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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Wibowo, Sandi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Development of Lung Cancer Risk Screening Tool with Causal Discovery Model Evaluation Approach Wibowo, Sandi; Mutaqin, Jatniko Nur; Apriansyah, Ari; Komiyatu, Muhamad; Soekidjo, Gusti Ayu Putri Saptawati
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2188

Abstract

Causal graph discovery approaches in healthcare for detecting high-risk diseases have been more widely applied in the last decade. The main challenge in causal graph discovery in healthcare data is the complexity of big data, which requires appropriate algorithms to reveal causal relationships between variables. This study focuses on evaluating the performance of seven causal discovery models—Peter-Clark (PC), Greedy Equivalent Search (GES), Direct LiNGAM, Directed Acyclic Graph-Graph Neural Network (DAG-GNN), Greedy Sparsest Permutation (GraSP), and Recursive Causal Discovery (RCD)—on opensource healthcare datasets. The model performance was evaluated using the Structural Intervention Distance (SID), Structural Hamming Distance (SHD), Matthews Correlation Coefficient (MCC), and Fobernius Norm (FN) metrics. The evaluation results conclusively show that the GES model performs best on low-complexity datasets. Meanwhile, the DAG-GNN model offers consistent performance on high-complexity data with MCC values ranging from 0.77 to 0.88. The application of the GES model for lung cancer risk screening, based on user question responses, demonstrated effectiveness by measuring MCC, SID, and SHD scores between the reference adjacency metrics and the resulting screening metrics.
Perancangan Framework Tata Kelola Data Sebagai Strategi Peningkatan Kualitas Informasi Penyuluhan di Sektor Kelautan dan Perikanan Wibowo, Sandi; Karim, Abdullah; Herawati, Neng Ayu; Yulianty, Lenny Putri; Surendro, Kridanto
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.132

Abstract

Tata kelola data yang efektif menjadi fondasi utama dalam menjamin kualitas informasi, terutama di sektor kelautan dan perikanan yang memiliki distribusi aktor dan operasi secara geografis luas. Ketersediaan data yang akurat, dapat dilacak, dan relevan sangat penting untuk mendukung pengambilan keputusan berbasis bukti, khususnya dalam kegiatan penyuluhan yang membina lebih dari 48.000 kelompok di seluruh Indonesia. Penelitian ini bertujuan merancang Data Governance Framework penyuluhan di sektor kelautan dan perikanan guna meningkatkan kualitas data penyuluhan. Pendekatan yang digunakan adalah Design and Development Research (DDR), dengan landasan pada prinsip tata kelola data oleh Ladley serta model evaluasi kapabilitas organisasi Capability Maturity Model Integration (CMMI). Framework yang dikembangkan mencakup struktur peran formal (Data Owner, Data Steward, Data Custodian), kebijakan akses berbasis peran, validasi data, manajemen metadata, serta glosarium untuk menjamin konsistensi dan keterlacakan data. Selain itu, penelitian ini menghasilkan Minimum Sustainable Operating Model (MSOM) sebagai strategi implementasi bertahap yang realistis di tengah keterbatasan sumber daya organisasi. Hasil validasi menunjukkan bahwa framework ini tidak hanya konsisten dengan literatur dan prinsip tata kelola data publik, tetapi juga relevan dengan regulasi nasional seperti Undang-Undang Perlindungan Data Pribadi. Framework ini diharapkan dapat meningkatkan akurasi, efisiensi, dan akuntabilitas data penyuluhan, sekaligus memperkuat pengambilan keputusan strategis berbasis data di sektor publik.   Abstract Effective data governance is a fundamental foundation for ensuring information quality, particularly in the marine and fisheries sector, which involves geographically dispersed actors and operations. The availability of accurate, traceable, and relevant data is essential to support evidence-based decision-making, especially in extension activities that serve over 48,000 community groups across Indonesia. This study aims to design a Data Governance Framework for extension services in the marine and fisheries sector to improve data quality. The research adopts a Design and Development Research (DDR) approach, grounded in Ladley’s data governance principles and the Capability Maturity Model Integration (CMMI) for evaluating organizational capabilities. The proposed framework includes formal role structures (Data Owner, Data Steward, Data Custodian), role-based access policies, data validation mechanisms, metadata management, and a glossary to ensure data consistency and traceability. Additionally, the study introduces a Minimum Sustainable Operating Model (MSOM) as a phased implementation strategy, suitable for organizations with limited resources. Validation results indicate that the framework aligns with existing literature and public data governance principles, while also complying with national regulations such as the Personal Data Protection Law. This framework is expected to enhance the accuracy, efficiency, and accountability of extension data, while strengthening strategic data-driven decision-making in the public sector.
Co-Authors ABDULLAH KARIM Ari Apriansyah, Ari Gusti Ayu Putri Saptawati Komiyatu, Muhamad Kridanto Surendro Mutaqin, Jatniko Nur Neng Ayu Herawati Yulianty, Lenny Putri