The existence of the Kereta Rel Listrik Commuter Line as the backbone of transportation in the Jakarta - Bogor - Depok - Tangerang - Bekasi - Banten area has a very important role for commuter mobility around Daerah Khusus Ibukota Jakarta. With an average number of 1.1 million passengers per day, Kereta Rel Listrik is one of the factors supporting Indonesia's economy and growth in various sectors. On the other hand, the Covid-19 pandemic that hit the world caused restrictions on human movement which resulted in a decline in all economic sectors. The purpose of this research is to obtain optimal train schedule recommendations for the operation of the Kereta Rel Listrik Commuter Line in the Rangkasbitung - Tanah Abang service to carry passengers optimally while adhering to the physical distancing protocol set by the Minister of Transportation to prevent the wider spread of Covid-19. With such a large amount of data that must be processed, Exploratory Data Analysis is one of the choices we take to process the above data to get satisfactory results.

This study aims to propose methods and models for extractive text summarization with contextual embedding. To build this model, a combination of traditional machine learning algorithms such as K-Means Clustering and the latest BERT-based architectures such as Sentence-BERT (SBERT) is carried out. The contextual embedding process will be carried out at the sentence level by SBERT. Embedded sentences will be clustered and the distance calculated from the centroid. The top sentences from each cluster will be used as summary candidates. The dataset used in this study is a collection of scientific journals from NeurIPS. Performance evaluation carried out with ROUGE-L gave a result of 15.52% and a BERTScore of 85.55%. This result surpasses several previous models such as PyTextRank and BERT Extractive Summarizer. The results of these measurements prove that the use of contextual embedding is very good if applied to extractive text summarization which is generally done at the sentence level.