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All Journal Jurnal Nasional Teknologi Komputer Jurnal Sains Student Research
Muhammad Nabhan Akbar Marpaung
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Rancangan Sistem Informasi Pengolahan Event Kampus Berbasis Web Menggunakan Metode Waterfall Muhammad Nabhan Akbar Marpaung; sarah, siti; Ragilia Putri Dinanti; Gilang Reynabil; Nurul Fikria; Alfin Budiman Sihotang; Marini; Revina Putri Damayanti; Dinda Ayu Ningsih; Ilka Zufria
Jurnal Nasional Teknologi Komputer Vol 5 No 3 (2025): Juli 2025
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v5i3.225

Abstract

Perkembangan teknologi informasi yang pesat menuntut adanya sistem digital yang efisien dan terintegrasi dalam berbagai sektor, termasuk di lingkungan kampus. Laporan ini membahas perancangan dan implementasi Sistem Informasi Pengelolaan Event Kampus berbasis web yang bertujuan untuk menggantikan proses manual dalam manajemen event seperti seminar, pelatihan, dan kegiatan organisasi kemahasiswaan. Sistem ini dirancang dengan metode Waterfall yang mencakup tahapan analisis kebutuhan, perancangan, implementasi, pengujian, dan dokumentasi. Fitur utama yang disediakan antara lain manajemen event, pendaftaran peserta, pencatatan kehadiran, laporan kegiatan, dan dokumentasi galeri. Dengan penerapan sistem ini, proses pengelolaan event menjadi lebih cepat, efisien, dan terdokumentasi secara baik, sekaligus meningkatkan transparansi dan profesionalisme penyelenggara. Sistem dikembangkan menggunakan teknologi open-source seperti HTML, CSS, PHP, JavaScript, dan MySQL. Hasil implementasi menunjukkan bahwa sistem mampu memenuhi kebutuhan pengguna dan mendukung kegiatan event internal kampus secara optimal.
PENINGKATAN AKURASI PREDIKSI PENJURUSAN SISWA SMK DENGAN OPTIMASI JARINGAN SYARAF TIRUAN BACKPROPAGATION Muhammad Nabhan Akbar Marpaung; Lailan Sofinah Harahap; Fajar Al Fahri
JOURNAL SAINS STUDENT RESEARCH Vol. 4 No. 1 (2026): Februari
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v4i1.7975

Abstract

The development of artificial intelligence (AI) technology has had an increasingly significant impact on various industries, including education, particularly in terms of data processing and decision making. However, in reality, students' choice of major is often determined without proper and measurable analysis, which means that students' potential is not always in line with their chosen major. The mismatch between academic abilities and chosen fields of study is one of the problems arising from this situation. To address this issue, this study predicts majors based on subject grade data using Artificial Neural Network techniques and the Backpropagation algorithm. Backpropagation was chosen because it can produce more accurate predictions by gradually learning data patterns through a directed learning process. This approach significantly improves prediction accuracy based on model training and testing results, making it a useful tool for more objective, flexible, efficient, adaptive, and data-driven decision-making in optimally selecting majors for students to support their overall and sustainable academic success.
Co-Authors Alfin Budiman Sihotang Dinda Ayu Ningsih Fajar Al Fahri Gilang Reynabil Ilka Zufria Lailan Sofinah Harahap Marini Nurul Fikria Ragilia Putri Dinanti Revina Putri Damayanti Siti Sarah, Siti