Development of an Android-based Machine Learning Student Problem Identification Tool Application at YPT Banjarmasin VHS

Abstract

The era of the 5.0 Industrial Revolution demands that we develop automation and digitalization technologies in various aspects of life, including education. Even Guidance and Counseling teachers who manually analyze counseling instrument items need assistance in swiftly and accurately analyzing instruments for hundreds of students. This research aims to support counselors in analyzing the Student Problem Identification Tool Instrument, which consists of 225 items, through student’s Android devices, thereby enabling the prompt resolution of student issues. Through the stages of Research and Development (R&D), the Student Problem Identification Tool Application is developed using the Multinomial Logistic Regression method within Machine Learning. This is achieved by replicating the capabilities of counselors based on analysis data from various previous instances of the Student Problem Identification Tool Instrument. Research outcomes reveal that the application achieves an accuracy rate of 100% when compared to manual analysis by counselors and application-based analysis for 30 students. The average performance test result is 85.00%, and the feasibility test result is 96.30%, categorizing it as "Highly Feasible." In conclusion, Machine Learning facilitates the effective and efficient analysis of extensive data when supported by quality training data and the appropriate method selection for problem-solving