentasi. Selain itu, metode ensemble learning terbukti lebih unggul dibandingkan model individual LR, NB, dan SVM.
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Sentiment analysis in Indonesian often faces the challenge of limited data, which causes the risk of data bias, overfitting, or underfitting machine learning models. To overcome this problem, this study proposes an ensemble learning-based approach that integrates Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machine (SVM) using the hard voting technique. In addition, text augmentation based on the prefixes "me-", "ter-", "di-", and "ber-" is applied to increase the variety and quantity of data designed to improve generalization and accuracy in sentiment analysis for limited Indonesian language data.
This study aims to improve the accuracy of the ensemble learning model for sentiment analysis. Prefix-based text augmentation is carried out to increase the number of datasets by adding affixes to verbs that have been identified through POS Tagging. This method can produce a more varied dataset without changing the meaning of the original sentence. The ensemble learning used is stacking-based to combine the strengths of each model and then uses the hard voting method in weighting with the aim that the final prediction results are determined based on the majority of votes. The test results show that prefix-based text augmentation and ensemble learning models are able to increase model accuracy by 91.29%. Compared to the model tested without augmentation data. In addition, the ensemble learning method is proven to be better than the individual LR, NB, and SVM models
Rancang Bangun Alat Security Portal Jalan Perumahan Berbasis Mikrokontroler
Full text linkHasil dari pengujian ini menunjukkan bahwa keseluruhan fungsi-fungsi yang diharapkan sesuai dengan kenginan dan berjalan dengan semestinya. Portal jalan perumahan berbasis mikrokontroler ini terdapat fitur-fitur yang bisa membantu meringankan dan memudahkan pekerjaan satpam dan juga meningkatkan keamanan dengan penggunaan sensor-sensor yang digunakan
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
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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