Abstract
Patient Visits Outpatient and inpatient insurance at Class C Hospitals is increasing from year to year. Increased visits to insurance patients will have an impact on the inpatient and outpatient health services provided. From the increase in patient visits, the data owned by the hospital is increasingly abundant. The data can be used to explore knowledge, find certain patterns.
 To explore knowledge about Inpatient and Outpatient Insurance patients, data mining clustering techniques are used with the Self Organizing Map (SOM) algorithm using R Studio tools. Clustering technique with the implementation of the Self Organizing Map (SOM) algorithm is a technique for grouping data based on certain characteristics which are then mapped into areas that resemble map shapes. The CRISP-DM method is used in this study to perform the stages of the data mining process.
 The results obtained from the implementation of clustering with the Self Organizing Map (SOM) algorithm are obtained 2 clusters representing dense areas and non-congested areas. Dense areas are represented by Internal Medicine Clinic, Surgery Clinic, Eye Clinic, Hemodialysis, Melati Room, Orchid Room, Bougenville Room, Flamboyan Room. Non-crowded areas are represented by General Clinics, Dental Clinics, Obstetrics and Gynecology Clinics, Children's Clinics, Mawar Room and Soka Room
Highlights
LATAR BELAKANG Rumah sakit kelas C merupakan rumah sakit yang didalamnya terdapat pelayanan kedokteran subspesialis terbatas yang meliputi beberapa pelayanan diantaranya adalah pelayanan kesehatan anak, pelayanan bedah, pelayanan penyakit dalam, serta pelayanan penyakit kandungan dan kebidanan
inpatient insurance at Class C Hospitals is increasing from year to year
the data owned by the hospital is increasingly abundant
Summary
LATAR BELAKANG Rumah sakit kelas C merupakan rumah sakit yang didalamnya terdapat pelayanan kedokteran subspesialis terbatas yang meliputi beberapa pelayanan diantaranya adalah pelayanan kesehatan anak, pelayanan bedah, pelayanan penyakit dalam, serta pelayanan penyakit kandungan dan kebidanan. Salah satu keunggulan dari algoritma Self Organizing Map adalah mampu untuk memetakan data berdimensi tinggi ke dalam bentuk peta berdimensi rendah. Clustering dengan pendekatan Partitioning atau partisi merupakan pengelompokan data dari satu kelompok besar kemudian dibagi menjadi beberapa kelompok yang lebih kecil.
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