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Kurniawan, Dydik
Universitas Mulawarman
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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.
Educational Revolution : Digital Project-Based Rotation Learning (DPBRL) Model to Improve Students' Critical Thinking Skills Kurniawan, Dydik; Masitoh, Siti; Bachri, Bachtiar Sjaiful; Hidayat, Taufik; Wahyuningsih, Tri
Jurnal Kependidikan : Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran, dan Pembelajaran Vol. 10 No. 3 (2024): September
Publisher : LPPM Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v10i3.12634

Abstract

The research aimed to develop and assess the effectiveness of the Digital Project-Based Rotation Learning (DPBRL) model in enhancing students' critical thinking skills. The study employed a research and development method, using a non-equivalent control group design during the pilot phase, a research method used to describe and measure the characteristics of populations or phenomena systematically using numerical data, involving 83 undergraduate students from Mulawarman University Guidance and Counseling Study Program, divided into two heterogeneous classes. Validation of the DPBRL model was conducted by experts in learning model design, materials, and media, with data collected through validation sheets, interviews, document analysis, questionnaires, observation, and documentation. The data was analyzed using descriptive technique and Independent Samples t-test. The results demonstrated that the DPBRL model effectively improved students' critical thinking skills and digital literacy, particularly through the use of digital modules with Flipbook Maker. The model’s rotation structure and presentation stage provided a structured learning approach, highlighting the importance of feedback. The study concluded that integrating technology and collaboration in project-based learning through the DPBRL model significantly benefited students’ critical thinking, digital literacy, and teamwork, offering a comprehensive and effective learning experience.
The Enterprise School Readiness Prediction System (ESRPS) Uses Machine Learning to Assess Children's Readiness for Entering Elementary School Umam, Muhammad Choerul; Sulistyaningrum I., Cicilia Dyah; Kurniawan, Dydik; Febrianto, Priyono Tri
Jurnal Kependidikan : Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran, dan Pembelajaran Vol. 10 No. 4 (2024): December
Publisher : LPPM Universitas Pendidikan Mandalika

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 A. A. Bagus Surya Atmaja Susastra Afy Defisah Anggraeni, Yulia Anita Sari Arifin, Zaynal Aru, heti Atmaja, A. A. Bagus Surya Atmaja, A.A. Bagus Surya Bachri, Bachtiar Sjaiful Bambang Cahyono Cicilia Dyah Sulistyaningrum I. Desi Normala Sari Dicky Dicky Dra. Tri wahyuningsih, M.Si Dwiyono, H. Yudo Dwiyono, Yudo Fitriani Fitriani Irawan, Andi Wahyu Lorensius, Lorensius Masnurimma Heriansyah Masnurrima Heriansyah, Masnurrima Mauren, Irena Yolanita Mochamad Nursalim Muhammad Choerul Umam Normala Sari, Desi Pangadongan, Fara Virgianita Pangadongan, Fara Virgianita Priyono Tri Febrianto Ridho Kani Lestari Siti Masitoh Siti Masitoh Sulistyaningrum I., Cicilia Dyah Taufik Hidayat Tri Wahyuningsih Tri Wahyuningsih Tri Wahyuningsih Tri Wahyuningsih Umam, Muhammad Choerul Umar, Huzaifah Wahyuningsih , Tri Warman Warman Warman Warman Yasintha Sari Pratiwi Yeni Astuti Yulia Anggraeni Zaynal Arifin