cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
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
Location
Kota semarang,
Jawa tengah
INDONESIA
Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
Other
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 808 Documents
 77   78   79   80   81 
PERAMALAN JUMLAH PENUMPANG PENERBANGAN DOMESTIK PADA TIGA BANDARA UTAMA INDONESIA MENGGUNAKAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) Nabila Zahra Tamami; Puspita Kartikasari; Deby Fakhriyana
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.15.1.243-254

Abstract

Forecasting the number of airplane passengers requires a model related to time and location because the data on the number of airplane passengers is space time series data. Generalized Space Time Autoregressive (GSTAR) is an approach to forecast time series data that show a connection between time and location. The GSTAR model can be applied to data with heterogeneous location characteristics.  In this research, the GSTAR model is used to form a forecasting model for the number of domestic flight passengers at three major airports in Indonesia Juanda Airport, Soekarno Hatta Airport, and Hasanuddin Airport, using optimal location weighting. The residual of the GSTAR model satisfies the white noise assumption. The location weights used are uniform weights, inverse distance weights, and cross-correlation normalization. The outcome model is constructed with a first-order differencing, an autoregressive order of 1, and spatial order of 1, resulting in the formation of the GSTAR (11)I(1) model. The most optimal model generated is the GSTAR (11)I(1) model utilizing inverse distance weights due to its smallest sMAPE value compared to other weightings, which stands at 8,71%. This sMAPE value signifies a highly precise forecast accuracy.
PERBANDINGAN PERAMALAN MENGGUNAKAN DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES MARKOV CHAIN UNTUK DATA INFLASI INDONESIA Ica Rahma Kurniawati; Suparti Suparti; Tarno Tarno
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.15.1.224-233

Abstract

Inflation is one measure of a country’s economic condition. The inflation rate in Indonesia had decreased to 1.33% in June 2021 when the Covid-19 pandemic was hitting. Forecast of the inflation rate is needed for monitoring economic condition. The Double Exponential Smoothing Holt method can be used on data that contains trend elements and uses two smoothing parameters in the analysis, while the Fuzzy Time method Series Markov Chain is a development of the Fuzzy Time Series method with the Markov chain concept which does not have any conditions in its analysis. The results of forecasting analysis from the Double Exponential Smoothing Holt method obtained an MAE value of 0.35957, while from the Fuzzy Time Series Markov Chain method obtained an MAE value of 0.22462. Thus, the Fuzzy Time Series Markov Chain model is a better model in forecasting than the Double Exponential Smoothing Holt method. That model is reused to analyze forecasting and get a MAPE value of 8.79% which means that the Fuzzy Time Series Markov Chain model predicts Indonesia’s inflation data very well.
PENGELOMPOKAN DAERAH RAWAN KRIMINALITAS DI INDONESIA TAHUN 2021 MENGGUNAKAN METODE K-MEANS & FUZZY C-MEANS Wulan Cahya Rahma Rahmadani; Tatik Widiharih; Tarno Tarno
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.15.1.234-242

Abstract

Crime is one of the aspects that can influence national stability and security. In this study, crime data is used to cluster crime-prone areas and is considered whether these areas require extra surveillance or not. This research employs the K-means and Fuzzy C-Means methods. The K-means method groups data based on the similarity of data with cluster centroids. This algorithm is relatively efficient in complexity and easy to understand. K-means explicitly allocates data to specific clusters. On the other hand, Fuzzy C-Means is capable of placing a data point that lies between two or more other clusters into a single cluster. This is due to each data point having a degree of membership to determine its grouping, making the chances of failure to converge or stable cluster centers very low. The optimal number of clusters is selected using validation through the Davies Bouldin Index and Calinski Harabasz Index. The research results indicate that clustering crime-prone areas using both methods yield the same outcome of 2 clusters. Cluster 1 exhibits a higher crime rate compared to cluster 2, as indicated by the higher average values of group members. The lowest Davies Bouldin Index is 1.04948, and the highest Calinski Harabasz Index is around 24.36783.
PENGUKURAN RISIKO KREDIT DAN PEMBENTUKAN SKENARIO RATING OBLIGASI KORPORASI MENGGUNAKAN CREDIT METRICS DAN SIMULASI MONTE CARLO Widyah Kurniasari; Agus Rusgiyono; Masithoh Yessi Rochayani
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.15.1.289-300

Abstract

Indonesia's economic recovery after the pandemic has positively impacted the capital market, leading investors to invest in fixed-income instruments like bonds. Bonds have their own risks, including default risk and future cash value (valuation). To measure bond risk, investors use the Credit Metrics method and Monte Carlo simulation to simulate bond rating scenarios. For this project, five bonds from companies listed in the PEFINDO25 Index are used. The credit risk of each bond is calculated based on the mean and standard deviation. HEAL bonds have the smallest mean at IDR 13.16 billion, while MYOR bonds have the largest mean at IDR1 542.87 billion. The rating scenario shows that DSNG, HEAL, MYOR, TINS, and TOBA bonds are more stable in their initial ratings, but DSNG and TOBA bonds have potential downgrade to CCC. Sixteen portfolio combinations were formed, with a mean valuation of IDR 2,446.63 billion for a portfolio of five bonds and IDR 639.23 billion for the three bond portfolios. These valuations can be used as a consideration for investors when making bond investment decisions.
ANALISIS VALUE AT RISK ASET TUNGGAL DENGAN PENDEKATAN EKSPANSI CORNISH-FISHER MOMEN KELIMA Nirma Oktaviani; Di Asih I Maruddani; Rahmila Dapa
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.15.1.264-270

Abstract

Stocks are tangible proof of ownership in a corporation. In investment, the greater the return obtained, the greater the risk received. Value at risk is used as a risk measurement tool by estimaing the worst loss at a given level of confidence and time stage. VaR generally assumes that the data used meets the assumption of normality. However, data in the field often shows deviations from the assumption of normality due to fluctuating movements. The Cornish-Fisher Expansion is one approach in calculating VaR that does not require normality assumptions. The Cornish-Fisher Expansion method generally takes into account the first 4 quantiles, namely mean, variance, skewness, and kurtosis in VaR predictions. However, in its development this method can be extended beyond the fourth moment. The data used in this study is PT Aneka Tambang Tbk's daily closing stock price data for the period January 4, 2021 to December 31, 2022 totaling 493 data. Calculation results using the Cornish-Fisher Expansion method to the fifth moment show that the estimated value of VaR is higher than the regular Cornish-Fisher Expansion.
PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN ALGORITMA PAMDAN ALGORITMA DBSCAN BERDASARKAN INDIKATOR KELUARGA SEHAT Inez Clarissa Nababan; Puspita Kartikasari; Ardiana Alifatus Sa’adah
Jurnal Gaussian Vol 15, No 2 (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.15.2.312-319

Abstract

The healthy family index is an index used to provide an overview of the health conditions in a family. The health status of families in Central Java Province varies in each district/city, so health development priorities are also different. Based on indications of a healthy family, this study seeks to classify Central Java Province's districts and cities to determine the high/low quality of healthy families in each district/city in order to aid the government in maximizing its efforts to enhance health. The combination of district/city in Central Java Province was carried out using cluster analysis using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Partitioning Around Medoids (PAM) algorithm. The outcomes of grouping using the PAM algorithm obtained 4 clusters where cluster one had 16 cluster members, cluster two had 10 cluster members, cluster three had 8 cluster members, and cluster four had 1 cluster members. Grouping using the DBSCAN algorithm produces 2 clusters and 3 noise. There are many members in each cluster, namely cluster one has 29 cluster members and cluster two has 3 cluster members. Comparing the grouping results using the silhouette index value, it was found that the DBSCAN algorithm was the best algorithm with a value of the silhouette index of 0.1425. The description of the characteristics obtained is that cluster two is a cluster that has a lower average of 6 healthy family indicators compared to cluster one.
ANALISIS KOMPARATIF DAN EVALUASI KINERJA METODE BRIDGE, LASSO, DAN RIDGE REGRESSION DALAM PREDIKSI PERSENTASE LEMAK TUBUH BERBASIS ANTROPOMETRI Chesa Nabilla Azzahra; M. Syarlan; A'inun Fauziyah; Zahra Hana Andrea; Etis Sunandi; Idhia Sriliana; Susi Wijuniamurti
Jurnal Gaussian Vol 15, No 2 (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.15.2.301-311

Abstract

This study comparatively evaluates the performance of Bridge, LASSO, and Ridge regression methods in predicting body fat percentage based on anthropometric measurements. Exploratory analysis indicated strong relationships between body fat percentage and abdominal and hip circumferences, with evidence of multicollinearity among the predictors. The ordinary least squares (OLS) model identified significant predictors but violated several classical assumptions, particularly multicollinearity, heteroskedasticity, and autocorrelation. Therefore, penalized regression methods were applied to obtain more stable parameter estimates and improve predictive performance. Model performance was assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R²), adjusted R², Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Variance Inflation Factor (VIF). The results show that Bridge regression with γ = 0.5 achieved the best predictive performance, producing the lowest MSE and AIC values. LASSO regression provided the simplest model by eliminating irrelevant predictors and achieved the lowest BIC and VIF values, while Ridge regression produced stable estimates but did not outperform the other methods. Overall, Bridge regression is recommended for prediction, whereas LASSO is preferable when model simplicity and interpretability are prioritized.
PERBANDINGAN ALGORITMA NEAREST NEIGHBOR DAN ANT COLONY OPTIMIZATION DALAM OPTIMASI RUTE WISATA SEMARANG BERBASIS MULTI-ATTRIBUTE UTILITY THEORY Ashilah Tsuraya Izzati; hasbi yasin; Triastuti Wuryandari
Jurnal Gaussian Vol 15, No 2 (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.15.2.320-331

Abstract

Tourism route planning is an important aspect of tourism development to improve travel efficiency and destination quality. This study aims to determine priority tourist destinations and to compare the performance of tourism route optimization methods in Semarang. Priority destinations are identified using the Multi-Attribute Utility Theory (MAUT) with criteria weights determined by the Rank Order Centroid (ROC). Tourism destinations with the highest utility values are selected as priority destinations and modelled as Traveling Salesman Problem (TSP). Tourism route optimization is carried out by comparing three optimization problem-solving method, namely exact method, heuristic method, and metaheuristic method. The exact method employs the Branch and Bound (B&B) algorithm as a benchmark to obtain the optimal solution. The heuristic method uses the Nearest Neighbour (NN) algorithm and metaheuristic method uses the Ant Colony Optimization (ACO) algorithm. Algorithm performance is evaluated based on total travel distance, computation time, and relative error (RE) to the optimal solution. The result show that the NN algorithm yields a relative error of 8.66%, while the ACO algorithm achieves a lower relative error of 2.2%. This indicates that ACO algorithm produces routes that are closer to the optimal solution.
REGRESI ROBUST ESTIMASI-M UNTUK PEMODELAN TINGKAT PENGANGGURAN TERBUKA (TPT) DI PROVINSI JAWA TENGAH Octa Choerunnisa; Mustafid Mustafid; Sudarno Sudarno
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.15.1.271-279

Abstract

Regression analysis can be used to overcome the outlier problem with robust regression. The M-estimation robust regression method is an estimation approach that is the simplest computationally and theoretically. The purpose of using the robust regression method is because there is an outlier problem in the data. This research will create a model on open unemployment rate data in Central Java in 2022 which involves variables including average length of schooling, population, number of poor people, minimum wage, and per capita expenditure. Based on the Ordinary Least Squares (OLS) regression equation, it shows that there is a violations of assumptions and outlier data is detected. So it is necessary to carry out robust regression to overcome the outlier problem. In the test carried out using M-estimation robust regression, the best robust regression model was obtained  = -5,899 + 0,0192 X3+ 0,0000024 X4+0,00036 X5. The M-estimation robust regression model can be concluded that total variation in the open unemployment rate in Central Java in 2022 is explained by the number of poor people, minimum wages, and per capita expenditure by 31.39%, and the MSE value is 2.143382.
PERBANDINGAN METODE QUANTILE REGRESSION DAN BAYESSIAN QUANTILE REGRESSION (BQr) PADA DATA KRIMINALITAS DI JAWA TIMUR Alfisyahrina Hapsery; Ade Novadio; Artanti Indrasetianingsih; Fenny Fitriani
Jurnal Gaussian Vol 15, No 2 (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.15.2.332-343

Abstract

According to 2023 Criminal Statistics, the number of crime cases in Indonesia in 2022 will reach 372,965 cases. There was a quite drastic increase in the number of crimes in 2022. The highest crime cases based on the 2023 Crime Statistics occurred in East Java, reaching 51,905 incidents in 2022. These incidents increased drastically compared to 2021, namely 19,257 incidents. There are several factors that can influence the crime rate. To determine the factors that can influence the crime rate, it is necessary to carry out an analysis using a method that can use data that contains outliers or inhomogeneous errors. The quantile regression method is a method used to estimate parameters, where this method is used on data that is not easily affected by outliers so that it does not disturb the stability of the data. Apart from using quantile regression, this research also uses the Bayessian Quantile Regression (BQr) method. By using residual values as quantile regression method analysis. The variables used in this research are crime data, percentage of population, Open Unemployment Rate, percentage of poor population, GRDP growth rate, and Labor Force Participation Rate, Human Development Index (HDI). Each data covers districts/cities in East Java in 2024.

Page 80 of 81 | Total Record : 808

 77   78   79   80   81 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 15, No 2 (2026): Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian Vol 14, No 1 (2025): Jurnal Gaussian Vol 13, No 1 (2024): Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian Vol 11, No 3 (2022): Jurnal Gaussian Vol 11, No 2 (2022): Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian Vol 6, No 4 (2017): Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian More Issue