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The objective of this research is to design a recommendation engine for Pilah Matur App. The recommendation engine can provide recommendations to a user about the list of recyclable waste that may be needed to a user according to interaction data with Pilah Matur App. Pilah Matur App is an Android-based recyclable waste mobile app that accepts the recyclable material data from recyclable waste donors or volunteers. The recyclable material may be needed by other users who act as the waste taker. Wastes taken by the waste taker can reduce the amount of waste disposed to the landfill. The recommendation engine in this research uses a combination of CF (Collaborative Filtering) and LBS (Location Based Service). Collaborative filtering performs data filtering based on the similarity of user characteristics so that it is able to provide new information to other users because CF provides information based on a pattern of one user group that has a similarity. The preference for recycled material uses the nearest neighbor similarity method based on GPS coordinates where the recycled material is uploaded by the recyclable waste donor or volunteer then compared to the location of the recyclable waste taker. The output recommendation engine is a list of recycled wastes that have the highest similarity to user preferences. The recommendations proposed by collaborative filtering methods can be measured for accuracy using Mean Absolute Error (MAE). The results of the MAE calculation in the user-based CF method, the App Maturity dataset has an MAE value of 0.33. While the item-based CF method gets an MAE value of 0.17 using the same dataset. The results of testing the CF method show that in user-based collaborative filtering the prediction errors are more than in item-based collaborative filtering. The results of the CF method recommendations are then sorted based on the closest distance by the LBS method.