The Implementation of Rough Set Algorithm to Classify Student Comfort Level Using Rosetta - Muhammad Rahmansyah Siregar; Jeni Sugiandi; Alpiki Pahriza; Salomo Marudut Pandapotan Sitorus (Yayasan Literasi Sains Indonesia, 2023)
Student comfort in the campus environment is an integral aspect in creating optimal learning conditions. Students who feel comfortable are more likely to be involved in academic and social activities. Several students were identified as frequently not attending class, and their interest in learning appeared to be lacking. This creates serious challenges in creating an optimal learning environment and meeting student needs. The research classifies student comfort levels and also provides a basis for developing more targeted campus policies. The data collection method uses a questionnaire method. The data processing method uses the Rough Set algorithm. Data processing uses Rosetta software. Based on the analysis carried out from 154 rules, the number of occurrences of the rest level attribute was 94 times, the class environment attribute was 110 times, the assignment difficulty level attribute was 114 times, the lecturer's teaching method attribute was 98 times, the campus facilities attribute was 136 times. So it can be seen that the campus facility attribute is the most influential because it has the highest number of occurrences. The next influential attribute after facilities is the level of difficulty of assignments, class environment, lecturer's teaching method and level of rest and reduce statistics show that campus facilities are a condition attribute that is very influential in student comfort levels, namely with an occurrence of 90,9%.