Natural language processing (NLP) has significant impact on society via technologies such as machine translation and search engines. Despite its success, NLP technology is only widely available for high-resource languages such as English and Mandarin Chinese, and remains inaccessible to many languages due to the unavailability of data resources and benchmarks. In this work, we focus on developing resources for languages of Indonesia. Despite being the second most linguistically-diverse country, most languages in Indonesia are categorized as endangered and some are even extinct. We develop the first-ever parallel resource for 10 low-resource languages in Indonesia. Our resource includes sentiment and machine translation datasets, and bilingual lexicons. We provide extensive analysis, and describe challenges for creating such resources. Our hope is that this work will spark more NLP research on Indonesian and other underrepresented languages.