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All Journal Bulletin of Electrical Engineering and Informatics JUSIFO : Jurnal Sistem Informasi Proceeding SENDI_U Jurnal Manajemen Kesehatan Indonesia AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Jurnal Rekam Medis dan Informasi Kesehatan Media Ilmu Kesehatan Humaniora Kumawula: Jurnal Pengabdian Kepada Masyarakat Journal of Telenursing (JOTING) Jurnal Abdi Insani Journal of Community Development and Disaster Management Analisa: Journal of Social Science and Religion ITIJ Jurnal Biostatistik, Kependudukan dan Informatika Kesehatan (BIKFOKES)
Retnowati Retnowati
Mahasiswa Program Studi Teknologi Pendidikan Pascasarjana UNS
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MFEP-Based Evaluation of Public Information Governance with Socio-Technical and Data Lifecycle Perspectives Retnowati, Retnowati; Eko Nur Wahyudi; Purwatiningtyas
Information Technology International Journal Vol. 3 No. 1 (2025): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v3i1.52

Abstract

In the era of public information disclosure, public information management is essential for transparency, accountability, and citizen participation. This study assesses eight Public Information Management Officers (PPID) in Central Java, Indonesia, utilizing 21 variables measured by a Multi-Factor Evaluation Process (MFEP) based on Data Lifecycle Management (DLM) and Socio-Technical Systems (STS). Data was collected using Likert-scale questionnaires, and performance was assessed using Evaluation Weight Value (EWV) and Total Evaluation Weight (TEW). The results suggest that technology-related indicators outperformed human competency and regulatory compliance. "Use" and "Disposal" were the DLM phases with the worst performance, demonstrating deficiencies in data accountability and infrastructure. In STS, the "People" dimension lagged, emphasizing the need for capacity building. The findings indicate that, while digital infrastructure is robust, governance and human resource development require strengthening. Strategic improvements in underperforming sectors are suggested to strengthen public information governance.
Enhancing skin cancer detection using transfer learning and AdaBoost: a deep learning approach Listiyono, Hersatoto; Retnowati, Retnowati; Purwatiningtyas, Purwatiningtyas; Nur Wahyudi, Eko; Maskur, Ali
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10379

Abstract

Skin cancer is one of the most prevalent types of cancer worldwide, with early detection playing a critical role in improving patient outcomes. In this study, we propose a deep learning model based on LeNet-7 combined with adaptive boosting (AdaBoost) to classify skin lesions as either benign or malignant using the International Skin Imaging Collaboration (ISIC) dataset. We evaluate the proposed model alongside other well-established deep learning architectures, such as residual network (ResNet), VGGNet, and the traditional LeNet model, through various performance metrics including precision, recall, F1-score, specificity, Matthew’s correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and testing accuracy. Our results demonstrate that the proposed model (LeNet-7+AdaBoost) significantly outperforms the other models, achieving a testing accuracy of 91.3%, precision of 0.92, recall of 0.91, and AUC-ROC of 0.93. The model successfully addresses issues of overfitting and generalization, providing a robust solution for skin cancer classification. However, some misclassifications of visually similar benign and malignant lesions highlight areas for future improvement. The proposed model shows promise in real-world medical applications and paves the way for further research into optimizing deep learning models for skin cancer detection.
Co-Authors Aghram, Nauval Agung Waluyo Akun Akun Ali Maskur Anak Agung Istri Sri Wiadnyani Anggri Noorana Zahra Anisi, Yunus Artin Bayu Mukti Atik Mawarni Dewi Handayani Untari Ningsih Efriadi, Yedi Eko Nur Wahyudi Eliyah, Eliyah Endang Ernawati Hardiyanti, Widhian Harsono, Yemima Sontiara Heribertus Yulianton Hersatoto Listiyono Ilyas, Arief Azhari Kafrawi, Aulia Putri Kemal N. Siregar Novita Mariana Nuridzin, Dion Zein Nurizin, Dion Zein Permatasari, Anggraeni Purba, Sariaman Purwatiningtyas Purwatiningtyas Purwatiningtyas Retno Mawarti, Retno Rina Candra Noor Santi Sariyun Naja Anwar Sri Eniyati Sri Mulyani Witono, A.B.M. Yati, Dwi Yunus Anis, Yunus