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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,
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 733 Documents
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PERAMALAN PEREDARAN UANG KARTAL DI INDONESIA MENGGUNAKAN MODEL HYBRID SARIMAX-NEURAL NETWORK Juliarto, Handy Kurniawan; Purnamasari, Ika; Prangga, Surya
Jurnal Gaussian Vol 12, No 4 (2023): 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.12.4.465-476

Abstract

Stability in the economy is influenced by technological advancements, which impact the digitization of the economy and lead to an increasing demand for electronic and digital payment systems compared to physical currency. There are certain months, such as during year-end holidays, when the circulation of physical currency increases. This study purpose to forecasting the total currency circulation in Indonesia, considering the influence of calendar variations, using a hybrid method that combines SARIMAX and NN. The SARIMAX method was utilized to capture linear effects related to calendar variations, while the NN method was employed to capture nonlinear patterns. The analysis results indicated that the hybrid SARIMAX-NN model with 1 to 3 neurons yielded accurate forecasts, with Mean Absolute Percentage Error (MAPE) values below 2%. However, the highest accuracy was achieved by the SARIMAX-NN hybrid model with 1 neuron, which had a MAPE of 1.38%. Additionally, the forecasting results showed a consistent monthly increase, particularly during the holiday season in December
PEMODELAN KASUS KEMATIAN IBU HAMIL DI JAWA TENGAH DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) DAN MIXED GWR Salma, Anugrah Rawiyah; Sugito, Sugito; Sa’adah, Ardiana Alifatus
Jurnal Gaussian Vol 13, No 1 (2024): 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.13.1.25-35

Abstract

Global maternal mortality reduction remains a part of The Sustainable Development Goals (SDGs). Maternal death in Central Java increased in 2021, with the leading cause is related to Covid-19. Maternal mortality in Central Java is demonstrated using Geographically Weighted Regression (GWR) for addressing the spatial heterogeneity aspects. Cross Validation is used to determine the optimal bandwidth and Euclidean distance used to discover the weighting matrix. Independent variables such as number of nurse, number of primary clinic, and household percentage with safe water supply are identified as local variables, whereas other independent variables, such as complications from delivery management and number of poverty are identified as global variables, hence the Mixed GWR model, which combine both local and global variables, is used. Based on the value of AIC, MSE, also adjusted , the optimal model for analyzing the maternal mortality is Mixed GWR with fixed Gaussian weighting.
KAJIAN SISTEM ANTRIAN PADA COUNTER KASIR DOMINO’S PIZZA MENGGUNAKAN MEAN VALUE ANALYSIS (STUDI KASUS: DOMINO’S PIZZA GAJAH MADA PEKALONGAN) Putri Milenia, Erin Novela; Sugito, Sugito; Widiharih, Tatik
Jurnal Gaussian Vol 12, No 3 (2023): 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.12.3.425-433

Abstract

Queuing is the phenomenon that occurs when a service needs more than it can handle. This phenomenon is common in many places, such as restaurants. Attempts to analyze the behavior of queuing systems are called queuing system studies, one of which is the use of mean analysis (MVA). MVA can be used when arrival and service times do not follow an exponential distribution. The case study is the queuing system of Domino's Pizza Gajah Mada Pekalongan, which has two counters and took seven days to observe. This study aims to apply MVA and determine performance measures for queuing systems. In this study, MVA can be used because the arrival-to-service time does not follow an exponential distribution. The resulting cue model is (Gamma/GEV/2). (GD/∞/∞) and utilization is 0.43045. The average customer queuing and in the system are at most one customer. The average time to queue is 31.80336 seconds, the average time to complete a service is 321.0971 seconds, and the probability that the system isn’t busy 0.39816 or 39.8%.
OPTIMASI PORTOFOLIO MEAN-VARIANCE DENGAN ANALISIS KLASTER FUZZY C-MEANS Gubu, La; Cahyono, Edi; Arman, Arman; Budiman, Herdi; Djafar, Muh. Kabil
Jurnal Gaussian Vol 12, No 4 (2023): 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.12.4.593-604

Abstract

Many studies have been carried out to solve and develop the Markowitz portfolio model. This was done to correct existing models in response to the changes in financial market dynamics and the needs of capital market practitioners. In this study, we provide Mean-Variance (MV) portfolio selection via cluster analysis. Fuzzy C-Means clustering is used to separate stocks into different categories. As a comparison, stocks categories were also carried out using K-Mean clustering. Based on the Sharpe ratio, a stock from each cluster is chosen as a cluster representative. The stocks chosen for each cluster have the greatest Sharpe ratio. The MV portfolio model is used to determine the best portfolio. For the empirical analysis, we examined the fundamental data and the daily return data of stocks that were included in the LQ-45 index from August 2022 to January 2023. The fundamental data of stocks are used to form clusters and the daily return of stocks are used to construct the best portfolio. The results of this study reveal that, for all given risk aversion values, portfolio performance created by Fuzzy C-Means clustering outperformed portfolio performance produced by K-Means clustering.
PERBANDINGAN KINERJA METODE KLASIFIKASI K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINES PADA DATASET PARKINSON Ridho, Wahyu Anwar; Wuryandari, Triastuti; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 3 (2023): 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.12.3.372-381

Abstract

The government program in the form of social assistance (bansos) is part of the effort to improve the welfare of the community and ensure basic needs and improve the standard of living of the recipients. However, there are often cases of mistargeting of social assistance programs by the government. Improper data management and Data Terpadu Kesejahteraan Sosial (DTKS) which are not used as the cause of the distribution of social assistance are not well targeted. The data can be analyzed using the classification method to determine whether or not the family accepts the ban from the government. This study classifies the SUSENAS data by comparing K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The advantage of the KNN method lies in the level of accuracy to solve problems with large data while the SVM method has better performance in various fields of application such as bioinformacs, handwriting recognition, text classification and so on. Based on training data and testing data comparison 85%:15% showed that KNN method had a better classification performance than the SVM method. The accuracy value of KNN method is 80,95% higher than the accuracy value of SVM method is 78,79%.
PEMODELAN JUMLAH KASUS PNEUMONIA PADA BALITA DI JAWA TIMUR MENGGUNAKAN METODE REGRESI POISSON INVERSE GAUSSIAN DILENGKAPI GUI-R Utami, Krisdiana Nur; Sugito, Sugito; Santoso, Rukun
Jurnal Gaussian Vol 12, No 4 (2023): 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.12.4.539-548

Abstract

Reducing toddler mortality is one of the desire of sustainable development programs.Modeling count data may be analyzed the usage of Poisson regression.The assumption that must be met in Poisson regression is that the mean and variance values must be equal, often in count data there is a violation of this assumption. This is indicated by the variance value which is greater than the mean value (overdispersion). Poisson Inverse Gaussian (PIG) regression is one form of mixed Poisson regression to model data that experience overdispersion cases. The MLE method is used to estimate the PIG regression parameters and hypothesis testing using the MLTR method. The best model of the PIG regression form is based on the smallest AIC value. The results of hypothesis testing concluded that the percentage of under-fives who received exclusive breast feeding had a significant effect on the number of pneumonia cases among toddler. Data modeling using the PIG regression method in this study is complemented by the creation of a Graphical User Interface (GUI) that can facilitate the process of selecting the best model.
PENERAPAN REGRESI COX UNTUK MENGANALISIS VARIABEL YANG BERPENGARUH TERHADAP DURASI STUDI MAHASISWA Wulandari, Dewi; Widyastuti, Rhoudhotul; Prasetyowati, Dina
Jurnal Gaussian Vol 13, No 1 (2024): 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.13.1.88-98

Abstract

One aspect that concerns stakeholders in a university is the study duration of students because this is one of the determinants of the quality of a university. In the Mathematics Education study program, at Universitas PGRI Semarang, there has never been any research on the length of study of students. So we conducted this study to determine the factors that significantly influence students' study duration. We applied Cox regression to data on students of the Mathematics Education study program at Universitas PGRI Semarang from entering 2017 until graduating in various years from 2021 to 2023 with predictor variables of Cumulative Achievement Index, gender, parental educational background, and student’s participation in the organizations. These data were collected using a questionnaire and data triangulation was confirmed through interviews. Meanwhile, the students’ study duration data is the secondary data that has been documented in the information system of Universitas PGRI Semarang. Analysis using Cox Regression is very suitable for the case study in this study because the dependent variable in this study is survival data. In addition, Cox Regression is known as a method that is relatively easy, simple and does not require survival data to have a certain distribution. From the analysis results, it was found that the Cumulative Achievement Index is the factor that has the most significant influence on the length of student study. 
PERAMALAN HARGA BERAS PREMIUM BULANAN DI TINGKAT PENGGILINGAN MENGGUNAKAN FUZZY TIME SERIES MARKOV CHAIN Sari, Virgania; Hariyanto, Sylvia Ayu
Jurnal Gaussian Vol 12, No 3 (2023): 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.12.3.322-329

Abstract

Rice is one of the crucial food commodities in Indonesia whose price fluctuates every year. Forecasting is the science of predicting an event in the future and predicting future conditions using historical data. One of the forecasting methods is the Fuzzy Time Series which is used to predict time series data that can be widely used on any real time data. This research used forecasting with the Fuzzy Time Series Markov Chain method because this method provides a good accuracy value. The historical data used is monthly data on the average price of premium rice at the Indonesian mill level for the period January 2014-July 2022 then divided into training data and testing data. The error rate used is MAPE and the results of calculations with Fuzzy Time Series Markov Chain on data testing the period November 2020-July 2022 obtained a very good MAPE value of 0.81%. Forecasting results for the period August 2022 obtained the results of Rp. 9.627,99
PEMBENTUKAN PORTOFOLIO OPTIMAL DENGAN METODE MEDIAN VARIANCE PADA SAHAM JAKARTA ISLAMIC INDEX (JII) SEKTOR CONSUMER GOODS Faadillah, Muhamad Nabil; Maruddani, Di Asih I; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 4 (2023): 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.12.4.487-498

Abstract

Investment is an activity to place owned assets or funds in a product hoping that there will be profits in the future. This case study was conducted by calculating the optimal portfolio using the median variance and calculating Value at Risk (VaR) using the historical simulation method. Median Variance in portfolio optimization is more suitable to be used as an investment guide because the method is not fixated on the normality distribution of the data. The data used is the Jakarta Islamic Index (JII) daily stock price data for 1 year period, which start from April 23th 2021 until April 23th 2022. The stock price used in this research is the closing price data each day during the period. The return data is used to find the weight using Median Variance method so that an optimal portfolio is formed. it is known that the Value at Risk with a confidence level of 95% and the next 1-day time period is -0,024088232 or -2,41% by investing 1% of the funds into UNVR.JK shares., by 58 % to shares of ICBP.JK, by 57% to shares of INDF.JK, by 1% to shares of JPFA.JK, and the last -17% to KLBF.JK shares is 2.41%. 
PEMODELAN GEOGRAPHICALLY WEIGTED REGRESSION PADA ANGKA PARTISIPASI SEKOLAH DI KALIMANTAN BARAT TAHUN 2022 Mujiarti, Eka May; Yundari, Yundari; Huda, Nur'ainul Miftahul
Jurnal Gaussian Vol 13, No 1 (2024): 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.13.1.36-47

Abstract

Angka Partisipasi Sekolah" (APS) indicates educational quality in a region, with higher APS reflecting better education. In 2022, APS for SMA/SMK/MA/Paket C in West Kalimantan was 68.72%, a decrease from the previous year. A Geographically Weighted Regression (GWR) approach which considers geographic characteristics in modeling the relationship between response and predictor variables, is used to analyze factors influencing APS in West Kalimantan. This study aims to model APS and identify influencing factors. Initial steps include detecting multicollinearity and spatial heterogeneity, and determining the Euclidean distance and bandwidth value of the weighting function. The study uses fixed and adaptive Gaussian, bisquare, and tricube kernels. GWR model parameters are then estimated, and the best model is chosen based on the smallest Akaike Criterion Information (AIC) value. Results show that the best weight is the adaptive bisquare kernel with the smallest AIC. Key factors influencing APS, with a 99.07% coefficient of determination, include the number of schools, teachers, student-teacher ratio, poverty rate, and PDRB per capita, with the remaining 0.93% influenced by unstudied factors.

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