Today, relational databases are widely used in information systems. SQL (structured query language) is taught extensively in universities and professional schools across the globe as a programming language for its data management and accesses. Previously, we have studied a web-based programming learning assistant system (PLAS) to help novice students learn popular programming languages by themselves through solving various types of exercises. For SQL programming, we have implemented the grammar-concept understanding problem (GUP) and the comment insertion problem (CIP) for its initial studies. In this paper, we propose an SQL Query Description Problem (SDP) as a new exercise type for describing the SQL query to a specified request in a MySQL database system. To reduce teachers’ preparation workloads, we integrate a generative AI-assisted SQL query generator to automatically generate a new SDP instance with a given dataset. An SDP instance consists of a table, a set of questions and corresponding queries. Answer correctness is determined by enhanced string matching against an answer module that includes multiple semantically equivalent canonical queries. For evaluation, we generated 11 SDP instances on basic topics using the generator, where we found that Gemini 3.0 Pro exhibited higher pedagogical consistency compared to ChatGPT-5.0, achieving perfect scores in Sensibleness, Topicality, and Readiness metrics. Then, we assigned the generated instances to 32 undergraduate students at the Indonesian Institute of Business and Technology (INSTIKI). The results showed an average correct answer rate of 95.2% and a mean SUS score of 78, which demonstrates strong initial student performance and system acceptance.

The OVO application is an application that provides payment system services that can provide convenience in non-cash transactions, besides that the OVO application also opens access to digital financial products and services. OVO application which is a payment system used by various users. The diversity of these users will be an important point in assessing customer satisfaction because each customer has a different level of satisfaction. Users of the OVO application will assess customer satisfaction satisfied or dissatisfied, so that the existing system, will require further development in the future. One of the methods used in measuring the level of customer satisfaction is the pieces framework method which method has variables of performance assessment, information, economy, control, efficiency, and service. Researchers use questionnaires on 103 respondents who use the OVO application, INSTIKI students class of 2019, dissemination of this questionnaire through the Google form platform. The research results on the level of customer satisfaction showed the results of PUAS, with an average value above 4.06.

Nowadays, dried Moringa leaves (M. oleifera) are increasingly in demand due to their health benefits. High-quality ones have shown remarkable positive effects as antioxidants, antidiabetics, and anti-inflammatory agents. However, in the industry, the quality classification process into six categories is performed manually by farmers, which is time-consuming and error-prone. Particularly, the two highest categories of Class A and Class B are hard to distinguish, since they are visually similar. In this paper, to automate the classification process, we introduce a new high-resolution dataset, extract color and texture features using the Gray-Level Co-occurrence Matrix (GLCM) method, and present a two-stage classification method using the Light Gradient Boosting Machine (LightGBM) algorithm with them. The experimental results show that the proposal improved classification accuracy from 82% by the baseline algorithm to 90% while maintaining high processing efficiency, demonstrating its potential for real-time and scalable industrial applications in dried Moringa leaves quality grading.