  In Indonesia, local languages play an integral role in the culture. However,
the available Indonesian language resources still fall into the category of
limited data in the Natural Language Processing (NLP) field. This is become
problematic when build NLP model for these languages. To address this gap, we
introduce Bhinneka Korpus, a multilingual parallel corpus featuring five
Indonesian local languages. Our goal is to enhance access and utilization of
these resources, extending their reach within the country. We explained in a
detail the dataset collection process and associated challenges. Additionally,
we experimented with translation task using the IBM Model 1 due to data
constraints. The result showed that the performance of each language already
shows good indications for further development. Challenges such as lexical
variation, smoothing effects, and cross-linguistic variability are discussed.
We intend to evaluate the corpus using advanced NLP techniques for low-resource
languages, paving the way for multilingual translation models.
