Bandingkan metode

	GRU yang Dapat Dijelaskan	LSTM yang Dapat Dijelaskan
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2014 (GRU); 2016–2017 (XAI integration)	2017–2019
Pencetus	Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis
Tipe	Recurrent neural network with post-hoc or attention-based interpretability	Interpretable deep learning (post-hoc explainability)
Sumber perintis	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Alias	XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM
Terkait	5	5
Ringkasan	Explainable GRU pairs the Gated Recurrent Unit, a compact and efficient recurrent architecture, with explainability techniques such as SHAP, LIME, or attention weighting to reveal which time steps and features drove each prediction. It brings interpretability to sequential modelling without sacrificing the GRU's ability to capture temporal dependencies.	Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparency demanded by high-stakes domains such as clinical decision support, fraud detection, and regulatory compliance.