Analysis of Health Research Topics in Indonesia Using the LDA (Latent Dirichlet Allocation) Topic Modeling Method

Sahria, Yoga; Fudholi, Dhomas Hatta

doi:10.29207/resti.v4i2.1821

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…To discover major research topics in the collected articles, LDA is applied, which is a topic model that has been widely adopted in many different domains and contexts [ 20 , 21 ]. The topic modeling analysis is conducted in the following three steps.…”

Section: Methodsmentioning

confidence: 99%

A bibliometric analysis of worldwide cancer research using machine learning methods

et al. 2023

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Section: Methodsmentioning

confidence: 99%

A bibliometric analysis of worldwide cancer research using machine learning methods

et al. 2023

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“…Classified data with two categories, namely positive and negative sentiment, will be modeled into topic modeling using the LDA method to conclude the topics hidden in the document. Stages in LDA [19] : a. The first step is to initialize parameters such as the number of documents, the number of words in a document, the number of topics, the number of iterations, and the LDA coefficient.…”

Section: Topic Modelingmentioning

confidence: 99%

Topic modeling and sentiment analysis about Mandalika on social media using the latent Dirichlet allocation method

Hammad

Hardita

Amrullah

2022

MATRIX

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The rapid and widespread dissemination of information currently affects the tourism sector. One tourist area that is quite widely discussed is the Mandalika Circuit. Twitter is one platform that provides comments related to the Mandalika Circuit. The amount of information related to the Mandalika Circuit is currently not being utilized properly by managers (government or private). It causes many topics related to the Mandalika Circuit that are currently trending, and public sentiment regarding the Mandalika Circuit is unknown to the government or private sector. Ignorance can result in delays in decision making which can harm the manager. To overcome this problem, research on sentiment analysis and topic modeling related to the Mandalika Circuit was carried out. The sentiment analysis method used is SVM and for modeling using LDA. Based on the results of the sentiment analysis, 1500 tweets were obtained before doing the pre-processing process, thus getting a dataset of 500 tweets divided into 398 positive and 102 negative tweets. So it can be concluded that more Twitter users give positive than negative responses to the Mandalika Circuit. The test results show that the SVM algorithm can classify sentiment toward the Mandalika Circuit well, as indicated by the measurement of the performance of the SVM algorithm, namely 87% accuracy, 77% precision, 84.81% recall, and 98.52% specificity. These results also show that the F1 Score compares the average precision and recall, which is weighted at 80.72%.

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“…Model LDA dapat diimplementasikan menggunakan bahasa pemrograman Phyton dengan menggunakan library genism. Hasil luaran dari package tersebut berupa grafik yang sesuai dengan topik bahasan [16].…”

Section: Metode Latent Dirichlect Allocationunclassified

Klasterisasi Keyword Terkait Pornografi pada Media Sosial Twitter Menggunakan Latent Dirichlet Allocation

Rakhmawati

Zuhroh

Muna

et al. 2022

JIEET

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Media sosial adalah saluran komunikasi bersifat online sebagai media yang digunakan untuk berbagi berbasis komunitas. Media sosialmemungkinkan seseorang terhubung satu dengan yang lain tanpa harus bertemu secara tatap muka. Twitter merupakan salah satu media sosial yang memberikan kebebasan bagi para penggunanya untuk membuat, mengunggah, dan membaca unggahan yang disebut tweet dengan jumlah pengguna di Indonesia mencapai 18,45 juta di tahun 2022. Twitter ternyata ramai digunakan sebagai media penyebarluasan konten asusila seperti pornografi. Pada penelitian ini, penulis mencari tahu beberapa kata kunci yang sering digunakan dalam penyebar luasan konten pornografi di Twitter dengan menggunakan metode Latent Dirichlet Allocation (LDA) untuk menemukan topik yang dominan dari kata kunci yang digunakan dan mengelompokkan kata kunci secara otomatis. Pada penerapan LDA, fitur stopword digunakan untuk mengeliminasi kata-kata yang tidak diperlukan. Cara menentukan jumlah topik yang optimal yaitu dengan melihat nilai perplexity dan topik coherence. Dari total data 15.135 unggahan yang didapatkan dari data crawling, selanjutnya dipetakan menjadi lima topik dan topik yang paling banyak digunakan menggunakan kata kunci sange, nonton, dan bokepindo.

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Analysis of Health Research Topics in Indonesia Using the LDA (Latent Dirichlet Allocation) Topic Modeling Method

Cited by 10 publications

References 7 publications

A bibliometric analysis of worldwide cancer research using machine learning methods

A bibliometric analysis of worldwide cancer research using machine learning methods

Topic modeling and sentiment analysis about Mandalika on social media using the latent Dirichlet allocation method

Klasterisasi Keyword Terkait Pornografi pada Media Sosial Twitter Menggunakan Latent Dirichlet Allocation

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