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All Journal bit-Tech Journal of Information Systems and Technology Research Jurnal Aplikasi Sains Data
Muhammad Nasrudin
UPN Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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Spatial Clustering of Dengue Hemorrhagic Fever Risk Areas in Lamongan Regency Using the SKATER Method Cahya Eka Melati; Muhammad Nasrudin; Mohammad Idhom
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1580

Abstract

Dengue Hemorrhagic Fever (DHF) remains a major public health problem in Lamongan Regency, Indonesia, with unequal distribution across sub-districts. This study aims to identify and classify DHF-prone areas using Spatial ‘K’luster Analysis by Tree Edge Removal (SKATER), a graph-based spatial clustering method. The study used cross-sectional secondary data at the sub-district level, including DHF Incidence Rate (IR), population density, rainfall, and percentage of adequate sanitation. Spatial autocorrelation was analyzed using Moran’s Index, followed by weighted graph construction and Minimum Spanning Tree (MST) partitioning for cluster formation. Cluster quality was evaluated using the Sum of Squared Deviations (SSD) and Between-Cluster Sum of Squares (BSS). The Moran’s I results showed significant spatial autocorrelation for all variables (p < 0.05). The five-cluster configuration produced better clustering performance, with lower SSD (49.84) and higher BSS (58.16) compared to the three-cluster configuration (SSD = 86.18; BSS = 21.82). The results revealed spatial variations in DHF vulnerability, ranging from very low to very high categories. These findings indicate that the SKATER method effectively identifies geographically contiguous and homogeneous DHF-prone areas to support spatially targeted DHF control planning in Lamongan Regency
Spatial Modeling of Factors Determining Active Family Planning Participation in East Java: A Geographically Weighted Regression and Elastic Net Approach Ardiana Fatma Dewi; I Nyoman Kresna Wira Yudha; Muhammad Nasrudin
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (2026): Journal of Data Science Applications.
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v2i1.58

Abstract

This research identifies regional variations and determinants of active family planning (KB) participation in East Java using spatial modeling. The study utilizes data from the 2024 Family Information System (SIGA) of BKKBN East Java. To address multicollinearity and high-dimensional data, the Elastic Net method—combining Ridge and Lasso penalties—was employed for variable selection, retaining 6 out of 10 initial variables. Global modeling through Ordinary Least Squares (OLS) showed an Adjusted of 0.668. However, a Moran’s I test on the residuals revealed significant spatial autocorrelation (Z-score = 2.5677, p = 0.0102), justifying the use of Geographically Weighted Regression (GWR). The GWR model, using a Fixed Gaussian kernel with a bandwidth of 103.63, improved performance with an Adjusted of 0.7348. The results demonstrate spatial heterogeneity, where factors such as unmet need, households with children, and welfare levels have varying impacts across different districts. This spatial visualization helps identify priority areas for strategic resource allocation to enhance KB program efficiency
Implementation of Multiplex Leiden Algorithm for Clustering Ancol Visitors Ajeng Puspa Wardani; Trimono Trimono; Muhammad Nasrudin
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3608

Abstract

Ancol is the largest recreational destinations, attracting visitors from diverse backgrounds. However, in 2024 the company experienced an 11.96% decline in visitor numbers. This condition highlights the urgent need for more accurate customer segmentation to support targeted and effective marketing strategies. Accordingly, this study investigates whether a Multiplex Leiden can produce coherent visitor segments, while also examining the relative contribution of each layer to community formation. Unlike prior multilayer segmentation studies, this study leverages the Multiplex Leiden algorithm, which guarantees well-connected communities and has been shown to achieve higher modularity. This is among the first applications of Multiplex Leiden for visitor segmentation, offering improved community coherence and interpretability in a multi-layer behavioral network. To balance network structures and reduce cross-layer density bias, kNN backbone preprocessing was applied before community detection. The results reveal 18 distinct visitor communities with substantial variation in size. Layer-wise quality analysis shows that the socioeconomic status layer contributes the strongest influence on the detected communities, followed by spending behavior and experiential preferences. The clustering quality was evaluated using multiple metrics. An Adjusted Rand Index (ARI) of 0.617 indicates a stable, non-random visitor segmentation, while a positive total quality score of 1.086 reflects strong cross-layer community structure. A mean conductance value of 0.548 suggests moderately well-separated yet realistically overlapping communities. Overall, the findings empirically confirm the effectiveness and interpretability of the Multiplex Leiden algorithm with backbone preprocessing for visitor segmentation in multi-layer networks. Future research may extend this framework by incorporating additional behavioral or temporal data.
Co-Authors Ajeng Puspa Wardani Ardiana Fatma Dewi Cahya Eka Melati I Nyoman Kresna Wira Yudha Mohammad Idhom Trimono Trimono