We found that (1) in general, accuracies on the analogy tasks were low, suggesting that improvements for Indonesian word embeddings are still possible and KaWAT is hard enough to be the benchmark dataset for that purpose, (2) on syntactic analogies, embedding by (<cite>Bojanowski et al., 2017</cite>) performed best and yielded 20% fewer training epochs when employed for POS tagging, and (3) on semantic analogies, GloVe embedding trained on Tempo corpus performed best and produced significant gains on ROUGE-1 and ROUGE-L scores when used for text summarization.