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Jurnal Riset Multidisiplin Edukasi
Published by PT. Hasba Edukasi Mandiri
ISSN : -     EISSN : 30629624     DOI : -
Core Subject : Education,
Other
Jurnal Riset Multidisiplin Edukasi adalah jurnal peer-review yang bertujuan untuk memfasilitasi pertukaran pengetahuan dan ide-ide inovatif di antara para peneliti, akademisi, dan praktisi dari berbagai disiplin ilmu. Kami menerima kontribusi ilmiah dalam bentuk artikel penelitian, tinjauan pustaka, studi kasus, dan laporan teknis yang mencakup berbagai bidang keilmuan, termasuk tetapi tidak terbatas pada: Sains dan Teknologi: Fisika, Kimia, Biologi, Matematika, Informatika, Teknik, dan Ilmu Lingkungan. Ilmu Sosial dan Humaniora: Sosiologi, Antropologi, Psikologi, Ekonomi, Pendidikan, Hukum, Sejarah, dan Filsafat. Kesehatan dan Kedokteran: Kedokteran, Farmasi, Kesehatan Masyarakat, Keperawatan, dan Ilmu Biomedis. Bisnis dan Manajemen: Manajemen, Akuntansi, Keuangan, Pemasaran, dan Kewirausahaan.
Arjuna Subject : Umum - Umum
Articles 1,411 Documents
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PEMBUATAN INTELLIGENT LEARNING SYSTEM BERBASIS WEB MENGGUNAKAN LARAVEL DAN PYTHON (STUDI KASUS: SMA NEGERI 2 KARANGANYAR) Andrea Ramadhan Cahya Aryana
Jurnal Riset Multidisiplin Edukasi Vol. 3 No. 8 (2026): Jurnal Riset Multidisiplin Edukasi (Agustus 2026)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v3i8.2521

Abstract

Selecting a major track in senior high school represents a pivotal phase deeply intertwined with students' preferences, capabilities, and future academic planning. The instructional process at SMA Negeri 2 Karanganyar currently relies on conventional methods and lacks a direct, continuous learning evaluation mechanism, leading tenth-grade students to encounter difficulty when choosing their academic track for eleventh grade. To address this issue, this study aims to develop a web-based Intelligent Learning System that couples the Laravel framework as the core architecture with Python as a recommendation engine via REST API, automatically generating tailored learning materials and track recommendations (Science/Social Science). The study employs a mixed-methods approach utilizing the Rapid Application Development (RAD) framework. Data were gathered through observation, interviews, literature review, and documentation, while data analysis was executed by combining SWOT analysis with a Content-Based Filtering algorithm powered by TF-IDF and cosine similarity.

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