Bandingkan metode

	Pemodelan Topik yang Dapat Dijelaskan	Pemodelan Topik
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2003–2020s	1999–2003
Pencetus	Community practice (Blei et al. seminal; explainability extensions 2010s–present)	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tipe	Unsupervised topic discovery + interpretability layer	Unsupervised generative probabilistic 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	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Terkait	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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.