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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) OKTAL : Jurnal Ilmu Komputer dan Sains
Yuniarti, Dian Tri
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Astronomy Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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Perancangan Aplikasi Pendataan Stok Opname Gudang Berbasis Web Menggunakan Metode Waterfall pada Kopiluvium Yuniarti, Dian Tri; Alfitra, Ramadhani; Prasetya, Rivaldi Nicho; Saprudin
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 4 No 05 (2025): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

This study discusses the design of a web-based stock opname recording application for Kopiluvium using the Waterfall method. The application is designed to address the challenges of manual stock recording, which is prone to errors, slow, and inefficient. The Waterfall method is implemented through the stages of requirements analysis, system design, implementation, testing, and maintenance. Tools used include PHP, MySQL, and UML diagrams (Use Case Diagram, Activity Diagram, ERD). The system includes multi-level login (admin, staff, employee), item data input, stock in/out recording, automatic reports, and role-based access. Testing using the blackbox method shows that the system functions according to user needs. This study demonstrates that a web-based system can improve warehouse stock recording efficiency and support faster, more accurate managerial decision-making.
Analisis Penerapan Metode WASPAS untuk Penentuan Pola Belajar Mahasiswa Berdasarkan Gaya Belajar Ester, Ria; Yuniarti, Dian Tri; Valentina, Putri Eka; Kusumah Putra, Faris Maulana
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10768

Abstract

Higher education in the digital era requires learning approaches that are able to adapt to individual student characteristics, including differences in learning styles. This study aims to develop a model for assessing students’ learning patterns and to provide more personalized learning recommendations using the Weighted Aggregated Sum Product Assessment (WASPAS) method. The data used are secondary data obtained from 1,000 students with seven learning criteria, namely academic score, course participation, attendance rate, physical activity, emotional engagement, device usage, and feedback score. The WASPAS method is applied through two main stages, namely the calculation of the Weighted Sum Model (WSM) and the Weighted Product Model (WPM), which are then aggregated to produce a composite WASPAS score for each student. Manual calculations are demonstrated using five student samples, while computations for the entire dataset are performed using Python in the Jupyter Notebook environment. The results show that students’ WASPAS scores range from 0.2815 to 0.9914 with a distribution that tends to be normal. Most students fall into the “fair” to “very good” learning pattern categories, while a small proportion are classified as “very high” and “requiring special attention.” Analysis based on visual, auditory, and kinesthetic learning styles indicates differences in average WASPAS scores across groups, supporting the effectiveness of the WASPAS method in integrating multiple learning criteria simultaneously. These findings demonstrate that WASPAS can be used as a decision support tool to map student learning profiles and assist in designing more adaptive, targeted, and personalized learning strategies in higher education
Co-Authors Alfitra, Ramadhani Ester, Ria Kusumah Putra, Faris Maulana Prasetya, Rivaldi Nicho Saprudin Valentina, Putri Eka