Abstract─ Kampung Bali is a public health Agencies engaged in public health services. Kampung Bali clinics still doing service to patients by manual search data such as patient data, patient pengelolahan, the recording of the examination of the patients still using the form, the management report data checks and drug data still using manual bookkeeping. Patient data storage media using the media of paper resulting in a data search is done by searching the archives can be time-consuming and still done manually by means of merekap patient data via the archives that can be time-consuming additionally vulnerable with errors. To support system development method of service Puskemas author using the method waterfall on system development, implementation of algorithms for data search system on Sequiential Searchin patients, and use the display Boostrap. Abstrak─ Puskesmas Kampung Bali merupakan suatu Instansi yang bergerak dibidang pelayanan kesehatan masyarakat. Puskesmas Kampung Bali masih melakukan pelayanan terhadap pasien dengan cara manual seperti pencarian data pasien, pengelolahan data pasien, pencatatan pemeriksaan pasien masih menggunakan formulir, pengelolaan laporan data pemeriksaan dan data obat yang masih menggunakan pembukuan manual. Media penyimpanan data pasien menggunakan media kertas sehingga mengakibatkan pencarian data dilakukan dengan cara menelusuri arsip-arsip yang dapat menyita waktu dan masih dikerjakan secara manual dengan cara merekap data-data pasien melaui arsip-arsip yang dapat menyita waktu selain itu rentan dengan kesalahan. Untuk mendukung metode pengembangan sistem pelayanan Puskemas penulis menggunakan metode waterfall pada pengembangan sistem,  implementasi Algoritma Sequiential Searching pada sistem pencarian data pasien, dan menggunakan tampilan Boostrap. Keyword: Sistem Pelayanan, Puskemas, Sequential, Boostrap

No abstract

The studies of human mobility prediction in mobile computing area gained due to the availability of large-scale dataset contained history of location trajectory. Previous work has been proposed many solutions for increasing of human mobility prediction result accuration, however, only few researchers have addressed the issue of<em> </em>human mobility for implementation of LSTM networks. This study attempted to use classical methodologies by combining LSTM and DBSCAN because those algorithms can tackle problem in human mobility, including large-scale sequential data modeling and number of clusters of arbitrary trajectory identification. The method of research consists of DBSCAN for clustering, long short-term memory (LSTM) algorithm for modelling and prediction, and Root Mean Square Error (RMSE) for evaluation. As the result,<em> </em>the prediction error or RMSE value reached score 3.551 by setting LSTM with parameter of <em>epoch</em> and <em>batch_size</em> is 100 and 20 respectively.

No abstract