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This Author published in this journals
All Journal Jurnal Penelitian Ekonomi Akuntansi (JENSI) Journal of Computer Networks, Architecture and High Performance Computing
Yesti Siti Nurjanah
Politeknik Triguna Tasikmalaya
Computer Science & IT Economics, Econometrics & Finance Education

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Penerapan SAK EMKM berbasis Penggunaan QRIS dan Literasi Keuangan (Studi persepsi Pelaku UMKM Kuliner Tasikmalaya) Yesti Siti Nurjanah; Taufik Wibisono
Jurnal Penelitian Ekonomi Akuntansi Vol 7 No 2 (2023)
Publisher : Program Studi Akuntansi Fakultas Ekonomi Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33059/jensi.v7i2.9007

Abstract

The writing of financial reports by culinary MSME players in Tasikmalaya is still experiencing obstacles due to the low understanding of financial literacy and the application of manual reporting systems. Although in fact, the preparation of financial reports that meet the standards will make it easier to get access to funding. To help make the quality of standard financial reports, this study aims to determine the effectiveness of financial literacy and the decision to use QRIS on the implementation of financial reporting based on SAK EMKM. this study uses a quantitative method with an explantory survey of culinary MSME players in Tasikmalaya who have used QRIS. The sampling technique used rondom sampling and managed to collect data from 158 respondents. The results showed that financial literacy contributed to the decision to use QRIS and SAK EMKM reporting. The decision to use QRIS makes the highest contribution to MSME actors in reporting finances based on SAK EMKM. The results of the study provide valuable direction for culinary MSME players in Tasikmalya that to facilitate the preparation of financial reports based on SAK EMKM, they can implement transactions with QRIS and continue to improve financial literacy. The limitations of this study only use research objects focused on Culinary MSMEs in Tasikmlaya and only examine the use of QRIS for financial reports. Suggestions for future research can conduct research with a wider and more diverse object, and can develop with other variables that can improve the quality of financial reports for business sustainability.
Explainable Machine Learning Framework for Thyroid Cancer Recurrence Prediction Tuti Alawiyah; Taufik Wibisono; Recha Abriana Anggraini; Bambang Kelana Simpony; Yesti Siti Nurjanah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8894

Abstract

Accurate prediction of thyroid cancer recurrence is essential for improving long-term patient management and supporting evidence-based clinical decision-making. Although machine learning has demonstrated promising predictive performance, limited model interpretability remains a major barrier to its clinical adoption. This study aims to develop an Explainable Machine Learning framework for thyroid cancer recurrence prediction by integrating Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) using clinicopathological features. A publicly available dataset containing 383 patient records was preprocessed through label encoding, correlation analysis, Chi-Square-based feature selection, and Min-Max normalization. Logistic Regression, Decision Tree, Random Forest, and XGBoost were comparatively evaluated using 10-fold stratified cross-validation with Accuracy, Precision, Recall, F1-score, and ROC-AUC as evaluation metrics. The best-performing model was subsequently interpreted using global and local SHAP analyses. XGBoost achieved the highest performance, with an accuracy of 95.8% ± 4.4%, precision of 93.4% ± 8.3%, recall of 91.4% ± 9.9%, F1-score of 92.2% ± 8.3%, and ROC-AUC of 98.6% ± 2.5%, outperforming the other models. SHAP analysis identified Response, Risk, and N Stage as the most influential clinicopathological factors affecting recurrence prediction. This study contributes by developing a unified Explainable Machine Learning framework that integrates comparative model evaluation, XGBoost prediction, and global and local SHAP interpretation within a single workflow. The proposed framework provides accurate and clinically interpretable recurrence prediction, supporting trustworthy risk assessment and personalized decision-making in thyroid cancer management.
Co-Authors Alawiyah, Tuti Bambang Kelana Simpony Recha Abriana Anggraini Taufik Wibisono