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All Journal FALASIFA : Jurnal Studi Keislaman Multicience Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran
M. CHOERUL UMAM
OFFICE ADMINISTRATION EDUCATION STUDIES PROGRAM, SEBELAS MARET UNIVERSITY
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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The Enterprise School Readiness Prediction System (ESRPS) Uses Machine Learning to Assess Children's Readiness for Entering Elementary School Muhammad Choerul Umam; Cicilia Dyah Sulistyaningrum I.; Dydik Kurniawan; Priyono Tri Febrianto
Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Vol 10, No 4 (2024): December
Publisher : Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v10i4.13488

Abstract

This study aims to develop and evaluate the Enterprise School Readiness Prediction System (ESRPS) to predict children's readiness for elementary school using machine learning algorithms.  This research employs the Research and Development (R&D) method using Borg and Gall’s model and Instruments include questionnaires, programming tools, performance evaluation metrics, and web/database development tools to ensure the system's validity, reliability, and practical applicability.The research analyzes data from 300 students in various Indonesian cities, focusing on attributes like age, gender, and parental education. The system implements four algorithms: Decision Tree, Random Forest, Naive Bayes, and SVM. Data preprocessing, model training, and hyperparameter tuning were conducted, followed by evaluation using metrics like accuracy and precision. A web-based application was developed for user interaction and deployment. The result showed that the Decision Tree and Naive Bayes algorithms achieved the highest accuracy at 55%, followed by SVM at 50%, and Random Forest at 45%. This suggests that simpler models may be more suitable for the dataset's characteristics. The system also demonstrated the feasibility of practical deployment for educational use. The study concludes that ESRPS effectively uses machine learning to assess school readiness, highlighting the value of data preprocessing and model tuning in enhancing accuracy. Despite moderate accuracy levels, the study confirms the system's potential for aiding educators and parents in supporting children's transition to school.
Co-Authors Anton Subarno C. DYAH SULISTYANINGRUM INDRAWATI C. Dyah Sulistyaningrum Indrawati CHAIRUL HUDA ATMA DIRGATAMA Chairul Huda Atma Dirgatama Cicilia Dyah Sulistyaningrum Cicilia Dyah Sulistyaningrum I. Cicilia Dyah Sulistyaningrum Indrawati Dyah Sulistyaningrum Indrawati Ikhlas Rahmat Zaki Ita Nugrahini Kurniawan, Dydik Mia Wahyu Permatasari Mochamad Nursalim natasya irawan PATNI NINGHARDJANTI Patni Ninghardjanti Patni Ninghardjanti Priyono Tri Febrianto Siti Masitoh WINARNO WINARNO Winarno Winarno Winarno Winarno