Transformasi Digital dalam Pengelolaan Aset Pemerintah pada Badan Keuangan dan Aset Daerah Kabupaten Takalar

Nasir Nasir — 2026-05-30

Local government asset management is an important aspect in realizing accountable and transparent governance. However, in practice, asset management still faces various problems, such as administrative disorder, inaccurate data, and weak supervision. Along with the development of information technology, digital transformation is seen as a strategic solution to improve the quality of government asset management. This research aims to examine the implementation of digital transformation in government asset management at the Takalar Regency Regional Finance and Assets Agency and identify supporting and inhibiting factors in its implementation. This study uses a qualitative approach with a case study method. Data collection techniques were carried out through in-depth interviews, observations, and documentation studies. The results of the study show that the implementation of the regional asset management information system has improved administrative order, the quality of asset data, and the transparency and accountability of local government asset management. However, the implementation of digital transformation is not fully optimal because it is still faced with limited human resource competencies, work culture adaptation, and technology infrastructure support. Therefore, continuous efforts are needed to strengthen the capacity of apparatus, internal policies, and infrastructure to optimize the benefits of digital transformation in the management of local government assets.

Optimalisasi Splitting Data untuk Kinerja Robust Model EfficientNetV2-B0 pada Deteksi Pneumonia

Syanti Irviantina — 2026-05-30

The splitting of datasets constitutes a fundamental yet frequently overlooked methodological decision in deep learning research for medical image classification. This study investigates the impact of various data splitting scenarios on the robust performance of the EfficientNetV2-B0 model in pneumonia detection using chest X-ray images. Using the Kaggle Chest X-ray Pneumonia dataset, seven experimental scenarios were designed encompassing differences in train-validation-test allocation ratios (70/15/15, 70/10/20, 80/10/10, 85/15, 70/30), partition strategies (stratified vs. random), and validation methods (holdout vs. 5-fold stratified cross-validation). The results demonstrate that 5-fold stratified cross-validation produces the most stable performance estimates with the lowest variance (Accuracy: 97.4%±0.3%, AUC: 0.993±0.002), whereas random partition without stratification yields significantly inferior results (Accuracy: 95.1%, AUC: 0.973). Among the holdout scenarios, the 70/15/15 stratified ratio achieved the best performance (Accuracy: 97.2%, AUC: 0.991). Statistical analysis confirms significant differences between stratified and non-stratified scenarios (p < 0.05). These findings provide empirical guidance for researchers in designing more valid and replicable machine learning experiments in the medical domain.

Majalah Ilmiah METHODA

Transformasi Digital dalam Pengelolaan Aset Pemerintah pada Badan Keuangan dan Aset Daerah Kabupaten Takalar

Optimalisasi Splitting Data untuk Kinerja Robust Model EfficientNetV2-B0 pada Deteksi Pneumonia