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All Journal Journal of Educational Science and E-Learning JITCoS : Journal of Information Technology and Computer System
Dimas Aqila Aptanta
Universitas Islam Negeri Sumatera Utara
Computer Science & IT Control & Systems Engineering Education Languange, Linguistic, Communication & Media Other

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Implementation of a Favorite Course Search System Based on Students’ Average Grades Using the A* Algorithm Dwiky Oldi Amsyah; Rusma Riansyah; Dimas Aqila Aptanta; Muhammad Randy Fachrezi; Nasywa Roudhotul Firdaus
Journal of Information Technology and Computer System Vol. 1 No. 2 (2025): December
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i2.50

Abstract

Optimal selection of elective courses plays an important role in supporting students’ academic success and ensuring alignment between learning interests and final project preparation. This study aims to develop a favorite course search system based on the A-Star (A*) algorithm by utilizing students’ average grades as the main evaluation variable. The system was implemented using the Java NetBeans platform, supported by datasets consisting of course names, credit weights (SKS), and grade distributions. The A* algorithm was adapted through the integration of heuristic components, including Standard Deviation and Relative Credit Load, to improve accuracy in identifying optimal course recommendations. Experimental results demonstrate that the system is capable of generating recommendations with an accuracy rate of 95%, verified through comparison between system outputs and manual calculations. The results also show that the Mitigation course ranked highest with a score of 6.1, indicating strong student performance in that subject. Overall, the system provides a practical and efficient solution for academic decision-making, enabling students to select elective courses more strategically based on data-driven insights. This study contributes to the development of computational methods in educational recommendation systems and opens opportunities for further enhancement through integration with real academic databases.
Analisis Performa CNN Berbasis MobileNetV2 pada Dataset Citra Dokumen Laporan Praktikum Hafiz Aryanda; Lailan Sofinah Harahap; Dimas Aqila Aptanta
Journal of Educational Science and E-Learning Vol. 2 No. 2 (2025): Desember
Publisher : CV Rena Cipta Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62354/jese.v2i2.44

Abstract

Penelitian ini bertujuan untuk menganalisis performa CNN berbasis MobileNetV2 dalam klasifikasi citra dokumen laporan praktikum. Pendekatan yang digunakan adalah transfer learning dengan bobot pra-latih dari ImageNet, serta fine-tuning pada lapisan atas untuk menyesuaikan model terhadap karakteristik dokumen akademik. Dataset terdiri dari ribuan citra dokumen yang telah melalui tahap pra-pemrosesan berupa resizing, normalisasi, dan augmentasi. Hasil eksperimen menunjukkan bahwa pada baseline, akurasi validasi mencapai 64,88% namun akurasi evaluasi manual hanya 10,54%, menandakan adanya masalah domain gap dan kesulitan separasi antar kelas. Fine-tuning meningkatkan akurasi validasi training menjadi 70,43%, tetapi akurasi evaluasi biner justru menurun menjadi 47,50%, sehingga performa model tetap rendah. Temuan ini menegaskan bahwa CNN kurang tepat untuk tugas validasi kepatuhan dokumen yang membutuhkan analisis semantik dan aturan tata letak yang kompleks serta dinamis. Sebagai solusi, integrasi OCR dengan rule-based validation direkomendasikan karena mampu memberikan akurasi lebih tinggi, interpretasi jelas, serta umpan balik detail sesuai pedoman penulisan akademik modern.
Co-Authors Dwiky Oldi Amsyah Hafiz Aryanda Lailan Sofinah Harahap Muhammad Randy Fachrezi Nasywa Roudhotul Firdaus Rusma Riansyah