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All Journal International Journal of Research in Community Services International Journal of Quantitative Research and Modeling
Hilda Azkiyah
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Physics Social Sciences Other

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The Impact of Digital Marketing Training on Increasing Sales Volume for Micro-Enterprises (A Case Study in the Culinary Sector) Hilda Azkiyah; Risfa Mustifa Wangi
International Journal of Research in Community Services Vol. 7 No. 4 (2026): International Journal of Research in Community Service
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v7i4.1375

Abstract

Digital marketing has become an essential strategy for improving the competitiveness of micro, small, and medium-sized enterprises (MSMEs). However, existing studies have predominantly examined digital marketing adoption as a general organizational capability, while limited empirical evidence is available regarding whether the intensity of digital marketing engagement is associated with sales performance among culinary micro-enterprises. This study addresses this gap by investigating the relationship between digital marketing intensity and sales volume using a quantitative explanatory design based on data from 4,546 culinary micro-enterprises. Descriptive statistics, Pearson correlation analysis, simple linear regression, independent-samples t-tests, and one-way ANOVA were employed to evaluate the proposed relationships. The results reveal a significant positive association between digital marketing intensity and sales volume, with regression analysis indicating that higher digital marketing intensity significantly increases sales performance (b = 22.188, p < .001) and explains 27.8% of the observed variation. Consistent findings from the independent-samples t-test further demonstrate that enterprises with high digital marketing intensity achieve substantially greater sales than those with low intensity. In contrast, no significant differences in sales volume were identified across nano, micro, macro, and mega promotional-partner tiers, suggesting that the consistency of digital marketing engagement, rather than the scale of promotional partners, is more strongly associated with commercial performance. These findings contribute to the digital marketing and MSME literature by demonstrating that digital marketing intensity constitutes a distinct explanatory dimension beyond simple technology adoption. The study also provides practical implications for policymakers and training providers by emphasizing sustained digital engagement as a more effective strategy for enhancing the sales performance of culinary micro-enterprises.
A Quantitative Study on the Accuracy of Deep Learning Algorithms in Classifying Machine Component Image Defects Hilda Azkiyah; Panji Agung; Astrid Sulistya
International Journal of Quantitative Research and Modeling Vol. 7 No. 3 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i3.1454

Abstract

Automated visual inspection increasingly relies on deep learning to identify machine-component damage with high accuracy and consistent performance. However, direct comparison among architectures remains difficult because differences in data preparation, training procedures, and evaluation protocols can substantially affect reported results. This study quantitatively evaluates four deep learning architectures—conventional convolutional neural network, ResNet50, EfficientNet-B0, and MobileNetV3—for multi-class classification of machine-component images comprising Normal, Crack, Wear, Deformation, and Corrosion categories. A dataset of 1,230 images was processed using standardized preprocessing, stratified 70:15:15 partitioning, training-only augmentation, and a common model-development procedure. Performance was assessed using accuracy, macro-precision, macro-recall, macro-F1-score, confusion matrices, repeated five-fold cross-validation, feature-importance analysis, and computational indicators. On the independent test set, the baseline convolutional neural network, EfficientNet-B0, and MobileNetV3 achieved perfect classification performance, with accuracy and macro-F1-score of 1.000, whereas ResNet50 achieved 0.989 accuracy and 0.988 macro-F1-score. Cross-validation provided a more nuanced result: ResNet50 obtained the highest mean macro-F1-score (0.9934) and the lowest variability (SD = 0.0021), while the difference between the baseline convolutional neural network and EfficientNet-B0 was not statistically significant. No model misclassified a damaged component as Normal. Feature-importance analysis identified a concentrated contribution from selected image-derived descriptors, while MobileNetV3 demonstrated the lowest computational burden. The findings demonstrate that predictive performance, stability, interpretability, and computational efficiency should be jointly considered when evaluating deep learning models for industrial visual inspection.
Co-Authors Astrid Sulistya Panji Agung Risfa Mustifa Wangi