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All Journal Journal of Digital Business and Innovation Management
Budhiraja, Irsyad
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Decision Sciences, Operations Research & Management Library & Information Science

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ANALISIS DAN DESAIN SISTEM PENDATAAN KEWIRAUSAHAAN MAHASISWA Budhiraja, Irsyad; Febbyanti, Adellyna Cantica; Zahroh, Syifaul Aini; Purnomo, Marcello Hamasias Dwi Greatajoy; Safitri, Anita
Journal of Digital Business and Innovation Management Vol. 2 No. 1 (2023): June 2023
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jdbim.v2i1.53708

Abstract

The student entrepreneurship data application is an innovative solution to enhance entrepreneurship's efficiency and development within the higher education environment. In an increasingly competitive era, entrepreneurship has become crucial in harnessing students' potential to create job opportunities and drive economic growth. This application is designed to facilitate collecting, processing and analysing data related to student entrepreneurship across various universities. Integrated and systematic data management will assist educational institutions in monitoring the progress of student entrepreneurship, identifying trends and patterns, and taking strategic measures to support entrepreneurial activities. Key features of this application include student entrepreneurship registration, collection of individual and business profile data, document archiving, and the generation of entrepreneurship reports and analysis. Students can register themselves as entrepreneurs and provide information about their ongoing or past business ventures. Faculty, counsellors, and other relevant parties can then utilise this data to provide appropriate guidance, training, and support. Furthermore, the application provides various data analysis and visualization tools, aiding in identifying untapped business trends and opportunities. Information regarding business types, success rates, and challenges student entrepreneurs face will provide valuable insights to universities in developing curricula, programs, and entrepreneurship development activities. With the student entrepreneurship data application, it is expected that educational institutions can optimize their role in supporting student entrepreneurship. The information collected through this application can be utilized to track progress, identify new needs and opportunities, and design appropriate strategies to enhance students' success in their entrepreneurial endeavours.
Systematic Review on Breast Cancer Classification Using Random Forest and Extreme Learning Machine: Cost Sensitivity and Computational Complexity Perspectives Budhiraja, Irsyad; Dhenabayu, Riska
Journal of Digital Business and Innovation Management Vol. 4 No. 2 (2025): December 2025-Article in Press
Publisher : Universitas Negeri Surabaya

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Abstract

Breast cancer remains one of the most common and deadly cancers affecting women worldwide. Early detection and accurate diagnosis are essential to improve patient survival rates and reduce long-term treatment costs. With the advancement of digital technologies, machine learning (ML) has emerged as a powerful tool in breast cancer classification. Among various ML algorithms, Random Forest (RF) and Extreme Learning Machine (ELM) have gained prominence due to their predictive capabilities. This systematic literature review aims to compare the classification performance of RF and ELM, focusing on cost sensitivity and computational complexity. Using PRISMA guidelines, 60 peer-reviewed articles published between 2013 and 2024 were analyzed. The findings show that RF generally offers high accuracy and robustness against overfitting, making it suitable for complex clinical datasets. Conversely, ELM excels in training speed and computational efficiency, making it ideal for real-time diagnostic systems. However, both methods face challenges in handling imbalanced data, where misclassification of malignant cases can be fatal. Cost-sensitive learning strategies are shown to improve model sensitivity toward minority classes, though their integration into ELM remains limited. Furthermore, computational efficiency is a critical factor, particularly in resource-constrained medical environments. This review provides a thematic synthesis of current research and highlights future directions, such as developing hybrid models combining RF’s accuracy with ELM’s efficiency, and implementing explainable AI for trustworthy clinical adoption.
Co-Authors Febbyanti, Adellyna Cantica Purnomo, Marcello Hamasias Dwi Greatajoy Riska Dhenabayu Safitri, Anita Zahroh, Syifaul Aini