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All Journal Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Sistem Informasi Dan E-Bisnis
Fathon, Ulil
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Rancang Bangun Sistem E-Commerce Umkm Bobapeer Menggunakan Python Dengan Framework Flask Fathon, Ulil; Diana Laily Fithri
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i3.8145

Abstract

Along with the development of the internet in this increasingly advanced digital era, it encourages the growth of technology as a solution to improve business performance. One of the developing technologies is website-based applications which are one of the strategic solutions to increase competitiveness, with website-based applications being able to help promote, market, and sell products more widely. This study develops a website-based e-commerce system for UMKM Bobapeer, a boba drink business, with the aim of supporting promotion, sales, and operational management more efficiently. The system is designed using the Waterfall method, this study goes through the stages of planning, needs analysis, system design, development, testing, and implementation. The Python Flask framework was chosen as the main tool in system development. The results of this system development provide an effective solution to increase product visibility, facilitate transactions, and increase customer satisfaction
Analisis Sentimen Ulasan Pengguna Aplikasi Crunchyroll, iQIYI, Wibuku di Google Play Store Menggunakan Metode Random Forest Fathon, Ulil; Arifin, Muhammad; Setiawan, Arif
Jurnal Sistem Informasi dan E-Bisnis Vol 7 No 2 (2025): Agustus
Publisher : LPPMPP Yayasan Sejahtera Bersama Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54650/jusibi.v7i2.603

Abstract

This study evaluates the performance of the Random Forest model in classifying sentiment in reviews on three platforms, namely Crunchyroll, iQIYI, and Wibuku. The evaluation results show an accuracy of 82% on Crunchyroll, 83% on iQIYI, and 84% on Wibuku. On Crunchyroll, the model demonstrated superiority in recognizing negative reviews (precision 88%, recall 84%), while on iQIYI and Wibuku, the best performance was achieved on positive sentiment with a precision of 94% and 92%, respectively. However, the precision values for negative sentiment on iQIYI (62%) and Wibuku (66%) indicate room for improvement, particularly in enhancing the model's ability to identify negative-toned reviews. Overall, Random Forest proved effective for cross-platform sentiment analysis; however, further optimization is needed to improve the performance balance across sentiment categories.
Co-Authors Arif Setiawan Diana Laily Fithri Muhammad Arifin