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All Journal Jurnal Teknologi Informatika dan Komputer Golden Ratio of Social Science and Education
Hidayat, Dahlan
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Religion Humanities Computer Science & IT Education Electrical & Electronics Engineering Social Sciences Other

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Organizational Resilience Development Model Irdiyansah, Iyan; Sintesa, Nika; Fitri, Euis Nessia; Hidayat, Dahlan; Kasih, Arisanti Muara
Golden Ratio of Social Science and Education Vol. 5 No. 1 (2025): December - May
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grsse.v5i1.1230

Abstract

This study aims to develop a model to strengthen organizational resilience in the form of a constellation model of variables, along with its mathematical model, which was identified in the field through an initial preliminary survey and interviews with pre-selected informants. The unit of analysis is LP3I Polytechnic Jakarta, involving 30 lecturers as informants and interviews with 11 campus heads to determine the research constellation identified in the field. The study will also uncover strategies and methods to enhance organizational resilience. From this model, research hypotheses are derived, which will be tested using path analysis during the quantitative research phase. The research begins with interviews with informants considered competent and recognized as experts in providing the expected responses. Subsequently, data reduction, data coding, data presentation, data analysis, and conclusion drawing are conducted. The research is carried out at LP3I Polytechnic Jakarta during the period of December to January 2025. The study identified several variables that are presumed to have a positive and dominant influence on organizational resilience, namely visionary leadership, knowledge management, empowerment, professional commitment, and trust as an intervening variable
A Comparative Study of DenseNet-201 and Swin Transformer for Malignant and Benign Skin Lesion Classification Hidayat, Dahlan; Musyafa, Ahmad; Handayani, Murni
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3265

Abstract

Skin cancer has a high global prevalence, underscoring the need for accurate and efficient early detection systems to support screening. This study presents a comparative analysis of DenseNet-201 and Swin Transformer for binary classification of malignant and benign skin lesions using the BCN20000 dataset, which contains 12,413 dermoscopic images. The proposed workflow includes image preprocessing and augmentation, transfer learning-based model training, and evaluation under a 5-fold stratified cross-validation protocol. Performance is assessed using Accuracy, Precision, Sensitivity (Recall), F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). In addition, computational efficiency is examined in terms of parameter count, model size, and training time. Across five folds, DenseNet-201 achieved 88.05% Accuracy, 88.90% Precision, 89.48% Sensitivity, 89.17% F1-score, and 94.73% AUC, whereas Swin Transformer achieved 87.42% Accuracy, 89.77% Precision, 87.06% Sensitivity, 88.39% F1-score, and 94.33% AUC. A paired t-test at α = 0.05 indicated no statistically significant performance difference between the two models. Model interpretability was investigated using Grad-CAM for DenseNet-201 and EigenCAM for Swin Transformer to verify that predictions were driven by lesion-relevant regions. Overall, the results suggest that both architectures are suitable candidates for dermoscopic image-based skin lesion screening support systems, including teledermatology applications.
Co-Authors Fitri, Euis Nessia Irdiyansah, Iyan Kasih, Arisanti Muara Murni Handayani musyafa, ahmad Sintesa, Nika