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Analysis and Testing of the Combox Web Application System Using Black Box Testing with the Equivalence Partitioning Method Dini Nurul Azizah; Ibnu Aqil Mahendar; Muhammad Fillah Alfatih; Setiady Ibrahim Anwar; Nabil Malik Al Hapid; Aditya Wicaksono; Gema Parasti Mindara
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 4 (2024): December : International Journal of Electrical Engineering, Mathematics and Com
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i4.118

Abstract

This research focuses on evaluating the Combox web application, a digital tool designed to help Food and Beverage (F&B) business owners strengthen their online presence. The analysis was carried out through Black Box Testing, specifically using the Equivalence Partitioning method, to assess core functionalities like login, logout, product management, and pagination. The findings reveal that while most features function as intended, there are issues with product addition and editing, as well as pagination when no data is available. These results highlight areas that need refinement to improve the application’s reliability and user experience. In summary, this research supports the advancement of a digital platform that enables F&B businesses to harness technology effectively in today’s competitive landscape.
Penerapan Business Model Canvas (BMC) pada Bisnis Jasa Fotografi Produk Rabbithall Studio Hasna Nabiilah Widiani; Muhammad Fillah Alfatih; Thoriq Muhammad Pasya; Wien Kuntari
Lokawati : Jurnal Penelitian Manajemen dan Inovasi Riset Vol. 3 No. 1 (2025): Januari : Jurnal Penelitian Manajemen dan Inovasi Riset
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/lokawati.v3i1.1432

Abstract

The growth of digital technology has significantly transformed the marketing strategies employed by micro, small, and medium enterprises. Within an increasingly competitive digital marketing ecosystem, high-quality visual branding serves as a crucial component in capturing consumer attention and enhancing brand awareness. However, the enterprises frequently encounter constraints related to budget, technology, and expertise in producing effective visual content. In response to these challenges, Rabbithall Studio offers a solution by providing affordable, high-quality content, particularly for enterprises that operating in the foods and beverages sector. This research aims to design a Business Model Canvas (BMC) for Rabbithall Studio to effectively support the digital marketing strategy of MSMEs. A descriptive qualitative approach is used to analyze key elements in the BMC, such as customer segments, value propositions, distribution channels, and cost structure. The research findings reveal that Rabbithall Studio has a strategic business model, focusing on providing aesthetic and Instagrammable product photography services, along with copywriting support. This study offers a strategic guide for MSMEs to adopt visual and content-based marketing to enhance their competitiveness in the digital market.
Sistem Deteksi Bahasa Isyarat Alfabet Menggunakan Dataset American Sign Language (ASL) dan Algoritma Random Forest Siti Farah Fakhirah; Muhammad Fillah Alfatih; Hasna Nabiilah Widiani; Thoriq Muhammad Pasya; Endang Purnama Giri; Gema Parasti Mindara
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 4 (2024): November : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i4.4321

Abstract

Introducing alphabetical sign language is necessary to bridge communication between deaf and hard-of-hearing people and their surrounding environment. This research aims to develop a sign language alphabet letter detection system based on American Sign Language (ASL). The research methods include data collection, feature extraction with OpenCV and Mediapipe, model development with Random Forest algorithm, and real-time system testing. The test results show that the developed system can achieve 97% prediction accuracy in recognizing hand patterns that represent ASL letters. The system uses a webcam as real-time input, providing accurate responses in various environmental conditions. This research contributes significantly to developing communication support technology for the deaf community, with implications for increased inclusivity and social engagement.