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All Journal Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Jurnal Informasi dan Komunikasi Administrasi (JIKAP)
Muhammad Choerul Umam
Universitas Sebelas Maret
Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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IMPLEMENTASI SISTEM INFORMASI ADMINISTRASI KEPENDUDUKAN (SIAK) DI KELURAHAN PUCANGSAWIT KECAMATAN JEBRES KOTA SURAKARTA Frans Ellyon Gracio; Anton Subarno; Muhammad Choerul Umam
JIKAP (Jurnal Informasi dan Komunikasi Administrasi Perkantoran) Vol 6, No 4 (2022): November
Publisher : Program Studi Pendidikan Administrasi perkantoran FKIP UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jikap.v6i4.59852

Abstract

Penelitian ini bertujuan untuk (1) menganalisis serta mengevaluasi penerapan Sistem Informasi Administrasi Kependudukan (SIAK) di kelurahan Pucangsawit, kecamatan Jebres, kota Surakarta, (2) mengetahui kendala dalam Penerapan Sistem Informasi Administrasi Kependudukan (SIAK) di kelurahan Pucangsawit, kecamatan Jebres, kota Surakarta serta  upaya pengembangan sistem yang telah ada. Penelitian ini merupakan penelitian deskriptif kualitatif dengan pendekatan studi kasus (case study). Sumber data dalam penelitian ini menggunakan sumber data library research. Teknik pengambilan sampel dilakukan dengan purposive sampling dan snowball sampling. Pengumpulan data dilakukan dengan kepustakaan, observasi, wawancara dan dokumentasi. Teknik uji validitas data yang digunakan adalah triangulasi sumber dan metode. Analisis data menggunakan penyajian data, reduksi data, penyajian data, serta tahap akhir. Hasil penelitian ini menunjukkan pelaksanaan penggunaan Sistem Informasi Administrasi Kependudukan (SIAK) di Kelurahan Pucangsawit Kecamatan Jebres Kota Surakarta telah  memenuhi dua aspek dalam teori Technology Acceptance Model, yakni persepsi kebermanfaatan (perceived usefulness) serta persepsi kemudahan pengguna (perceived ease of use). Sementara itu, fasilitasi pelatihan serta sosialisasi dinilai penting sebagai kunci dalam menyelesaikan permasalahan kompetensi pegawai atau petugas dalam penerapan Sistem Informasi Administrasi Kependudukan (SIAK) serta upaya pelaksanaan koordinasi lebih lanjut dalam mengembangkan sistem yang telah ada.  
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 Cicilia Dyah Sulistyaningrum I. Frans Ellyon Gracio Kurniawan, Dydik Patni Ninghardjanti Priyono Tri Febrianto Winarno Winarno