The character of emotions in children is different from that of adults, where the characteristics of the emotions in childres include, (1) Briefly and ends suddenly, (2) Seems greater or stronger, (3) Temporary or superficial, (4) Frequent, (5) Can be known clearly from behaviour, and (6) Reaction reflects individuality. Emotions that are felt can be expressed through faces, this is continuous with how interested the child is tho the material presented in fornt of him. Measuring the level of interest in PAUD children in this study using CNN. In the process of training the level of interest in PAUD children, the accuracy value of the four models always increases from epoch 25 until 100 with the highest value being the Rajmehra architecture. But during the data testing process, the architecture in this study increased slightly and the highest peak reached an accuracy value of 81.66%. It is 3.33% better than the result obtained with the Rajmehra architecture.   Emosi adalah perasaan atau afeksi yang timbul, ketika seseorang berada dalam suatu keadaan yang dianggap penting oleh individu tersebut. Karakteristik emosi pada anak berbeda dengan karakteristik yang terjadi pada orang dewasa, dimana karekteristik emosi pada anak itu antara lain, (1) Berlangsung singkat dan berakhir tiba-tiba, (2) Terlihat lebih hebat atau kuat, (3) Bersifat sementara atau dangkal, (4) Lebih sering terjadi, (5) Dapat diketahui dengan jelas dari tingkah lakunya, dan (6) Reaksi mencerminkan individualitas. Emosi yang dirasakan dapat diekspresikan melalui wajah, hal ini berkesinambungan dengan seberapa tertariknya anak terhadap tayangan materi yang disajikan dihadapannya. Pengukuran tingkat ketertarikan anak PAUD pada penelitian ini menggunakan CNN. Dalam proses training tingkat ketertarikan anak PAUD, nilai akurasi keempat model selalu mengalami peningkatan mulai dari epoch 25 hingga 100 dengan nilai tertinggi adalah arsitektur Rajmehra. Tetapi saat proses testing data, arsitektur pada penelitian ini mengalami peningkatan secara perlahan dan puncak tertinggi mencapai nilai akurasi 81,66%. Hal tersebut jauh lebih baik 3,33% dibandingkan hasil yang diperoleh dengan arsitektur Rajmehra.

Abstract—The Covid-19 pandemic has transformed the offline education system into online. Therefore, in order to maximize the learning process, teachers were forced to adapt by having presentations that attract student's attention, including kindergarten teachers. This is a major problem considering the attention rate of children at early age is very diverse combined with their limited communication skill. Thus, there is a need to identify and classify student's learning interest through facial expressions and gestures during the online session. Through this research, student's learning interest were classified into several classes, validated by the teacher. There are three classes: Interested, Moderately Interested, and Not Interested. Trials to get the classification of student's learning interest by teacher validation, carried out by training and testing the cut area of the center of the face (eyes, mouth, face) to get facial expression recognition, supported by the gesture area as gesture recognition. This research has scenarios of four cut areas and two cut areas that were applied to the interest class that utilizes the weight of transfer learning architectures such as VGG16, ResNet50, and Xception. The results of the learning interest classification test obtained a minimum validation percentage of 70%. The result obtained through scenarios of three learning interest classes four cut areas using VGG16 was 75%, while for two cut areas using ResNet50 was 71%. These results proved that the methods of this research can be used to determine the duration and theme of online kindergarten classes.