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All Journal Media Statistika Journal of Mathematical and Fundamental Sciences Indonesian Journal of Statistics and Its Applications Nusantara Science and Technology Proceedings Annals of Mathematical Modeling
Devvi Sarwinda
Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Universitas Indonesia
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Journal : Media Statistika

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MULTIPLE IMPUTATION FOR ORDINARY COUNT DATA BY NORMAL DISTRIBUTION APPROXIMATION Titin Siswantining; Muhammad Ihsan; Saskya Mary Soemartojo; Devvi Sarwinda; Herley Shaori Al-Ash; Ika Marta Sari
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.68-78

Abstract

Missing values are a problem that is often encountered in various fields and must be addressed to obtain good statistical inference such as parameter estimation. Missing values can be found in any type of data, included count data that has Poisson distributed. One solution to overcome that problem is applying multiple imputation techniques. The multiple imputation technique for the case of count data consists of three main stages, namely the imputation, the analysis, and pooling parameter. The use of the normal distribution refers to the sampling distribution using the central limit theorem for discrete distributions. This study is also equipped with numerical simulations which aim to compare accuracy based on the resulting bias value. Based on the study, the solutions proposed to overcome the missing values in the count data yield satisfactory results. This is indicated by the size of the bias parameter estimate is small. But the bias value tends to increase with increasing percentage of observation of missing values and when the parameter values are small.
SPRATAMA MODEL FOR INDONESIAN PARAPHRASE DETECTION USING BIDIRECTIONAL LONG SHORT-TERM MEMORY AND BIDIRECTIONAL GATED RECURRENT UNIT Titin Siswantining; Stanley Pratama; Devvi Sarwinda
MEDIA STATISTIKA Vol 15, No 2 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.2.129-138

Abstract

Paraphrasing is a way to write sentences with other words with the same intent or purpose. Automatic paraphrase detection can be done using Natural Language Sentence Matching (NLSM) which is part of Natural Language Processing (NLP). NLP is a computational technique for processing text in general, while NLSM is used specifically to find the relationship between two sentences. With the development Neural Network (NN), nowadays NLP can be done more easily by computers. Many models for detecting and paraphrasing in English have been developed compared to Indonesian, which has less training data. This study proposes SPratama Model, which models paraphrase detection for Indonesian using a Recurrent Neural Network (RNN), namely Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The data used is "Quora Question Pairs" taken from Kaggle and translated into Indonesian using Google Translate. The results of this study indicate that the proposed model has an accuracy of around 80% for the detection of paraphrased sentences.
Co-Authors Amnia Salma Anak Agung Ketut Sudharmawan Bevina D Handari Bustamam, Alhadi Cindy Cynthia Gatot Fatwanto Hertono Herley Al-Ash Herley Shaori Al-Ash Ika Marta Sari Ika Mattasari Kathan Gerry Vivaldi Muhammad Ihsan NadyaAsanul Husna Nyoman A. Wibawa Saskya Mary Soemartojo Saskya Mary Soemartojo Selly Anastassia Amellia Kharis Stanley Pratama Titin Siswantining Titin Siswantining Titin Siswantining Valentino Vito