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Graph-Theoretic Analysis of Weighted Neighborhood Graphs for Spatial Soil Nutrient Prediction Nurul Maulida Surbakti; Sisti Nadia Amalia; Muhammad Ashari; Riza Pahlwan
Journal of Mathematics, Computations and Statistics Vol. 9 No. 3 (2026): Volume 09 Issue 03 (September 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/fazqcb69

Abstract

Soil nutrient prediction is a spatial modeling problem because nutrient values observed at nearby locations may share common spatial structure. This study develops a graph-theoretic analysis of weighted neighborhood graphs for Graph Convolutional Network (GCN)-based prediction of synthetic nitrogen (N), phosphorus (P), and potassium (K) patterns. Ten independent Monte Carlo realizations of 100 sampling locations were generated from a uniform distribution on the unit square. For each realization, weighted k-nearest-neighbor (kNN) and epsilon-neighborhood graphs were constructed using an exponential distance-decay kernel. Graph structure was quantified using the number of edges, average degree, density, number of connected components, and algebraic connectivity. Separate transductive GCN regressors were trained for N, P, and K using  train-validation-test splits, target standardization based only on the training set, early stopping, and repeated random seeds. Binary GCN, multilayer perceptron (MLP), Random Forest, and inverse-distance weighting (IDW) were included as baselines. Among the tested graph configurations, kNN with  gave the lowest mean test error , whereas kNN with  produced a marginally higher mean . Weighted GCN improved numerically over binary GCN overall , with a significant paired improvement in MAE under a Wilcoxon test , although MLP remained slightly stronger overall . Across graph configurations, density was negatively associated with test  indicating that excessive neighborhood mixing can reduce predictive performance in these controlled settings. Nutrient-specific analysis showed that phosphorus was the most difficult target, while residual Moran’s I values were close to zero. Within the controlled synthetic scenarios, weighted neighborhood graphs provided a useful and interpretable representation for GCN-based spatial nutrient modeling. Future work should evaluate the framework on field measurements using spatial block validation and environmental covariates.
FRIEZE-BASED DIGITAL MOTIF GENERATION FOR MANDAILING ANGKOLA ULOS USING A MATLAB GRAPHICAL USER INTERFACE Kartika, Dinda; Niska, Debi Yandra; Nasution, Hamidah; Amalia, Sisti Nadia; Syahputra, Fikri
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 5 (2026): Volume 10, Nomor 5, October 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i5.58811

Abstract

This study develops a computational approach to generating digital motifs for Mandailing Angkola ulos based on seven frieze patterns. We examined two ceremonial textiles, Abit Godang and Parompa Sadum, to identify 16 basic motifs (nine and seven, respectively) based on their visual characteristics, documented philosophical meanings, and suitability for one-directional repetition. We developed a MATLAB-based graphical user interface (GUI) to support textile selection, motif cropping, visual adjustment, and frieze-based generation. Each basic motif was processed using all seven frieze types (F1–F7), which employ distinct combinations of translation, reflection, half-turn rotation, and glide reflection. The computational process generated 112 motif outputs, comprising 63 variations from Abit Godang and 49 from Parompa Sadum. The resulting designs exhibit different linear symmetry arrangements while retaining recognizable visual elements of their source motifs. Representative outputs generated using F7 illustrate how a single motif can be reorganized through repeated and reflected configurations. This study contributes a structured workflow integrating traditional motif documentation, geometric transformation, and digital design. The proposed approach supports further exploration of culturally informed motif variations for textile and decorative applications, while assessment of cultural acceptance and practical use remains a subject for future research.