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All Journal Bulletin of Computer Science Research Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi
Jefry Sunupurwa Asri
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Computer Science & IT Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Perancangan Website E-commerce Fashion dengan Brand Prolific Studi Kasus: Prolific Studio Ramzy Haediprawira; Ratna Yulika Go; Muhammmad Hadi Afrian; Jefry Sunupurwa Asri
Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Vol. 2 No. 4 (2025): SINTA: OKTOBER
Publisher : Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/sinta.v2i4.88

Abstract

Prolific Studio, sebuah brand fashion lokal asal Bekasi, menghadapi tantangan dalam memperluas pasar karena masih mengandalkan transaksi via WhatsApp tanpa dukungan platform e-commerce yang optimal. Hal ini berdampak pada keterbatasan waktu layanan dan penurunan penjualan. Penelitian ini merancang website e-commerce berbasis Laravel untuk mempermudah proses transaksi, memperluas jangkauan pelanggan, dan meningkatkan efisiensi operasional. Metode pengembangan menggunakan model prototype dengan tahapan komunikasi, pembuatan desain awal, evaluasi, hingga pengujian menggunakan Blackbox Testing. Hasilnya adalah website fungsional dengan fitur autentikasi, katalog produk, pemesanan pre-order, dan integrasi pembayaran. Platform ini diharapkan menjadi solusi strategis bagi pertumbuhan Prolific Studio ke arah digital.
Implementasi Metode Cosine Similarity Dalam Sistem Profiling Dosen Berbasis Data Bibliometrik Untuk Pemetaan Kompetensi Akademik Jefry Sunupurwa Asri; Firnanda Amalia; Muhammad Thifaal Dzaki; Muhammad Fikri; Ardra Rianisa
Bulletin of Computer Science Research Vol. 5 No. 6 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i6.811

Abstract

Lecturer profiling based on scientific publications is a strategic component in managing human resources in higher education institutions. The manual process of identifying lecturer competencies often requires considerable time and may lead to inaccuracies. This study aims to develop an automated application for lecturer profiling and competency mapping to relevant courses using an unsupervised text similarity approach based on the Term Frequency–Inverse Document Frequency (TF-IDF) and Cosine Similarity methods. The application was developed using the Streamlit framework with integrated data from Google Scholar, SINTA, and Scopus. The evaluation involved 50 lecturers and 120 lecturer–course pairs, measured using accuracy, precision, recall, F1-score, response time, and usability metrics. The results show an accuracy of 85.3%, an F1-score of 0.853, an average response time of 2.3 seconds, and a usability score of 86.4, which falls into the excellent category. The system is capable of displaying interactive lecturer profiles, performing competency mapping to relevant courses, and generating automatic reports in PDF format. Therefore, this application effectively supports data-driven academic decision-making processes for assigning lecturers according to their areas of expertise.
Co-Authors Ardra Rianisa Firnanda Amalia muhammad fikri Muhammad Thifaal Dzaki Muhammmad Hadi Afrian Ramzy Haediprawira Ratna Yulika Go