<![CDATA[Monitoring the comfort of classrooms within a certain timeframe for the room is one that is quite important to do. Increased humidity, temperature, sound, the light will affect the concentration of teaching and learning. In designing this system, the author intends to design a convenience monitoring system based on the internet of things (IoT) so that its scope is wider. This study aims to group data into clusters using the Data Mining method, the K-means Clustering algorithm. Data is grouped based on this similarity data so that data with the same characteristics will be in one cluster. The attributes used are humidity, temperature, sound, and light. The results of K-Means Clustering obtained were 3 groups, cluster center with cluster 1 = 47.76; 26.07; 61; 92; 3602 clusters 2 = 58; 29; 59.5; 502 and cluster 3 = 60; 30.25; 58,75; 769,75. The cluster with the highest value is cluster three. Iteration of data grouping occurs 4 times iteration. Sistem yang diterapkan dalam sistem ini adalah sistem pemantauan kenyamanan kelas. Sistem pengambilan mengambil dan menghitung data fisik untuk membentuk kelembaban, suhu, suara, cahaya melalui sensor menjadi informasi di ruangan yang dipantau menggunakan mikrokontroler Arduino Mega2560 dan sensor DHT11, LDR, Kondensor yang terhubung ke hal-hal yang berbicara. Algoritma K-means dapat melakukan pengelompokan yang baik pada sistem yang sedang dibangun.]]>
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