This study aimed to determine the use of CEP-based inquiry learning that influence the increase in SPS and students’ interest in entrepreneurship in the concept of acid-base. The research method used was pre-experimental design with research design with one group pre-test post-test design. The study population was students of class XI MAN Banda Aceh which consisted of three schools. Determination of the sample was carried out by purposive sampling technique, the measurement carried out using entrepreneur interest questionnaires. Whereas class determination was done by total sampling technique. The results showed that the initial analysis of the student’s SPS was 33.88 after the CEP-based inquiry learning was applied must be there an increase of 88.71 with 84.04 N-gain acquisition which included high criteria. This proves that CEP-based inquiry learning is effective in increasing student’s SPS in the concept of acid-base. The interest of entrepreneur students gained an average of 76.37.

Flooding is the most common natural disaster in Indonesia. Damage caused by floods is a global problem in order to reduce the loss of life and losses economically. Mapping flood-prone areas is one of the solutions to flood disaster mitigation. The method of mapping flood-prone areas generally uses manual mapping so that requires large resources such as mapping, cost and inefficient data updating. In addition, mapping using conventional Geographic Information System is used to automate the mapping with fast and efficient results. Therefore, this study aims to create a geoprocessing model as a new tool in ArcGIS to automatically map flood-prone areas and evaluate the performance of these automation methods. Automation is done by assembling each stage of mapping flood-prone areas conventionally into a tool that runs every stage on one process. Through the application of the geoprocessing model to automate mapping of flood-prone areas, a better performance improvement is obtained than conventional maps of flood-prone areas. Mapping flood-prone areas automatically requires an average of 2 minutes 32 seconds. This automation tool has also been tested using the System Usability Scale method, the results obtained with an average value of 81.66 are included in category B (Excellent).