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OPTIMALISASI BIG DATA DALAM MENGURANGI MISS-TARGETING PROGRAM KELUARGA HARAPAN (PKH) DI KABUPATEN SIDOARJO DENGAN PENDEKATAN MACHINE LEARNING
Get PDFProgram Keluarga Harapan (PKH) menjadi episentrum dan center of excellence
penanggulangan kemiskinan yang mensinergikan berbagai program perlindungan
dan pemberdayaan sosial. Data kemiskinan yang akurat dan tepat sasaran harus
mampu diwujudkan dengan mengoptimalkan Big Data sebagai source. Namun,
dalam praktiknya, program ini seringkali mengalami miss-targeting. Pendekatan
Machine Learning digunakan karena berangkat dari permasalahan tersebut. Metode
yang digunakan adalah metode supervised learning. Tujuan penelitian ini untuk
memberikan gambaran perbandingan tingkat miss-targeting Program Keluarga
Harapan antara aplikasi SIKS-NG dan machine learning melalui aplikasi R
berdasarkan data dan pengukuran indikator kemiskinan yang sama. Diperoleh
model algoritma Averaged Neural Network dengan hasil yang optimal
dibandingkan algoritma-algoritma lain. Adapun hasil pengujian data yang
diperoleh pada SIKS-NG dan Machine Learning yang menggunakan evaluasi
confusion matrix dengan 3 indikator sebagai berikut: 1) Accuracy yang didapatkan
SIKS-NG 72,40% meningkat menjadi 81,18% pada Machine Learning; 2)
Precision pada SIKS-NG mendapatkan angka persentase tinggi 91,01%, akan tetapi
hasil tersebut mampu meningkat setelah data diberi Machine Learning menjadi
95,37%; 3) Recall dengan SIKS-NG memperoleh hasil 75,49%, sedangkan
Machine Learning memperoleh hasil yang lebih tinggi yakni 82,19%. Dengan
demikian, pendekatan Machine Learning dapat dijadikan rekomendasi alternatif
dalam pengambilan keputusan otomatis dan praktik manajemen inovatif.
Kata kunci: Program Keluarga Harapan, Miss-Targeting, Big Data, Machine
Learnin
Optimalizing Big Data in Reducing Miss-Targeting Family Hope Program (PKH) in Sidoarjo Disctrict with Approach Machine Learning
Get PDFMachine learning approaches have been used to solve various problems. PKH experienced miss-targeting. This study aims to compare the result of big data by SIKS-NG and machine learning based on the same data and measurement indicators. Obtained algorithms Averaged Neural Network with optimal output compared to others. As for data testing obtained on SIKS-NG and machine learning that uses elevated matrix evaluations with the following 3 indicators: 1) Accuracy obtained by SIKS-NG 72.40% increased to 81.18% for Machine Learning; 2) Precision at the center is getting a high percentage of 91,01%, but it is capable of increasing once the data is given Machine Learning to 95,37%; 3) Recall with the cycle was obtained at 75.49%, while Machine Learning obtained a higher yield of 82.19%. Thus, machine learning has been proven to reduce miss-targeting and can be used as an alternative recommendation in automatic decision making and innovative management practices in government circles
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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