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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ekonomi dan Bisnis Digital (MINISTAL)
Matrissya Hermita
Gunadarma University
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering

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How Social Support and Grit Influence Entrepreneurs’ Innovative Behavior in Micro and Small Enterprises: The Mediating Role of Creativity Matrissya Hermita; Nurlintang Putri Ayuning Rizal; Budi Hermana
Jurnal Ekonomi dan Bisnis Digital Vol. 2 No. 1 (2023): January, 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ministal.v2i1.2468

Abstract

Business is characterized by an uncertain and fast-growing environment, so innovation is important in that sphere. The study aims to examine the influence of social support and grit on innovative work behaviour with creativity as a mediation role among entrepreneurs in micro and small enterprises. A survey questionnaire was used in this research to gather data from various region in Indonesia. There were 130 respondents which have been running their business for at least two years gathered by accidental sampling technique. Data analysis used two-model of multiple regression and path analysis was used for determine the mediation role. The results showed that there was an influence between social support, grit with mediation of creativity on innovative behavior. This means that social support and grit can affect an individual's level of creativity, and creativity can drive innovative behavior in the workplace. The result suggests that creativity can act as a mediation variable. Grit is known to be the dominant variable with a 47.1% contribution in influencing innovative behavior through creativity, and the rest is influenced by other factors outside the study, such as job characteristic.
Efficient deep learning for automated corneal ulcer severity classification from fluorescein images Rodiah Rodiah; Indah Sinthya Permata Sari; Matrissya Hermita; Sarifuddin Madenda; Diana Tri Susetianingtias
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3603-3613

Abstract

Corneal ulcers can cause permanent vision loss if not diagnosed and managed promptly, particularly in settings with limited access to ophthalmology services. This study aims to develop an automated deep learning approach for classifying corneal ulcer severity from fluorescein slit-lamp images. An EfficientNetV2-S–based model is employed, incorporating corneal area masking to suppress non-relevant regions and class distribution–based augmentation to address data imbalance. To improve evaluation reliability, a leakage-aware data splitting strategy is applied before and after augmentation. Experimental results show that the proposed approach achieves a maximum validation accuracy of 95.93% under non-leakage conditions for the category classification scenario, while maintaining high training efficiency. These results demonstrate that the proposed method provides a robust and efficient solution for automated corneal ulcer severity assessment and has the potential to support clinical decision-making in ophthalmic practice.
Co-Authors Budi Hermana Indah Sinthya Permata Sari Nurlintang Putri Ayuning Rizal Rodiah Rodiah Sarifuddin Madenda Susetianingtias, Diana Tri