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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275
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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
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Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.
Arjuna Subject : -
Articles 790 Documents
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ANALISIS KLASIFIKASI MENGGUNAKAN REGRESI LOGISTIK BINER DAN K-NEAREST NEIGHBOR PADA DATA IMBALANCE Eva Fitriyani; Tatik Widiharih; Bagus Arya Saputra
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.154-165

Abstract

Savings and Loan Cooperative or (KSP) is a cooperative that conducts its business activities only saving and borrowing. KSP members come from various different backgrounds so that they can affect their behavior in carrying out their obligations. To find out the status of current or bad customer payments, a classification process is carried out. The division of KSP customer data is carried out in the classification process into two, namely training data and test data. In the classification process, there are often cases of data imbalance, so it is necessary to handle data imbalance in training data with SMOTE and ADASYN. SMOTE and ADASYN were chosen because these methods handle imbalance data by generating data from minor classes so as not to eliminate important parts of the data. Classification was performed with Binary Logistic Regression and K-Nearest Neighbor. Binary Logistic Regression is a regression where the dependent variable is binary. While K-Nearest Neighbor is a grouping method based on the closeness of the distance of a data with other data as many as k nearest neighbors. The results of this study indicate that the ADASYN Binary Logistic Regression method is the best method that can classify and predict the payment status of KSP customers because it produces the highest accuracy and G-mean, namely the accuracy value of 70.67% and G-Mean 67.63%.
ANALISIS FAKTOR-FAKTOR YANG MEMENGARUHI DAYA KONSENTRASI BELAJAR MENGGUNAKAN EXTENDED COX REGRESSION Jessica Valenci Soegianto; Triastuti Wuryandari; Agus Rusgiyono
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.166-175

Abstract

Learning concentration plays a major role in the success of teaching and learning activities and is the main asset for students in receiving and mastering the subject matter presented. This study aims to determine the average endurance time of learning concentration power of students in grades 4-6 at SDN 02 Jenarwetan and the factors that influence it. The method used is Cox Extended because there are independent variables that do not meet the Proportional Hazard assumption. Parameter estimation uses the Maximum Partial Likelihood Estimation (MPLE) method with the Efron approach because there is data with co-occurrence. Based on the results of data analysis, the average endurance time of students' learning concentration power is 13.22 minutes. It is also known that the factors that influence the endurance of students' learning concentration power are the level of learning motivation and the level of stress experienced by students. Students with high learning motivation are able to maintain their learning concentration for a long period of time , while students with high stress levels are more at risk of losing learning concentration 4.6294 times higher than students with low stress levels.
DETERMINASI INDIKATOR PEMBANGUNAN KESEHATAN MASYARAKAT (IPKM) DI WILAYAH PESISIR MENGGUNAKAN MODEL STRUCTURAL DENGAN SAMPEL KECIL Riwi Dyah Pangesti; Dyah Setyo Rini; Winalia Agwil; Septiara Santi Anggriany; Muhammad Kevin Rido Ariendra
Jurnal Gaussian Vol 14, No 1 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.1.157-168

Abstract

Public health in coastal areas is a crucial aspect of a nation's development but faces unique challenges due to geographical, demographic, and environmental factors. This study seeks to analyze the factors influencing the Health Development Index (HDI) in coastal areas using the Structural Equation Modeling (SEM) approach with the Partial Least Square (PLS) method for a small sample. The analyzed variables include Environmental Health, Health Behavior, Health Services, Poverty Status, and the HDI, as well as their influence on Health Status. This study utilizes secondary data from the 2018 Riskesdas report and BPS publications in the southern part of Sumatra. The analysis results show that Environmental Health has a significant effect of -0,45 and Health Behavior has an effect of -0,30 on Health Status. However, Health Services, Poverty Status, and HDI do not show significant effects on Health Status. By gaining a deeper understanding of the determinants of IPKM in coastal areas, this study is expected to contribute to the development of more targeted and effective health policies. The PLS-SEM approach used in this study is also expected to serve as a reference for other researchers in applying structural models to small samples.
ESTIMASI PARAMETER DAN PENGUJIAN HIPOTESIS MODEL GTW LOG LOGISTIC 3-PARAMETER REGRESSION Nur Huda; Purhadi Purhadi; Tintrim Dwy Ary Widhianingsih
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.212-223

Abstract

The Geographically and Temporally Weighted Logistic Lindley Three-Parameter Regression (GTWLL3R) is a local regression model developed to analyze spatial-temporal heterogeneity using the flexibility of the three-parameter log-logistic distribution. Unlike global regression models that assume constant parameters across observations, GTWLL3R allows model parameters to vary across locations and time periods. This study aims to estimate the parameters of the GTWLL3R model using the Maximum Likelihood Estimation (MLE) approach. Since the log-likelihood function does not have a closed-form solution, parameter estimation is carried out numerically using the Newton–Raphson iterative algorithm. This study derives the likelihood function, parameter estimation procedure, variance–covariance matrix based on the observed information matrix, and statistical hypothesis testing model. The validity of statistical inference is established through the Hessian matrix, where the covariance matrix is obtained from the negative inverse Hessian matrix. Based on MLE theory, the parameter estimators are theoretically consistent and asymptotically normally distributed under regularity conditions. This research is limited to theoretical and methodological development without simulation or empirical validation. Future studies are recommended to conduct simulation analyses and apply the GTWLL3R model to real spatial-temporal datasets to evaluate estimator performance and model accuracy.
Pemilihan Bandwidth Optimal pada Regresi Nonparametrik Kernel Menggunakan Metode Unbiased Risk (UBR) Muchni Illahi Efendi; I Nyoman Budiantara; Vita Ratnasari
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.619-630

Abstract

Nonparametric regression is used when the relationship pattern between the response and predictor variables is not clearly defined. Among several nonparametric regression approaches, the kernel method is commonly used. The selection of the optimal bandwidth is a key factor in kernel regression since it significantly affects the estimation accuracy. The optimal bandwidth selection can be obtained using the Unbiased Risk (UBR) method. The study aims to derive the mathematical formulation of the UBR method and evaluate its effectiveness in determining the optimal bandwidth for Indonesia’s 2024 economic growth rate data. The results indicate that the UBR method can be utilized in choosing the optimal bandwidth in nonparametric kernel regression for the economic growth rate data in Indonesia in 2024, producing bandwidths for each predictor of , and , with a minimum UBR of 2.827 and an MSE of 2.198. This implies that the nonparametric regression model with the optimal bandwidth obtained using the UBR method has good predictive capability with a relatively low estimation error for the economic growth rate data in Indonesia.
PERAMALAN HARGA BERAS DI INDONESIA MENGGUNAKAN METODE HOLT-WINTERS ADDITIVE EXPONENTIAL SMOOTHING DENGAN OPTIMASI GOLDEN SECTION Yuni Nurul Faiza; Suparti Suparti; Arief Rachman Hakim
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.257-268

Abstract

Rice is one of the staples that must be fulfilled to support human survival. As a result, if the price of rice is instable, it can cause a decrease in people's purchasing power. Therefore, a system is needed that can forecast rice prices to help maintain food security. This study uses the Holt-Winters Additive method because it can be used to predict time series data that has trend and seasonal patterns. The optimum parameter is found using the Golden Section optimization method that minimize the MAPE value. The data used is the average monthly data of rice prices at the level of large trade (wholesale) Indonesia. The results showed that the data contained elements of trend and seasonality additives and obtained the best model with α = 0.999702, β = 0.059114, γ = 0.145618. The results of measuring the forecasting ability of the formed model show that the forecast results are close to the actual data and are evidenced by the MAPE out sample value of 7.006% which is include MAPE criteria < 10% so that the forecasting ability is very high. The forecast results for 2023 show that rice prices have fluctuated but the changes are not too significant.
AKURASI KINERJA METODE HYBRID GEOMETRIC BROWNIAN MOTION-KALMAN FILTER DALAM PERAMALAN HARGA SAHAM INDONESIA Aldan Maulana Hamdani; Nur Iriawan; Irhamah Irhamah
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.524-534

Abstract

This study aims to analyse the performance of a stock price forecasting model based on Geometric Brownian Motion (GBM) modified with the Kalman Filter (KF) approach. The GBM model is used to represent the basic behaviour of stock price movements, which are stochastic in nature, while the Kalman Filter plays a role in estimating model parameters based on actual observation data. This study uses closing price data for PT. Aneka Tambang Tbk. (ANTM) shares. The results of this study show the accuracy level using Mean Absolute Error Percentage (MAPE) in the GBM-KF hybrid model and are in the best fitting condition. For ANTM shares, it is 5.72% (GBM) and 3.83% (Hybrid GBM-KF). The GBM-KF hybrid model has proven to be effective in minimising prediction errors and capturing changes in market trends and volatility that cannot be explained by the classic GBM model. Furthermore, this study highlights that integrating Kalman Filter into GBM improves the model’s adaptability to dynamic market conditions, allowing for real-time parameter estimation and enhanced predictive stability. The findings suggest that the GBM-KF framework can serve as a robust tool for financial forecasting, particularly in volatile markets where traditional models tend to underperform.
PEMODELAN JUMLAH KASUS KEMATIAN BAYI DAN IBU DI PROVINSI LAMPUNG MENGGUNAKAN BIVARIATE GENERALIZED POISSON REGRESSION Dewi Indra Setiawan; Purhadi Purhadi
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.433-444

Abstract

Maternal and infant mortality are closely related, as the fetus receives nutrition from the mother through the placenta. Therefore, the mother's health condition during pregnancy directly impacts fetal development. Additionally, the mother's role in caring for the infant significantly affects the child's growth and survival. One of the goals of the Lampung Provincial Health Office’s Regional Medium-Term Development Plan (RPJMD) for 2020–2024 is to reduce maternal and infant mortality. The expected health target by the end of 2024 is to lower maternal deaths to 110 cases and infant deaths to 520 cases. This study employs the Bivariate Generalized Poisson Regression (BGPR) method to identify factors influencing maternal and infant mortality in Lampung Province in 2022. BGPR is suitable for handling overdispersed count data with two correlated response variables. Based on the AICc criterion, the best model includes all prediktor variables. The results show that the percentage of deliveries assisted by health professionals (X1) significantly affects maternal mortality, while both the percentage of deliveries by health professionals (X1) and the percentage of fourth antenatal care visits (K4) (X3) significantly affect infant mortality.
Pemilihan Parameter Osilasi Optimal Menggunakan Generalized Cross-Validation (GCV) pada Regresi Nonparametrik Deret Fourier Hasna Faridah Dhiya Ul Haq; I Nyoman Budiantara; Jerry Dwi Trijoyo Purnomo
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.577-587

Abstract

This research focuses on determining the optimal oscillation parameter in a Fourier series nonparametric regression model using the Generalized Cross-Validation (GCV) method to analyze factors influencing poverty in Central Java Province in 2024. The response variable is the percentage of the poor population, with Gross Regional Domestic Product (GRDP), Average Length of Schooling (ALS), and Open Unemployment Rate (OUR) as predictor variables. The optimal model is selected based on the minimum GCV value, with performance evaluated using MSE and . The results show that the minimum GCV is achieved at one oscillation, yielding an MSE of 4.545 and an  of 0.546, indicating that 54.6% poverty variation is explained by the predictors. Simultaneous testing shows a significant joint effect of predictors, while partial testing indicates no individual significance. Thus, GCV effectively determines the optimal oscillation parameter in Fourier series nonparametric regression for poverty analysis.
PENERAPAN MODEL REGRESI SEMIPARAMETRIK DERET FOURIER UNTUK MENGIDENTIFIKASI FAKTOR PENENTU ANGKA HARAPAN HIDUP DALAM KONTEKS SDGS 3 Sifriyani Sifriyani; Jesselin Paskalis Sitinjak; Andrea Tri Rian Dani
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.302-313

Abstract

Semiparametric regression combines parametric and nonparametric regression, applied when some relationships between the response and predictor variables are known while others are unknown. This study employed linear regression for the parametric component and the Fourier series estimator for the nonparametric component. The data consisted of life expectancy in Indonesia and its influencing factors, relevant to Sustainable Development Goals (SDGs) 3. The relationship between life expectancy and some predictors (maternal mortality rate, poverty rate) was linear, whereas with others (open unemployment rate, average years of schooling, Gini ratio, stunting prevalence, exclusive breastfeeding) the pattern was unknown and tended to be periodic. This characteristic aligns with the strength of the Fourier series semiparametric regression, which combines linear modeling with Fourier series components for nonlinear periodic relationships. The objective was to determine the optimal number of oscillations using the Generalized Maximum Likelihood (GML) method, obtain the best model, and identify factors affecting life expectancy. Results showed the best model had three oscillations with a GML of , RMSEP of 0.99, and R² of 82.67%. Significant factors included poverty rate, open unemployment rate, average years of schooling, Gini ratio, stunting prevalence, and exclusive breastfeeding.

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