[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123878-en":3,"doc-seo-123878-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123878,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Development of an Android-based Machine Learning Student Problem Identification Tool Application at YPT Banjarmasin VHS - study findings","The era of the 5.0 Industrial Revolution calls for automation and digitalization across education, including faster support for Guidance and Counseling teachers who must analyze counseling instrument items manually for large student cohorts. This research develops an Android-based Student Problem Identification Tool application using Research and Development (R&D) with Multinomial Logistic Regression, mirroring counselors’ analytical capabilities from prior instrument data. Results show 100% accuracy versus manual counselor analysis and application-based analysis for 30 students, with average performance of 85.00% and feasibility of 96.30% (“Highly Feasible”).","JPPIPA9(11) (2023)  \nJurnalPenelitian Pendidikan IPA Journal of Research in Science Education  \n[http://jppipa.unram.ac.id/index.php/jppipa/index](http://jppipa.unram.ac.id/index.php/jppipa/index)  \nDevelopment of an Android-based Machine Learning Student Problem Identification Tool Application at YPT Banjarmasin VHS  \nMuhammad Arisandy Rizky1*, Handaru Jati1  \n1Department of Electronics Technology and Informatics Education, Yogyakarta State University, Yogyakarta, Indonesia.  \nReceived: June 22, 2023  \nRevised: October 17, 2023  \nAccepted: November 25, 2023  \nPublished: November 30, 2023  \nCorresponding Author: Muhammad Arisandy Rizky  \n[muhammadarisandy.2020@student.uny.ac.id](muhammadarisandy.2020@student.uny.ac.id)  \nDOI: 10.29303/jppipa.v9i11.4420  \n© 2023 The Authors. This open access article is distributed under a (CC-BY License)  \nIntroduction  \nAbstract: 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.  \nKeywords: Android-based; Machine Learning; Multinomial Logistic Regression; Student Problem Identification Tool  \nGuidance and Counseling Teachers play a vital role in monitoring and supporting students' personal  \nVocational High Schools (VHS) are secondary education institutions that aim to prepare students for employment in specific fields. Irwanto (2021) suggests that VHS should focus on integrating classroom learning with real-world work situations in companies or industries. Yayasan Pendidikan Teknologi (YPT) VHS is a private vocational high school located in Banjarmasin. Similar to other schools, YPT Banjarmasin VHS enrolls students from diverse backgrounds who may face various challenges. It is crucial for Guidance and Counseling Teachers to address these student problems promptly, ensuring that they do not hinder the teaching and learning process for students.  \ndevelopment through a range of service programs and accountable counseling procedures. According to Azahari et al. (2022), counseling involves providing students with guidance, information, and advice to help them reach their goals. For Guidance and Counseling Teachers to provide effective counseling services, they need to have a thorough understanding of their students problems. Rosiani et al. (2022) explainedcounseling guidance for new students can be provided through orientation services to help students adapt to and understand their new environment, thereby preventing problems that may hinder their learning and harm them.  \n___________  \nHow to Cite:  \nRizky, M.A., & Jati, H. (2023) . Development of an Android-based Machine Learning Student Problem Identification Tool Application at YPT Banjarm","cbCaioCyf6d10Uj5","https://ap.wps.com/l/cbCaioCyf6d10Uj5","pdf",385869,1,"English","en",105,"# Introduction\n# Development Method (R&D)\n## Machine Learning Approach: Multinomial Logistic Regression\n# Problem Identification Tool in Guidance and Counseling\n## Instrument Description and Use at YPT Banjarmasin VHS\n# Results and Discussion\n## Accuracy and Performance Test\n## Feasibility Assessment\n# Conclusion","[{\"question\":\"What problem does the research target in student counseling support?\",\"answer\":\"Guidance and Counseling teachers need assistance to analyze Student Problem Identification Tool instruments quickly and accurately when handling hundreds of students.\"},{\"question\":\"How is the Android-based application developed and what model is used?\",\"answer\":\"The application is built through Research and Development (R\\u0026D) using Multinomial Logistic Regression to replicate counselors’ analysis based on prior instrument data.\"},{\"question\":\"What performance and feasibility results does the application achieve?\",\"answer\":\"The application reports 100% accuracy compared with manual counselor analysis and application-based analysis for 30 students, with average performance of 85.00% and feasibility of 96.30% (“Highly Feasible”).\"}]","Development of an Android-based Machine Learning Student Problem Identification Tool Application at YPT Banjarmasin VHS - 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