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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang 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 MATHunesa: Jurnal Ilmiah Matematika 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
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Implementation of Fuzzy Time Series Markov Chain Method using Kernel Smoothing in forecasting the Stock Price of PT. Elnusa Tbk. Mokodompit, Marcela; Nasib, Salmun K; Djakaria, Ismail; Yahya, Nisky Imansyah; Hasan, Isran K.
Indonesian Journal of Computational and Applied Mathematics Vol. 1 No. 1: February 2025
Publisher : Gammarise Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64182/indocam.v1i1.9

Abstract

This research aims to apply the Fuzzy Time Series Markov Chain combined with Kernel Smoothing in forecasting stock prices. The Kernel Smoothing technique is used to smooth stock data before the fuzzification process, resulting in more accurate predictions. The research stages include Data Smoothing, Fuzzy interval formation, Fuzzy Logical Relationship and Fuzzy Logical Relationship Group formation, and forecasting using Markov Chain Transition Matrix. Evaluation using MAPE shows a low prediction error rate, with a value of 0.005974257%, so this method is effective for volatile stock data. The implementation of this model is expected to be a reference for investors and analysts in understanding and predicting future stock price movements.
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.
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.
APPLICATION OF BINARY LOGISTICS REGRESSION AND RANDOM FOREST TO CIGARETTE CONSUMPTION EXPENDITURE IN GORONTALO REGENCY 2022 Mohamad Taufik Hamani; Dewi Rahmawaty Isa; Salmun K. Nasib; Hasan S. Panigoro; Isran K. Hasan; Nisky Imansyah Yahya
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.14-22

Abstract

The goal of this research is to predict or identify an object's class using its available attributes through classification. The aim of this research is to use the random forest method to develop a classification model and the binary logistic regression method to discover significant determinants in cigarette consumption expenditure in Gorontalo Regency. The findings indicated that the size of the home, the number of family members, and the head of the household's educational attainment all had a considerable impact. Only the household head's educational attainment, however, consistently influences the model and satisfies the goodness of fit requirements. In contrast, the random forest model outperformed binary logistic regression in the classification analysis when classification characteristics including accuracy, precision, recall, and f1-score were assessed. Consequently, random forest was found to be the most effective classification model in this investigation.
Model Regresi Multilevel Negative Binomial Pada Kasus Kronis Filariasis di Indonesia Rizal Usman; Salmun K. Nasib; Djihad Wungguli; Siti Nurmardia Abdussamad
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. 
Determination of Premium Price for Rice Crop Insurance in Gorontalo Province Based on Rainfall Index with Black Scholes Method Ana Nadiyyah; Emli Rahmi; Salmun K. Nasib; Agusyarif Rezka Nuha; Nisky Imansyah Yahya; La Ode Nashar
Pattimura International Journal of Mathematics (PIJMath) Vol 3 No 2 (2024): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol3iss2pp51-62

Abstract

With its complex topography, Gorontalo Province experiences significant rainfall variations that impact the agricultural sector, particularly rice crops. These variations can cause substantial losses for farmers. One way to address uncertain probabilities caused by rainfall is through agricultural insurance. This research aims to calculate the value of agricultural insurance premiums based on the rainfall index. The Black- Scholes method is used to calculate the premiums, while the Burn Analysis method is employed to determine the rainfall index. The research results classify the rainfall index values in Gorontalo Province into 7 (seven) percentiles. The lowest is at the 20th percentile, with 17.37 mm and a premium value of IDR 1,574,190, while the highest is at the 80th percentile, with 17.65 mm and a premium value of IDR 2,154,574. This indicates that the higher the rainfall, the greater the premium to be paid.
Wind Speed Category Characteristics in Bone Bolango Regency: A Markov Chain Approach Using the Beaufort Scale and Metropolis-Hastings Algorithm Saiful Pomahiya; Nurwan Nurwan; Nisky Imansyah Yahya; Salmun K. Nasib; Isran K. Hasan; Asriadi Asriadi
Pattimura International Journal of Mathematics (PIJMath) Vol 3 No 2 (2024): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol3iss2pp63-68

Abstract

This study models daily wind speed transitions in the Bone Bolango Regency using the Markov Chain Monte Carlo (MCMC) method and the Metropolis-Hastings algorithm, employing the Beaufort scale for wind speed classification. The research aims to predict the steady-state distribution of wind speeds and evaluate their temporal stability. Daily wind speed data from 2023, provided by the Meteorology, Climatology, and Geophysics Agency (BMKG), were categorized into three levels: calm, light breeze, and fresh breeze, based on the Beaufort scale. Transition probabilities were estimated using the Beta distribution, and simulations via the Metropolis-Hastings algorithm yielded the steady-state distribution. Results show a significant tendency for transitions from calm and light breeze categories to fresh breezes, with varying probabilities. Notably, calm conditions exhibit a 69% likelihood of transitioning to a light breeze. This research contributes to improving wind speed prediction models by integrating statistical algorithms with meteorological classifications. The findings have implications for enhancing short-term weather forecasts and developing predictive systems for regions with similar weather patterns.
Co-Authors Abas Kaluku Abdul Wahab Abdullah Adam, Dwi Putri Juniar Adinda Pratiwi Musa Afifah Farhanah Akadji Agusyarif Rezka Nuha Ainun Sukmawati Al Idrus Akadji, Afifah Farhanah Al Idrus, Ainun Sukmawati Amanda Adityaningrum Ana Nadiyyah Armayani Arsal Asriadi Asriadi Asriadi Asriadi Asriadi Asriadi Biduli, Meiske Cindy Aisa Putri Noor Dewi Rahmawaty Isa Djahara A. Nusi Djihad Wungguli Fadilah Istiqomah Pammus Fatmawati, Ainun Fitria Djafar Fuji Fauzia Kiayi Gani, Friansyah Hasan S. Panigoro Hasan, Riyanto Hinelo, Ikrar Prasetyo I Wayan Can Aryasandi Imran, Nurain Indrawati Lihawa Isa, Jefri N. Ismail Djakaria Isran K Hasan Isran K. Hasan Isran K. Hasan Jusuf, Anryan Karmila Mokoginta Kasim, Afrianto Pratama La Ode Nashar Lailany Yahya Lakisa, Narti Macmud, Tedy Madonsa, Muhammad Rifai Mahmud, Sri Lestari Mahrifat Ismail Marukai, Nur Amalia Meitasya wolah Mohamad Taufik Hamani Mokodompit, Marcela Muhammad Rezky F. Payu Muhammad Rifai Katili Nathania Oktavia Gunawan Naue, Siti Nurmeylisya Ni Wayan Tiarawati Nikmatisni Arsad Ningsih, Setia Nisky Imansyah Yahya Nisky Imansyah Yahya Nisky Imansyah Yahya Nisky Imansyah Yahya NISKY IMANSYAH YAHYA Nisky Imansyah Yahya Novarianti Firdaus Nur Dhea Wahab Nurwan Nurwan Nurwan Nurwan, Nurwan Nur’ain Manoppo Putri Ayuningtias Mahdang Rahmi, Emli Rakhmat Jaya Lahay Rauf, Dewi Nur Angriani Resmawan Resmawan Rizal Usman Saiful Pomahiya Sembiring, Rinawati Sigar, Tesya Siti Nurmardia Abdussamad Stella Junus Sumarno Ismail Sumarno Ismail Syahrizal Koem Taha, Dennynatalis Trieke Nurfadilah Harun Yanuari, Eka Dicky D Zian Bula