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All Journal Widyagogik : Jurnal Pendidikan dan Pembelajaran Sekolah Dasar Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Madura Hijri Masyarakat, Kebudayaan dan Politik Sosial Horizon: Jurnal Pendidikan Sosial Pendas : Jurnah Ilmiah Pendidikan Dasar JURNAL PENDIDIKAN TAMBUSAI Bada'a: Jurnal Ilmiah Pendidikan Dasar Jurnal Sosiologi Dialektika Jurnal Masyarakat Maritim Ideguru: Jurnal Karya Ilmiah Guru Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran INOVATIF: Jurnal Penelitian Pendidikan, Agama, dan Kebudayaan maktab: Jurnal Pendidikan dan Teknologi Jurnal Ilmiah Pendidikan Dasar (JIPDAS) MASALIQ: Jurnal Pendidikan dan Sains ARZUSIN: Jurnal Manajemen dan Pendidikan Dasar Inspirasi Dunia: Jurnal Riset Pendidikan dan Bahasa Lencana: Jurnal Inovasi Ilmu Pendidikan Jurnal Motivasi Pendidikan dan Bahasa Cendikia: Jurnal Pendidikan dan Pengajaran Journal of Education for All Jurnal Media Akademik (JMA) Proceeding of International Conference on Humanity Education and Society Society Jurnal Pengembangan dan Evaluasi Pendidikan eductum : Jurnal Literasi Pendidikan EDUKASI : Jurnal Penelitian dan Artikel Pendidikan
Priyono Tri Febrianto
Universitas Trunojoyo
Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics 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.
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 'Ayun, Dya Qurotul Ade Cyntia Pritasari Aditya Dyah Puspitasari Ainia, Carryna Zalfa Ali Maksum Andika Adinanda Siswoyo Arum Budiastuti, Arum Budiastuti Asbah Binti Razali Assayyidah, Jasmine Atika, Putri Ayu Puspita, Julia Bachtiar Sjaiful Bachri BACHTIAR SYAIFUL BACHRI Bekiyatus Solehah Cicilia Dyah Sulistyaningrum I. Damayanti, Salsabila Dwi Safitri Dwiyanto, Febri Fatimah Azzahra Firdaus, Adella Tiara Bintara Firdausi Nurharini Husnul Jannah Alfauzah Indrayani, Vindy Indun Bi Wastuti Irena Yolanita Maureen Khusna, Mufidatul Khusna, Mufidatul Kurniawan, Dydik Kusumawardani, Tri Nidia LAMIJAN HADI SUSARNO Lola Viska Ardani Lutfi Apreliana Megasari Lutfi Apreliana Megasari Lutfi Apreliana Megasari Mansuroh, Khusnul Laili Mas'udah, Siti Mas'udah, Siti Maulana, M. Nur Afifudin Dwi Maulidiya, Zamrotun Megasari, Lutfi Apreliana Merlia Indah Prastiwi Merlia Indah Prastiwi, Merlia Indah Mochamad Nursalim Muhamad Zakhi Ramadhan Muhammad Choerul Umam Mustikasari , Sevia Dwi Nilamsari Damayanti Fajrin Nur Diana Firdaus Puspita, Julia Ayu Putra, Moh Vikram Dwi Qodaria, Rizka Lailatul Rahmadhani, Putri Nabiella Fitri Razali, Asbah Razali, Asbah Binti Romadhon, Moh. Sulaiman Salsabila Damayanti Sandro Jadi Marulitua Nadeak Saropah, Siti Sauqi, Iqbal Siti Masitoh Sudarso Sudarso Sudarso Sudarso SUKMAWATI, DWI Sulaiman Sulaiman Sulaiman Sulaiman Sulaiman, Sabri Sulistyaningrum I., Cicilia Dyah Syafrilla Faigha Utami Syafrilla Faigha Utami Syaifullah Alramadhani Tiara Bintara Firdaus, Adella Tsabitah, Ismi Uliatul Murtasidah Umam, Muhammad Choerul Umi Hanik Wahyudi, Adisti Istivari Wahyuni, Esa Nur