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Journal : Dinamik

Prioritizing Pregnant Mother at Risk of Stunting: An Analytic Network Process Approach Cahyono, Taufiq Dwi; Hadikurniawati, Wiwien
Dinamik Vol 29 No 2 (2024)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v29i2.9889

Abstract

Stunting occurs due to malnutrition which inhibits growth in toddlers. Stunting can also be caused by problems during pregnancy. This study aims to identify the risk of stunting during pregnancy and determine pregnant women who are at risk of this condition. By identifying and prioritizing critical factors that contribute to stunting in children under five, this research is expected to assist policy makers in developing effective solutions to reduce stunting rates. Handling the problem of stunting is important for the Government because it relates to the future generation of Golden Indonesia 2045. This study evaluates appropriate actions or therapies to reduce the risk of having children born with the potential to experience stunting. In the process of selecting pregnant women who are at risk of giving birth to children with the risk of stunting, a selection procedure is carried out that considers several factors such as the mother's age, mother's nutritional intake, arm circumference, hemoglobin level, parity, birth spacing, height, and mother's body mass index (BMI). The analytic network process (ANP) approach is used to determine the outcome of the selection process. The ranking is determined based on the calculation of the weighting of the criteria and sub-criteria in the ANP method. Based on the results of calculations using the ANP approach, PM 1 pregnant women get the highest score and are ranked first. These pregnant women are considered to have the highest risk of giving birth to babies with stunting risk.
Pendekatan Graph-Based Community Detection dalam Social Network Analysis Jaringan Undang-Undang Republik Indonesia 2014-2024 Wibisono, Setyawan; Wahyudi, Eko Nur; Hadikurniawati, Wiwien; Lestariningsih, Endang; Cahyono, Taufik Dwi
Dinamik Vol 30 No 2 (2025)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v30i2.10218

Abstract

This study evaluates the performance of three community detection algorithms—Leiden, Infomap, and Label Propagation—on the legal network of the Republic of Indonesia spanning the period 2014–2024. The network consists of 679 nodes and 2,295 edges, constructed based on citation relationships among regulations. The evaluation employs four network topology metrics: modularity, coverage, conductance, and inter-cluster density. Results show that the Leiden algorithm achieves the highest modularity score (0.522991), indicating the formation of communities with strong internal density. Additionally, it yields the lowest conductance value (0.302455), suggesting relatively well-isolated communities. In contrast, the Label Propagation algorithm produces the highest coverage (0.835294) and inter-cluster density (0.542331), but with a lower modularity (0.431583), reflecting the formation of large communities with less distinct boundaries. Infomap exhibits moderate performance, with a modularity score of 0.508406 and inter-cluster density of 0.420803, yet records a relatively high conductance (0.410409). Network visualizations reveal three major communities for each algorithm, representing thematic clusters such as institutional governance, constitutional law, and public finance. Overall, the Leiden algorithm is considered the most optimal for detecting modular, stable, and thematically coherent community structures within the complex and interrelated network of Indonesian laws.
Analisis Deteksi Komunitas Louvain, Infomap, dan Walktrap pada Konstruksi Social Network Analysis Jaringan Undang-Undang Republik Indonesia 2014-2024 Al Amin, Imam Husni; Wibisono, Setyawan; Hadikurniawati, Wiwien; Lestariningsih, Endang; Eniyati, Sri
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10308

Abstract

Penelitian ini mengevaluasi performa tiga algoritma deteksi komunitas Louvain, Infomap, dan Walktrap dalam konteks social network analysis pada jaringan undang-undang Republik Indonesia periode 2014–2024. Jaringan dibangun dari hubungan kutipan antar undang-undang Republik Indonesia pada rentang waktu antara tahun 2014 sampai dengan tahun 2024. Kutipan antar undang-undang diperoleh pada bagian “Mengingat” pada setiap undang-undang, menghasilkan sebuah konstruksi struktur graf berarah dan tak berbobot. Setiap algoritma diuji berdasarkan empat metrik evaluasi: modularity, coverage, conductance, dan inter-cluster density. Evaluasi terhadap tiga algoritma deteksi komunitas Infomap, Louvain, dan Walktrap pada jaringan undang-undang menunjukkan perbedaan karakteristik dalam membentuk struktur komunitas. Louvain unggul dalam hal modularity (0.522387) dan conductance (0.287157), yang mencerminkan kemampuan optimal dalam memisahkan komunitas besar yang kohesif dan minim koneksi keluar. Infomap menempati posisi menengah dengan modularity dan inter-cluster density yang cukup baik, menawarkan keseimbangan antara segmentasi dan kepadatan komunitas. Walktrap memiliki keunggulan pada coverage (0.809586) dan inter-cluster density (0.50640), menandakan kemampuannya membentuk komunitas kecil yang sangat padat secara internal, meskipun cenderung kurang terstruktur secara global karena modularity-nya paling rendah (0.464787). Dengan demikian, Louvain direkomendasikan sebagai algoritma paling sesuai untuk analisis jaringan undang-undang, terutama jika tujuan utama adalah memperoleh segmentasi komunitas yang terstruktur kuat dan representatif secara makro terhadap arsitektur hukum nasional.