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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Faktor Exacta BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Matematika UNAND Jurnal Varian Inferensi ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Aplikasinya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS MATHunesa: Jurnal Ilmiah Matematika PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Indonesian Journal of Statistics and Its Applications
Siswanto Siswanto
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, Universitas Hasanuddin, Indonesia
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Comparison of Negative Binomial Regression Model and Geographically Weighted Poisson Regression on Infant Mortality Rate in South Sulawesi Province Siswanto Siswanto; Edy Saputra R; Nurtiti Sunusi; Nirwan Ilyas
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p170-179

Abstract

The number of infant mortality cases is an important indicator to assess the quality of a country's public health. A number of studies argue that the case of infant mortality has a close relation to the living area condition and the social status of the parents. Indirectly, the quality of life of babies in a country will impact the nation's quality of life in general. Therefore, many efforts are required to reduce the infant mortality in Indonesia. One of the steps that could be done to overcome this issue is to analyze the causative factors. The statistical method that has been developed for data analysis taking into account current spatial factors is the Geographically Weighted Poisson Regression (GWPR) with a weighted Bisquare kernel function. Based on the partial estimation with the GWPR model, there are seven groups based on significant variables that affect the number of infant deaths in South Sulawesi Province. Of the seven groups formed, the first group is the Selayar Islands where all variables have a significant effect. This needs to be a concern for the South Sulawesi provincial government to improve facilities and infrastructure in the Selayar Islands, of course the location which is very far from the city center can affect access to drug reception, medical personnel and so on. Based on the results of the analysis of the factors that affect the number of infant deaths in South Sulawesi Province using a negative binomial regression approach and GWPR with a bisquare kernel weighting, it can be concluded that the GWPR model used is the best for analyzing the number of infant deaths in South Sulawesi Province because it has an AIC value. The smallest is 167.668.
Structural Equation Model Approach to National Health Insurance Participation in Disadvantaged Regions Nurul Rezki; Siswanto Siswanto; Musdalifah Musdalifah; Evaletrina Gracelita Marisda; Ismail Dwi Saputra
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i2p169-180

Abstract

Kartu Indonesia Sehat is Indonesia's national health insurance program, which is a right for everyone. However, the distribution of the program is not evenly distributed, especially in disadvantaged, frontier, and outermost regions. This research examines the factors influencing participation rates in Indonesia's national health insurance, the Healthy Indonesia Card program, in disadvantaged, frontier, and outermost regions of South Sulawesi. There are seven variables to estimate these factors, including education, employment, income, knowledge, motivation, socialization and trust. Based on descriptive statistics and a Structural Equation Model Partial Least Squares analysis using bootstrap parameters, there are three influencing factors: occupation, motivation, and trust, with a goodness of fit model of 32.3568%.
COMPARISON OF THE PERFORMANCE OF NAÏVE BAYES AND CORRELATED NAÏVE BAYES METHODS WITH THE APPLICATION OF SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE Radia Sultan; Siswanto; Andi Isna Yunita
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 02 (2025)
Publisher : Universitas Negeri Surabaya

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

Abstract

Classification is the process of creating a model to recognize patterns with the aim of mapping them into specific classes and predicting classes. Naive Bayes is a popular, simple and effective classification method with a probabilistic approach based on Bayes' Theorem. The assumption of independence in this method sometimes makes the classification performance decrease. Correlated naïve bayes corrects this assumption by considering attribute correlations, while SMOTE is used to overcome data imbalances. This approach is important in medical data analysis, one of which is predicting ischemic heart disease. This study aims to compare the performance of Naïve Bayes and Correlated Naïve Bayes methods in the classification of ischemic heart disease, with the application of SMOTE to overcome data imbalance. The analysis was carried out using ischemic heart disease data at the Integrated Heart Center of Dr. Wahidin Sudirohusodo Hospital, Makassar City, for the period of July 2021 to July 2022. Naïve Bayes managed to classify 66 data with 75% accuracy, 94% precision, and 62% sensitivity. Meanwhile, Correlated Naïve Bayes showed better performance by correctly classifying 77 data, resulting in 87.5% accuracy, 86% precision, and 94% sensitivity. These results show that Correlated Naïve Bayes has a superior performance in classifying ischemic heart disease.
BIVARIATE POISSON LOG-NORMAL REGRESSION MODELING ON THE NUMBER OF LEPROSY CASES IN INDONESIA Nasrah Sirajang; Salsabila Rahmadhani S; Siswanto Siswanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0493-0508

Abstract

Bivariate Poisson regression is a method for modeling two correlated count response variables. However, standard Poisson models often assume equidispersion, which is frequently violated in real-world data due to overdispersion. To address this issue, the Bivariate Poisson Log-Normal Regression (BPLNR) model is employed, which incorporates random effects to account for variability beyond that captured by the Poisson distribution. This study applies the BPLNR model to analyze the number of leprosy cases in Indonesia in 2021, categorized by the World Health Organization (WHO) into Paucibacillary (PB) and Multibacillary (MB). These two types are known to be correlated and exhibit overdispersion, rendering standard Bivariate Poisson models inadequate. This research contributes by applying BPLNR to leprosy data in Indonesia—an area that has been underexplored in prior studies, which largely employed univariate or standard Poisson approaches and ignored the correlation and overdispersion structure. Data were obtained from the 2021 Indonesian Health Profile and the Central Statistics Agency. Parameter estimation was conducted using Maximum Likelihood Estimation (MLE) with the Newton-Raphson algorithm, and hypothesis testing was performed using the Maximum Likelihood Ratio Test (MLRT). The results confirm that BPLNR effectively models the joint distribution of PB and MB cases while accounting for overdispersion. Key factors influencing both types of leprosy include population density, poverty rate, access to proper sanitation and drinking water, and availability of medical personnel and health facilities. A limitation of this study is the use of aggregate provincial-level data, which may obscure local variation and spatial effects. Future research could integrate spatial modeling techniques or individual-level data to enhance inference.
A MODIFIED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODELING ON OPEN UNEMPLOYMENT RATE IN SOUTH SULAWESI Siswanto Siswanto; Nurtiti Sunusi; Andi Isna Yunita; Muhammad Ridzky Davala; Andi M. Alfin Baso; Nurfadilah Nurfadilah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1099-1110

Abstract

The Open Unemployment Rate (OUR) in Indonesia is still a challenge despite a decline, namely 4.82% in February 2024 and around 7.2 million unemployed people. The main cause of the OUR is the imbalance between the number of the workforce and the availability of jobs. This issue is directly related to the Sustainable Development Goals (SDGs), especially Goal 8 which focuses on the creation of decent jobs and economic growth. South Sulawesi Province has experienced a spike in the OUR in the last five years, especially due to the Covid-19 pandemic which caused the poverty rate to decline to 6.31% in 2020. Along with economic recovery, this figure decreased to 4.19% in August 2024. Although low, the thickness of the layer remains a concern because 4 out of 100 people have not been absorbed in the labor market. Therefore, it is important to identify the factors that influence the OUR in South Sulawesi in order to design a reduction strategy. Various factors that influence the OUR include the human development index, percentage of poor people, average length of schooling, life expectancy, population density, and regional gross domestic product. To analyze the influence of these factors, this study uses the Geographically and Temporally Weighted Regression (GTWR) method which can capture spatial and temporal variations. Modifications are made using the Mahalanobis distance to consider inter-regional correlation and the Locally Compensated Ridge (LCR) approach to overcome high collinearity in the data. The data used comes from the Central Statistics Agency of South Sulawesi Province. Meanwhile, partial testing obtained each observation of the influencing factors varying from 2020 to 2023. In general, the factors that significantly influence the open poverty rate in South Sulawesi in 2020-2023 are the human development index, percentage of poor people, average length of schooling and life expectancy.
Modeling with Robust Kernel Nonparametric Regression on Childhood Stunting in Kalimantan Samsul Arifin; Selvi Annisa; Siswanto
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.9914

Abstract

This study models stunting prevalence across 56 regencies/cities in Kalimantan using robust kernel nonparametric regression. This approach addresses the nonlinear relationship between stunting and four predictors: access to improved sanitation, low birth weight, population density, and poverty rate. An examination of influential observations using DFFITS identified five regencies as outliers; thus, the robust MM-estimator approach was applied to mitigate the influence of these extreme observations on the estimation results. Optimal bandwidth selection was performed using the Cross-Validation (CV) method across several kernel functions, namely Epanechnikov, Gaussian, and Uniform. The results demonstrated that the Uniform kernel function yielded the smallest CV value with a bandwidth combination of h1=0.6, h2=0.2, h3=0.6, and h4=0.2. The Robust Uniform Kernel model delivered the best performance, with an MSE of 2.0556, RMSE of 1.4337, MAE of 0.6581, and of 0.9510. This study indicates that robust MM-estimator kernel nonparametric regression can produce stunting prevalence estimates that are more accurate, flexible, and stable in the presence of outliers.
PEMODELAN REGRESI NONPARAMETRIK DENGAN ESTIMATOR SPLINE POLYNOMIAL TRUNCATED PADA DATA JUMLAH WISATAWAN NUSANTARA Agym Nastiar Arman; Ryo Lemido; Siswanto Siswanto; Anisa Kalondeng
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 01 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n1.p127-133

Abstract

The nonparametric regression approach is a statistical method used to determine the relationship between predictor variables and the dependent variable when the assumed pattern is unknown. Truncated spline is an estimator used in nonparametric regression to handle data with varying behaviors. Nonparametric regression modeling with truncated polynomial spline was applied to local Indonesian tourist visitation data obtained from BPS for the years 2017-2019, for each month. The optimal knot points were selected based on the smallest Gross Cross Validation values. Based on the analysis, the optimal model is a second-order spline with the smallest Gross Cross Validation value of 17,95 and the optimal knot points are in the 2nd, 6th, and 7th months. The goodness of the model is evident from an value of 81,88% and an MSE of 12,46. The best model obtained shows a fairly accurate ability to explain the estimated number of domestic tourists so that it can be a basis for stakeholders to make key decisions in planning and managing the tourism industry as an effort to increase domestic tourism interest.
OPTIMASI METODE JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK PERAMALAN CURAH HUJAN BULANAN DI KOTA DENPASAR Fadia Nailah; Dwi Ina Larasati; Siswanto Siswanto; Anisa Kalondeng
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 01 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n1.p134-140

Abstract

Rainfall is a natural phenomenon that depends on many factors that are an important part of life on earth. The high intensity of rainfall can lead to disasters. Therefore, this study aims to forecast monthly rainfall. The data used was obtained from BMKG Bali Province, namely monthly rainfall data for Denpasar City from 2009 to 2019. The method used is backpropagation artificial neural network. The artificial neural network method is an information processing method inspired by the human nervous system. Optimal backpropagation network architecture is needed so that the prediction results have a low error rate, by optimizing the use of training data and test data taken from sample data. Based on the results of the testing and prediction process with the parameters of one hidden layer with 50 neorons, epoch 11 and learning rate 0.01, the results obtained with the MSE value in network testing are 0.037. So it can be concluded that the backpropagation artificial neural network method has good accuracy results used as a reference for decision making in predicting monthly rainfall in Denpasar City in the future.
Co-Authors A. Muh. Amil Siddik ADHIYAKSA PRANANDA Agus Hermawan Agym Nastiar Arman Alfiyah Salsa Dila Sabir Alya Safira Irtiqa Miolo Andi Atirah Arumtiwi Andi Isna Yunita Andi Isna Yunita Andi Kresna Jaya Andi M. Alfin Baso Anisa Anisa Anisa Kalondeng Anisa Kalondeng Anisa Kalondeng Anni Ivoni Parapa Arifin, Samsul Asma Auliarani Ayu Sofia Clarisa Eudia Chesynanda Dhita Hartanti Octavia Dwi Ina Larasati Edy Saputra R Edy Saputra Rusdi Evaletrina Gracelita Marisda Evelyn Priscilla Fadhil Al Anshory Fadia Nailah Fadilah Amirul Adhel Hedi Kuswanto Ika Inayah Ismail Dwi Saputra Iwan Kurniawan Junianto Sesa Kalondeng, Anisa Maria Yus Trinity Irsan Mirna Mirna Muhammad Ridzky Davala Muhammad Yusran Musdalifah Musdalifah Musdalifah Musdalifah Nasrah Sirajang Nirwan Ilyas Nirwan Nirwan Nurfadilah Nurfadilah NURTITI SUNUSI Nurtiti Sunusi Nurul Rezki Putri, Ayu Pratika Radia Sultan Rahmah, Syifa Ur Raupong Raupong Raupong Raupong Rezki, Nurul Rizka Pitri Ryo Lemido Salsabila Rahmadhani S Selvi Annisa Sri Astuti Thamrin Sri Astuti Thamrin Sri Astuti Thamrin Sri Astuti Thamrin Sri Mulyani Taufik Hidayat Thamrin, Sri Astuti Ummul Auliyah Syam Waode Sitti Amni Zakiyah Mar'ah Zulfanita Dien R