Model Latent Dirichlet Allocation Pada Perilaku Siswa Menggunakan Media Pembelajaran Daring

Abstrak: Indonesia saat ini sedang dihebohkan dengan yang namanya sekolah Daring. Dimana yang seharusnya sekolah adalah tempat untuk guru dan siswa mengajarkan ilmu dari pendidikan hingga perilaku secara tatap muka dan sekarang karena keadaan yang tidak bisa dihindari maka harus dilakukannya pembelajaran secara online yaitu dengan alat perantara. Permasalahan diambil dari banyaknya siswa sudah mempunyai alat komunikasi yaitu handphone dan berbagai media sosial yang sudah dikuasai seperti Instagram. Dengan maksud untuk menganalisa siswa khususnya di Indonesia, sikap apa yang diambil ketika siswa menggunakan Instagram ketika sedang berlangsungnya pembelajaran secara daring. Didapatkan hasil ketika melakukan Teknik crawling data untuk mendapatkan teks atau caption dari penggunaan hashtag sekolah daring yaitu 120 post dalam keadaan sudah terseleksi dari yang bukan post dari siswa. Bentuk analisa untuk pengolahan data yang sudah didapat menggunakan model Latent Dirichlet Allocation (LDA) yaitu untuk menemukan topik yang mendominasi dari hashtag yang digunakan dengan penambahan fitur Stopword untuk kata yang tidak diperlukan. Hasil akhir dari analisa tersebut terdapat 4 topik yang dominan dan dimayoritasi oleh siswa yang mendapatkan penugasan dari sekolah seperti pelajaran biologi.   Kata kunci: Instagram, Latent Dirichlet Allocation (LDA), Pembelajaran Daring,   Abstract: Indonesia is currently being shocked by the named school Online. Where the school should be a place for teachers and students to teach knowledge from education to face-to-face behavior and now because of circumstances that cannot be avoided,learning must be carried out online, namely with an intermediary tool. The problem is taken from the number of students who already have communication tools, namely mobile phones and various social media that have been mastered such as Instagram. With a view to analyzing students, especially in Indonesia, what attitudes are taken when students use Instagram when learning is taking place online. Obtained results when performing techniques crawling data to get text or captions from the use hashtags, of online school namely 120 posts in a selected state from non- posts student. The form of analysis for processing the data that has been obtained uses the model, Latent Dirichlet Allocation (LDA) which is to find the dominant topic of the hashtags used by adding the feature Stopword for unnecessary words. The final result of the analysis, there are 4 topics that are dominant and are majored by students who get assignments from schools such as biology lessons.   Keywords: E-Learning, Instagram, Latent Dirichlet Allocation (LDA)

Deep Belief Nets for Topic Modeling

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Inferring Concept Prerequisite Relations from Online Educational Resources

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Sparse Stochastic Inference for Latent Dirichlet allocation

We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a corpus of 1.2 million books (33 billion words) with thousands of topics. Our approach reduces the bias of variational inference and generalizes to many Bayesian hidden-variable models.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012