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All Journal JMPM: Jurnal Matematika dan Pendidikan Matematika Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Dinamisia: Jurnal Pengabdian Kepada Masyarakat JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Jambura Journal of Mathematics Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications ILKOMNIKA: Journal of Computer Science and Applied Informatics InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Statistika dan Aplikasinya Jambura Journal of Mathematics Education Indonesian Journal of Applied Research (IJAR) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Jurnal Diferensial Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi International Journal of Health, Economics, and Social Sciences (IJHESS) Griya Journal of Mathematics Education and Application Unnes Journal of Mathematics Journal of Fundamental Mathematics and Applications (JFMA) Research in the Mathematical and Natural Sciences Research Review: Jurnal Ilmiah Multidisiplin Pattimura International Journal of Mathematics (PIJMath) Bulletin of Applied Mathematics and Mathematics Education Jurnal Riset Mahasiswa Matematika Jurnal Pengabdian Pada Masyarakat Indonesian Journal of Mathematics and Applications Hexagon: Jurnal Ilmu dan Pendidikan Matematika Journal of Mathematics, Computation and Statistics (JMATHCOS) System Information and Computer Technology (SYNCTECH) Indonesian Journal of Computational and Applied Mathematics
Salmun K. Nasib
Universitas Negeri Gorontalo
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Analysis of a Predator-Prey Model incorporating Prey Cannibalism and Intraspecific Competition on Predator Biduli, Meiske; Rahmi, Emli; Nasib, Salmun K.
Indonesian Journal of Computational and Applied Mathematics Vol. 1 No. 2: June 2025
Publisher : Gammarise Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64182/indocam.v1i2.33

Abstract

In this research, we formulated a predator-prey model by considering cannibalism in the prey and intraspecific competition on predator population. We found three types of equilibrium points existed under certain condition, except the extinction of all population equilibrium point. Further, we analyzed the local stability of each equilibrium point via linearization method. We found that the extinction of all population equilibrium point is always unstable and the other points locally asymptotically stable under some conditions. Finally, the numerical simulation carried out to verify the analytical results and to perform the impact of prey cannibalism rate.
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. 
Pengelompokkan Tindakan Kriminalitas di Indonesia dengan K-Medoids Menggunakan Algoritma Artificial Bee Colony Musa, Adinda Pratiwi; Achmad, Novianita; Nasib, Salmun K.
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 1 (2026): February
Publisher : Subaltren Inti Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aims to classify regional police forces in Indonesia based on the level and characteristics of criminal activity by applying the K-Medoids method optimized using the Artificial Bee Colony (ABC) algorithm. This grouping is intended to identify the dominant crime patterns in each region and evaluate the effectiveness of the methods used in producing representative clusters. The analysis results show that the K-Medoids-ABC method produces three main clusters, with the distribution of each consisting of 7 regional police departments in cluster 1, 5 regional police departments in cluster 2, and 21 regional police departments in cluster 3. Cluster validation using the Silhouette Index (SI) yielded a value of 0.387, indicating that the clustering results fall into the weak structure category, meaning the cluster structure is formed but with weak separation (Weak Separation). Cluster 1 shows a moderate and relatively even crime rate, Cluster 2 is dominated by crimes against life and crimes of fraud, embezzlement, and corruption, while Cluster 3 shows low values across all variables, with the lowest values for violent property crimes and drug-related crimes. This cluster reflects regions with relatively safe conditions, as evidenced by very low crime rates. These differences in characteristics between clusters reflect the diversity of factors causing crime in each region and have important implications for formulating more contextual and targeted crime prevention strategies.
Implementasi Algoritma Artificial Bee Colony dan Gravitational Search pada Fuzzy Geographically Weighted Clustering untuk Pemetaan Stunting di Sulawesi Tahun 2023 Djahara A. Nusi; Dewi Rahmawaty Isa; Salmun K. Nasib
Research Review: Jurnal Ilmiah Multidisiplin Vol. 4 No. 1 (2025): Research Review: Jurnal Ilmiah Multidisiplin (Februari 2025 - Juli 2025)
Publisher : Transbahasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54923/researchreview.v4i1.115

Abstract

Stunting is a chronic nutritional problem affecting child growth, particularly in regions with high prevalence, such as Sulawesi Island. This study aims to compare two optimization methods, Artificial Bee Colony (ABC) and Gravitational Search Algorithm (GSA), in the Fuzzy Geographically Weighted Clustering (FGWC) analysis to group regencies and cities based on factors contributing to stunting. The data used included health and socio-economic indicators from 66 regencies/cities in Sulawesi Island. Three validity indices—Classification Entropy (CE), Separation Index (SI), and Xie and Beni’s Index (XB)—were employed to assess clustering performance. The findings indicate that the FGWC-ABC method outperformed FGWC-GSA, yielding lower CE and XB values and a higher SI value, signifying better clustering results. The FGWC-ABC method, at a fuzziness value of m = 1.5, formed two clusters: Cluster 1, comprising 49 regencies/cities with relatively lower stunting prevalence and better socio-economic conditions, and Cluster 2, consisting of 17 regencies/cities with higher stunting prevalence and poorer socio-economic conditions. This study highlights the potential of FGWC-ABC in optimizing regional classification for targeted interventions in addressing stunting. The results provide a valuable reference for policymakers in designing effective strategies to mitigate stunting issues.
Analisis Regresi Logistik Multinomial untuk Menentukan Faktor-Faktor yang Mempengaruhi Jenis Penyakit pada Mahasiswa (Studi Kasus: Mahasiswa Program Studi Statistika, Jurusan Matematika, Universitas Negeri Gorontalo) Nathania Oktavia Gunawan; Salmun K. Nasib
Research Review: Jurnal Ilmiah Multidisiplin Vol. 4 No. 1 (2025): Research Review: Jurnal Ilmiah Multidisiplin (Februari 2025 - Juli 2025)
Publisher : Transbahasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54923/researchreview.v4i1.122

Abstract

Final-year students often experience illnesses due to numerous challenges, such as repeated revisions, difficulty finding references, and time constraints in completing their final assignments. Various factors contribute to the occurrence of illnesses among students. Multinomial logistic regression analysis is used to identify which factors significantly influence the types of illnesses in students. The data source used in this research was primary data, with a sample consisting of 83 active Statistics students at Universitas Negeri Gorontalo who met the inclusion criteria. The dependent variable was the type of illness, while the independent variables included age, gender, eating habits, sleeping patterns, and stress levels. The research instruments were validated using Pearson correlation analysis and tested for reliability using Cronbach's alpha statistical test. The results of the chi-square correlation test indicated that the stress level variable had a significant relationship (p-value = 0.03853) with the dependent variable. Based on the results of the multinomial logistic regression analysis, it was found that there is a 10.85% relationship between the stress level variable and the types of illnesses in students.
Perbandingan Jackknife Ridge Regression dan Principal Component Regression dalam Penanganan Kasus Multikolinearitas (Studi Kasus: Indeks Pembangunan Manusia di Indonesia) Nur’ain Manoppo; La Ode Nashar; Djihad Wungguli; Muhammad Rezky F. Payu; Siti Nurmardia Abdussamad; Salmun K. Nasib
Research Review: Jurnal Ilmiah Multidisiplin Vol. 4 No. 1 (2025): Research Review: Jurnal Ilmiah Multidisiplin (Februari 2025 - Juli 2025)
Publisher : Transbahasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54923/researchreview.v4i1.181

Abstract

According to data from Statistics Indonesia, the Human Development Index (HDI) in 2022 reached 72.91, increasing from 72.29 in the previous year. Although Indonesia’s HDI continues to improve, disparities remain among provinces, indicating that HDI distribution is still uneven. Given the importance of HDI in aregion, it is necessary to conduct statistical analysis to identify the factors that significantly influence HDI using regression analysis. In applying multiple linear regression, several classical statistical assumptions must be met, one of which is the central focus of this analysis-addressing the issue of multicollinearity. Several methods have been identified to address multicollinearity, including Jackknife Ridge Regreesion (JRR) and Principal Component Regression (PCR). This study aims to compare the effectiveness of both methods in handling multicollinearity based on Adjusted R2 and Mean Square Error (MSE) and to analyze the factors that significantly influence the HDI level in Indonesia. The data used in this study are secondary data comprising HDI and its related factors for each province in Indonesia in 2022, obtained from bps.go.id. Based on the analysis, the best model uses the JRR method, with an Adjusted R2 value of 96.7% and MSE of 0.033.
Co-Authors Abas Kaluku Abdul Wahab Abdullah Adam, Dwi Putri Juniar Afifah Farhanah Akadji Agusyarif Rezka Nuha Ainun Sukmawati Al Idrus Akadji, Afifah Farhanah Al Idrus, Ainun Sukmawati Armayani Arsal Asriadi Asriadi Asriadi Asriadi Biduli, Meiske Cindy Aisa Putri Noor Dewi Rahmawaty Isa Djafar, Fitria Djahara A. Nusi Djihad Wungguli Fadilah Istiqomah Pammus Fatmawati, Ainun Firdaus, Novarianti Gani, Friansyah Harun, Trieke Nurfadilah Hasan S. Panigoro Hasan, Riyanto Hinelo, Ikrar Prasetyo I Wayan Can Aryasandi Imran, Nurain Indrawati Lihawa Isa, Jefri N. Ismail Djakaria Isran K Hasan Jusuf, Anryan Kasim, Afrianto Pratama Kiayi, Fuji Fauzia La Ode Nashar Lailany Yahya Lakisa, Narti Macmud, Tedy Madonsa, Muhammad Rifai Mahmud, Sri Lestari Mahrifat Ismail Marukai, Nur Amalia Mokodompit, Marcela Muhammad Rezky F. Payu Muhammad Rifai Katili Musa, Adinda Pratiwi Nadiyyah, Ana Nathania Oktavia Gunawan Naue, Siti Nurmeylisya Nikmatisni Arsad Ningsih, Setia NISKY IMANSYAH YAHYA Nurwan Nurwan Nurwan Nurwan, Nurwan Nur’ain Manoppo Pomahiya, Saiful Putri Ayuningtias Mahdang Rahmi, Emli Rakhmat Jaya Lahay Rauf, Dewi Nur Angriani Resmawan Resmawan Sembiring, Rinawati Sigar, Tesya Siti Nurmardia Abdussamad Siti Nurmardia Abdussamad Stella Junus Sumarno Ismail Syahrizal Koem Taha, Dennynatalis Tiarawati, Ni Wayan Usman, Rizal Wahab, Nur Dhea wolah, Meitasya Yanuari, Eka Dicky D Zian Bula