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All Journal Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Science and Technology Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Journal of Multidisciplinary Applied Natural Science Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi
Eni Sumarminingsih
brawijaya university
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Spatial Clustering with Autocorrelation-Based Weighting for Regional Socio-Economic Pattern Analysis: A Case Study of East Java Fitriani, Rahma; Sumarminingsih, Eni; Amaliana, Luthfatul
Journal of Multidisciplinary Applied Natural Science Vol. 6 No. 2 (2026): Journal of Multidisciplinary Applied Natural Science
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.345

Abstract

Clustering, an unsupervised machine learning technique, categorizes objects into groups based on shared characteristics. When applied to spatial data, the assumption of independence is often violated due to similarities among adjacent regions—a phenomenon known as spatial autocorrelation. To address this, spatial clustering incorporates both non-spatial attributes (e.g., socio-economic indicators) and spatial attributes (e.g., geographic location), with spatial attributes weighted based on their influence in defining clusters. In regional economic development, creating clusters that are both spatially coherent and socio-economically homogeneous is critical for effective policy design. Strong interactions among neighboring regions can promote more integrated and balanced growth. This study proposes a spatial clustering framework that optimizes spatial attribute weighting according to the degree of spatial autocorrelation. A simulation study using 2023 data from East Java’s 38 regencies/municipalities determines optimal weights under varying spatial dependence levels. The results show that optimal spatial weights increase with the number of clusters and vary according to the strength of spatial autocorrelation. Applied to East Java, the method produced clusters with higher socio-economic homogeneity than official zones, though with reduced spatial contiguity. These findings highlight the importance of adaptive, autocorrelation-aware clustering to improve regional planning and support more evidence-based development strategies.
Block Bootstrap for Spatiotemporal Data in Generalized Space Time Autoregressive (GSTAR) Sumarminingsih, Eni; Fitriani, Rahma; Darmanto; Maulana, Eka Dani; Aulia, Natasha; Ruszardi, Luzar Dwain
Science and Technology Indonesia Vol. 11 No. 2 (2026): April
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2026.11.2.701-731

Abstract

Generalized Space-Time Autoregressive is a model that can be used for data with spatial and temporal dependence. The GSTAR model is widely used in various phenomena such as rainfall, temperature, inflation, and others. GSTAR assumes normality of errors and non-autocorrelation. If the assumption of normality of errors is not met, then inference on parameters cannot be made. One solution to this problem is to use bootstrapping. However, bootstrapping for spatiotemporal data in the GSTAR model has not been developed. Therefore, this study aims to develop a bootstrapping method for spatiotemporal data in the GSTAR model. This development is done by adapting bootstrapping methods for time series data, namely, the non-overlapping block bootstrap (NBB) and the moving block bootstrap (MBB). This research continued with a series of simulations to evaluate the performance of the block bootstrap method as the number of observations, block length, and number of bootstrap replications were varied. Furthermore, the method’s effectiveness was tested using rainfall data from Malang Regency. Simulation results show that both resampling schemes satisfy the asymptotic condition, where the bias decreases monotonically with increasing sample size (T) and block length. MBB consistently produces lower bias than NBB due to its more intensive use of overlapping data, which effectively reduces boundary effects. Although inference on autoregressive parameters can be accurate, inference on spatial autoregressive parameters yields less satisfactory results, indicating the limitations of time blocks in capturing complex spatial dependencies. Increasing the number of replications above B=100 does not significantly improve the precision of the variance estimate, indicating computational efficiency at that threshold. The t-test results confirm that there is no statistically significant difference in performance between NBB and MBB. Nevertheless, MBB is more recommended for practical applications due to its higher information density and better estimation stability.
Spatial Data Science for Regional Pattern Analysis: Dynamic Time Warping-Based Clustering of East Java’s Economic Indicators Fitriani, Rahma; Sumarminingsih, Eni; Diartho, Herman Cahyo
Science and Technology Indonesia Vol. 11 No. 2 (2026): April
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2026.11.2.569-578

Abstract

Motivated by the need to better capture dynamic regional disparities, this study examines spatial and temporal development patterns in East Java, Indonesia, using spatial panel data from 2020 to 2023. A data-driven framework is proposed that integrates Principal Component Analysis (PCA) for dimensionality reduction, Dynamic TimeWarping (DTW) for temporal similarity measurement, and spatially constrained clustering using the SKATER algorithm. PCA compresses multiple socio-economic indicators, GDP growth, GDP level, Human Development Index (HDI), and population density, into a unified development profile, enabling comparison of regional trajectories over time. DTW captures non-linear temporal alignment, while SKATER preserves spatial coherence in cluster formation. The resulting clusters are used to construct an endogenous spatial weight matrix that reflects functional regional relationships rather than purely geographic adjacency. Validation using Moran’s I indicates stronger spatial autocorrelation compared to conventional contiguity-based weights, suggesting improved representation of spatial interaction. Four clusters reveal distinct development patterns and uneven regional trajectories. By integrating dimensionality reduction with temporal alignment and spatial clustering, the proposed approach extends dynamic spatial weighting toward a functional interpretation of regional dependence and offers a transferable framework for spatial data science and regional policy analysis.
Integration Sentiment Analysis and K-Means Clustering in Semeru Pine Forest Management Solimun; Sumarminingsih, Eni; Mudjiono
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 5 No. 2 (2026)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v5.i2.46926

Abstract

Forest-based tourism management requires integrated institutional and community involvement to ensure sustainability. Semeru Pine Forest in Sumberputih Village, Malang Regency, has significant tourism potential but has experienced a decline in management quality in recent years. This condition is reflected in rising public and visitor complaints about infrastructure, accessibility, and governance. This study aims to empirically examine patterns in public and visitor perceptions of the management of the Semeru Pine Forest. The research employs sentiment analysis of Google review data to extract key perceptual variables, followed by K-Means clustering. Survey data were collected from 200 respondents using a five-point Likert scale. The suitability of the data structure was assessed using Bartlett’s Sphericity Test prior to clustering. The sentiment analysis identified key variables including organizational management, community participation, and tourism sustainability. The K-Means analysis produced two distinct clusters representing different levels of management performance. The first cluster reflects lower perceptions of management quality, while the second cluster indicates relatively better management performance. These findings provide empirical evidence to support data-driven strategies for improving sustainable forest tourism management.
Co-Authors Achmad Efendi Ani Budi Astuti Asaliontin, Lisa Atiek Iriany Aulia, Natasha Darmanto Elfrida Ratnawati Fernandes, Adji Achmad Rinaldo Hamdan, Rosita Henny Pramoedyo Herman Cahyo Diartho Iriany , Atiek Jannah, Berliana Kalangi, Olyvia Maria Lusia, Dwi Ayu Luthfatul Amaliana, Luthfatul Maulana, Eka Dani Melanwati, Rinda Lolita Menufandu, Dahlia Gladiola Rurina Mudjiono Ni Wayan Surya Wardhani Nurdin, Muhammad Rafi Hasan Nurjannah Rahma Fitriani Rotchildi, Gusti Ayu Putu Rawi Ruszardi, Luzar Dwain Sa'diyah, Nur Kamilah Semathea, Karen Sepriadi, Hanifa Solimun Solimun, Solimun Sovia, Nabila Ayunda Suci Astutik Ullah, Muhammad Ohid Waego Hadi Nugroho Wahyuni, Djihan