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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 Ade Irma Damayanti Aditya Dyah Puspitasari Adji Setyorini, Devi Agustina, Rufaydah Aidah Fatin, Rifka Ainia, Carryna Zalfa Ainur Rasikin, Muhammad Aisyah Noviyanti, Nur Ali Maksum Alya Agustina, Dea Anam Wijaya, Fahriz Andika Adinanda Siswoyo Andina, Aizza Naya Arum Budiastuti, Arum Budiastuti Arwinda, Vivi Prila Asbah Binti Razali Assayyidah, Jasmine Atika, Putri Ayu Puspita, Julia Ayu Ramanda Putri, Inessya Bachtiar Sjaiful Bachri BACHTIAR SYAIFUL BACHRI Bekiyatus Solehah Catharina Indah Kartika Cicilia Dyah Sulistyaningrum I. Damayanti, Salsabila Desy Elvira Wulandari Dwi Elsa Romadhoni Dwi Rahmah, Alisyah Dwi Safitri Dwiyanto, Febri Dzofiroh, Amirotudz Eky Junnata Marianadya Sukadi Elsa Safila, Amanda Faizatul Hasanah Fatimah Azzahra Fatiya Auliya Firdaus, Adella Tiara Bintara Firdausi Nurharini Firmansyah, Muhammad Bagus Hamim Mullah Wahyu Ramadhan Hana Naina Hanum Eka Pratiwi , Citra Hoiris Zuhro, Istihana Husnul Jannah Alfauzah Indrayani, Vindy Indun Bi Wastuti Inggrid Adithalia Irena Yolanita Maureen Izmatul Mufidah Jumiyati Khoirunisa, Dinda Salsabila Khoirus Tsani, Mutiara Khusna, Mufidatul Khusna, Mufidatul Kurniawan, Dydik Kusumawardani, Tri Nidia LAMIJAN HADI SUSARNO Lola Viska Ardani Luluk Lailatul Mukarromah Lutfi Apreliana Megasari Lutfi Apreliana Megasari Lutfi Apreliana Megasari Mafrichatuz Zuhroh Maghfirli Islami, Sabrina Mansuroh, Khusnul Laili Mas'udah, Siti Mas'udah, Siti Maulana, M. Nur Afifudin Dwi Maulidiya, Zamrotun Mega Kalista, Zulfia Megasari, Lutfi Apreliana Merlia Indah Prastiwi Merlia Indah Prastiwi, Merlia Indah Mochamad Nursalim Mohassin Muhamad Zakhi Ramadhan Muhammad Choerul Umam Muhammad Fahmi Amrullah Mustikasari , Sevia Dwi Nanda Dwi Saharani, May Nilamsari Damayanti Fajrin Noverawati Syah Fitri Novia Safitri, Narulita Novita, Tri Nur Choyyina, Avin Nur Diana Firdaus Nur Rohmah, Eva Nurul Fauziyah, Vita Nuudiya Anburika Puspita, Julia Ayu Putra, Moh Vikram Dwi Putri Faridatus Sholeha Putri, Aqsha Maulidiyah Qodaria, Rizka Lailatul Rafiqa Dzurriyya Rahmadhani, Putri Nabiella Fitri Razali, Asbah Razali, Asbah Binti Reftamey, Cinta Ricca Aulia Rahma Rifatil Badri, Zafirah Rizky Dellentya Romadhon, Moh. Sulaiman Sahlya, Zulfatus Salsabila Damayanti Sandro Jadi Marulitua Nadeak Sari Devi, Kartika Saropah, Siti Sauqi, Iqbal Sayyidati Beta Masbaha Azzahroh Seftinengseh, Nurjihan Shofiyati Fadilah Siti Chomsiyatul Ma'rifah Siti Masitoh Sri Wilda Ningsih 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 Warosatul Amalia Yudha Aldetya Fanda Febriarta Zainur Rohman