The number of cases of Covid-19 in this pandemic era is increasing and getting out of control every day. This triggers the Indonesian government to set policies on schools with online learning methods. Of course, online learning cannot ensure that it runs smoothly in all circles because several factors hinder the learning process. The difficulty of the internet network, limited quotas, unfamiliarity with the use of learning media, and an unsupportive environment for conducting online learning are the obstacles to ineffective online learning. The purpose of this study was to determine the level of satisfaction with online learning during the pandemic. This study uses quantitative research methods with a descriptive approach. Quantitative research methods will be processed into data mining using the K-Means Clustering Algorithm. The clustering process is carried out to get the results of clustering the level of student satisfaction. The dataset was obtained from the results of the questionnaire by submitting statements of satisfaction and dissatisfaction. The cluster type is based on high, medium, and low class. The test results obtained a value with the final iteration, namely the level of satisfied statements is categorized as high with a value of 11.79 compared to the dissatisfied statement, which is categorized as moderate with a value of 7.46. In contrast, for the low category level, there is no value of 0.00 cluster results state that the category is satisfied with online learning with a value of 9.33.

Insomnia is a form of sleep disorder. This study develops an expert system model that can help determine the tendency of insomnia based on the Diagnostic and Statistical Manual for Mental Disorders (DSM-V) guidelines. The forward chaining design method was used in this study because it is bottom-up by collecting facts from patients and concluded based on the DSM-V guidelines. The forward chaining method was chosen to test the hypothesis of the classification of insomnia tendencies. The factual information then acts as a knowledge base fed into computer programs that can generate system rules. In addition, tacit knowledge is used, as evidenced by O'Leary validation, to strengthen the validation of psychologist practitioners. There are three validation criteria: the accuracy of the knowledge base, completeness of the knowledge base, and condition-decision matches. The parameters used are based on complaints, dysfunction, time range, and other factors. The results of modeling and analysis of the rule system using the forward chaining method classify insomnia tendencies into three types based on time range: episodic, persistent, and recurring. The validation results carried out by practicing psychologists based on the analysis showed that the three rule systems were following the DSM-V guidelines and practical experience.