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STRUCTURAL EQUATION MODELING-GENERALIZED STRUCTURED COMPONENT ANALYSIS TO ANALIZING STRUCTURE OF POVERTY IN INDONESIA IN 2022 Marukai, Nur Amalia; Wungguli, Djihad; Nashar, La Ode; Nasib, Salmun K.; Asriadi, Asriadi; Abdussamad, Siti Nurmardia
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page167-174

Abstract

Structural Equation Modeling - Generalized Structured Component Analysis (SEM-GSCA) is a component-based method suitable for limited sample sizes. GSCA is appropriate for structural models that include variables with reflective and formative indicators. This study utilizes the Alternating Least Square (ALS) parameter estimation. Iterations in ALS are used to achieve minimal residuals. Additionally, this study employs jackknife resampling to obtain standard error estimates. This study aims to identify the poverty model structure in Indonesia and examine the relationships among poverty, human resources, economic, and health variables. The results of the structural model of poverty in Indonesia are explained as follows: the influence of human resources and economic variables on poverty is insignificant, while the health variable significantly negatively influences poverty. Furthermore, the health variable significantly influences human resources, and both human resources and health significantly influence the economy.
Model Regresi Multilevel Negative Binomial Pada Kasus Kronis Filariasis di Indonesia Usman, Rizal; Nasib, Salmun K.; Wungguli, Djihad; Abdussamad, Siti Nurmardia
Jambura Journal of Probability and Statistics Vol 6, No 2 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i2.31648

Abstract

Filariasis is a contagious disease caused by infection with the parasitic worm Filaria and transmitted through the bite of an infected mosquito. Analysis of the number of chronic filariasis cases in Indonesia often faces statistical problems in the form of overdispersion and excess zero. To overcome this, a Multilevel Negative Binomial Regression model is used which is able to handle data variance that is greater than the average as well as the number of zero values in the data. The results showed that the model was effective in overcoming overdispersion and excess zero problems. Based on the parameter significance test using the Wald test, environmental variables such as the presence of unprotected wells (X4) and household proximity to waste storage (X5) have a significant effect on the number of chronic filariasis cases. In contrast, socioeconomic variables such as percentage of male population (X1), productive age population (X2), proper sanitation (X3), percentage of poor population (X6), and Human Development Index (X7) did not show a significant effect. These findings confirm that environmental factors play an important role in the spread of chronic filariasis cases in Indonesia. 
Sifat Fundamental Pada Granum Eulerian Suaib A. Siraj; Asriadi; Djihad Wungguli; Hasan S. Panigoro; Nurwan; Nisky I. Yahya
Limits: Journal of Mathematics and Its Applications Vol. 21 No. 2 (2024): Limits: Journal of Mathematics and Its Applications Volume 21 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Mathematical analysis has several important connections with graph theory. Although initially, they may seem like two separate branches of mathematics, there are relationship between them in several aspects, such as graphs as mathematical objects that can be analyzed using concepts from analytic mathematics. In graph theory, one often studies distance, connectivity, and paths within a graph. These can be further analyzed using analytic mathematics, such as in the structure of natural numbers. Literature studies on graph theory, especially Eulerian graphs, are interesting to explore. An Eulerian path in a graph G is a path that includes every edge of graph G exactly once. An Eulerian path is called closed if it starts and ends at the same vertex. The concept of granum theory as a generalization of undirected graphs on number structures provides a rigorous approach to graph theory and demonstrates some fundamental properties of undirected graph generalization. The focus of this study is to introduce the connectivity properties of Eulerian granum. The granum G(e,M) is called connected if for every u,v E M with u != v there exists a path subgranumG^' (e,M^' )c G(e,M)  where u,v E M^' and is called an Eulerian granum if there exists a surjective mapping O: [||E(G(e,M))|| + 1]-> M such that e(o(n),o(n+1))=1 for every n E [||E(G(e,M))||]. This property provides a deeper understanding of the structure and characteristics of Eulerian granum, which have not been fully comprehended until now.
Comparison of OPTICS and HDBSCAN Performance in Clustering Population Administration Document Ownership in Bone Bolango Regency Adisti Dayo; Djihad Wungguli; Muhammad Rezky Friesta Payu
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/s6m22g74

Abstract

Population administration is essential for public service delivery and development planning; however, disparities in population document ownership across villages remain a challenge in Bone Bolango Regency. The heterogeneous nature of the data, the presence of outliers, and variations in density patterns limit the effectiveness of classical statistical approaches in capturing the underlying distribution. Therefore, this study aims to compare two density-based clustering algorithms, Ordering Points to Identify the Clustering Structure (OPTICS) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), in grouping villages based on population document ownership levels. The data were obtained from the Department of Population and Civil Registration of Bone Bolango Regency in 2024 and consist of ownership records of birth certificates, identity cards, and family cards from 165 villages. Both algorithms successfully formed two main clusters representing villages with relatively high and low levels of population document ownership. Internal validation results indicate that OPTICS outperformed HDBSCAN, achieving a Silhouette Coefficient of 0.827, a Davies–Bouldin Index of 0.242, and a Calinski–Harabasz Index of 1217.425, compared to 0.787, 1.210, and 767.866, respectively, for HDBSCAN. In conclusion, OPTICS demonstrates superior capability in producing a more coherent clustering structure for population document ownership data. Therefore, the clustering results obtained using OPTICS can serve as a supporting basis for formulating policies to promote equitable population administration services.
Pendekatan Hybrid VARIMA–ANN untuk Peramalan Multivariat Data Cuaca Bulanan di Provinsi Gorontalo Ali, Nur Anggraini T.; Wungguli, Djihad; Hasan, Isran K.
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.37513

Abstract

Multivariate time series forecasting is essential for understanding the interrelationships among weather parameters. This study aims to develop a multivariate forecasting model using a hybrid Vector Autoregressive Integrated Moving Average (VARIMA)–Artificial Neural Network (ANN) approach with the backpropagation algorithm, applied to weather data from Gorontalo Province over the 2015–2023 period, including air temperature, humidity, and wind speed. The data were divided into training data (2015–2021) and testing data (2022–2023). The VARIMA model was employed to capture the linear component, while the residuals from the VARIMA model were subsequently modeled using ANN to capture nonlinear patterns. The order of the VARIMA model was determined based on the smallest Akaike Information Criterion (AIC) value, while model performance was evaluated using Mean Absolute Percentage Error (MAPE). The results indicate that the best-performing model is VARIMA(5,1,1)–ANN(18,9,3), with MAPE values of 1.32% for air temperature, 20.54% for humidity, and 21.96% for wind speed. These findings suggest that the hybrid VARIMA–ANN approach provides good forecasting performance and has the potential to serve as an alternative method for multivariate weather forecasting.