Assigning reviewers to paper submissions is a knowledge-intensive and time-consuming work, especially since committee members have to sufficiently understand the paper and have a broad knowledge of reviewers' expertise to be able to match a submission to multiple reviewers. In this paper, we apply the author-topic modelling to a corpus of scientific papers in the Indonesian language, and reviewers as represented by the papers they author. We explore the use of stemming and POS-tagging to address some of the issues arising from the morphologically agglutinative nature of Bahasa Indonesia. We also use bigrams to capture multiword terms, as these are often found in Bahasa Indonesia as capturing semantically atomic concepts. We found that stemming does improve the performance of the author-topic model in the reviewer assignment task, while POS-tagging might not. Our results show that upon inspecting papers and the reviewers suggested by the model, there indeed exists a sound and reasonable relationship between these, revealing prospective reviewers which might not previously been suspected of being a good match.