Bandingkan kaedah

	Pemodelan Topik Boleh Dijelaskan	Model Topik LDA
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2003–2020s	2003
Pengasas	Community practice (Blei et al. seminal; explainability extensions 2010s–present)	Blei, D. M., Ng, A. Y., & Jordan, M. I.
Jenis	Unsupervised topic discovery + interpretability layer	Probabilistic generative topic model
Sumber perintis	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Alias	XTM, interpretable topic modeling, transparent topic modeling, explainable LDA	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
Berkaitan	6	5
Ringkasan	Explainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence scores, SHAP, attention weights) that make the learned topics transparent, auditable, and communicable to domain experts and stakeholders beyond the modeling team.	Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words.