Sugito Sugito
Departemen Statistika, FSM, Universitas Diponegoro, Jl. Prof Soedharto SH Tembalang, Semarang

Published : 37 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 37 Documents
Search

PEMODELAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN ADAPTIVE BANDWIDTH UNTUK ANGKA HARAPAN HIDUP (Studi Kasus : Angka Harapan Hidup di Jawa Tengah) Rizki Faizatun Nisa; Sugito Sugito; Arief Rachman Hakim
Jurnal Gaussian Vol 11, No 1 (2022): 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.v11i1.33998

Abstract

Life expectancy at birth (AHH) is an estimate of the years a person will take from birth. AHH is used as an indicator of public health and welfare. These two indicators are of concern to the government in relation to human development. It is hoped that the AHH value will continue to increase so that the quality of human development will also increase. Modeling of the factors that influence AHH needs to be done so that efforts to increase AHH become more effective.The AHH value for Central Java (Central Java) in 2020 is 74.37. Factors thought to influence AHH in Central Java are the percentage of poor people (X1), the percentage of households with proper sanitation (X2), the percentage of children under five who are fully immunized (X3) and the open unemployment rate (X4). The assumption of homoscedasticity in AHH modeling in Central Java using linear regression was not fulfilled, meaning that there was spatial heterogeneity between districts/cities, so the Geographically Weighted Regression (GWR) method was used. The weighting function used is the Bisquare and Tricube kernels with adaptive bandwidth. The GWR method will encounter problems if not all independent variables are local, so the Mixed Geographically Weighted Regression (MGWR) method is used. The results of the GWR analysis for the two weighting functions are that the X1 variable is not local, so the MGWR method is used. The results of MGWR modeling for the two weighting functions are that local variables and global variables have a significant effect. The best model is the MGWR model with Kernel Tricube weighting because it has the smallest AICc value. Keyword : AHH, GWR, MGWR, Adaptive Kernel Bisquare, Adaptive Kernel Tricube, AICc
ANALISIS KEPUASAN DAN LOYALITAS PELANGGAN DALAM PEMESANAN TIKET PESAWAT SECARA ONLINE MENGGUNAKAN PENDEKATAN PARTIAL LEAST SQUARE (PLS) Trisnawati Gusnawita Berutu; Abdul Hoyyi; Sugito Sugito
Jurnal Gaussian Vol 7, No 4 (2018): 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.v7i4.28863

Abstract

Technology advances are bring rapid changes, thus bringing the world to the information society. From this technological progress thus e-commerce emerged, as a means to meet the needs of goods and services through internet access (online). This is what the airlines utilized by cooperating with various internet service providers (online), to provide convenience and comfort of airplane passengers in buying tickets without having to come directly to the place and through intermediaries. To provide the best service, need to know what factors that influence customer satisfaction in ordering airline tickets online. Appropriate modeling for this problem using structural equation modeling, with Partial Least Square (PLS) approach. The PLS approach is chosen because it is not based on several assumptions, one of these is the normal multivariate assumption. In this research, the exogenous latent variables used are performance, access, security, sensation, information, and web design, while the endogenous latent variables are satisfaction and loyalty. Based on the results of the analysis it can be concluded that the latent variables of access, security, sensation, information, and web design are able to explain the latent satisfaction variable of 70.32% while the satisfaction latent variable is able to explain the latent variable of loyalty by 36.02%. 
ANALISIS SISTEM PELAYANAN KERETA API DI STASIUN SEMARANG TAWANG MENGGUNAKAN PROSES BAYESIAN Lifana Nugraeni; Sugito Sugito; Dwi Ispriyanti
Jurnal Gaussian Vol 9, No 4 (2020): 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.v9i4.29407

Abstract

Along with the times, transportation has progressed. Regarding the means of transportation, one of the phenomenon that is easily encountered in everyday life is the queue at public transportation facilities. One of the queues that occurred at public transportation facilities is  the train queue at Semarang Tawang Station. The number of trains that passes the station can cause the train service at the station busy. This study aims to see whether the train service system of Semarang Tawang Station is good or not. This can be consider by the queues method, determining the distribution of arrival patterns and service patterns to obtain a queues system model and a system performance standard. In this study, the distribution of arrival patterns and service patterns are determined by calculating the posterior distribution using the Bayesian method. The bayesian method was chosen because it is able to combine the sample distribution in the current study with the previous information for the same cases. The prior distribution and the likelihood function are the elements needed to obtain the posterior distribution. The distribution of arrival patterns and service patterns obtained from previous information follows the Poisson distribution. Based on the calculation of the posterior distribution, the result shows that the distribution of the arrival pattern is a discrete uniform distribution and the distribution of the service pattern is a Poisson distribution. The result shows that the train service system at Semarang Tawang Station has a model (Uniform Discrete / Gamma / 7: GD / ~ / ~) and has good service based on the performance values obtained.
ANALISIS METODE BAYESIAN PADA SISTEM ANTREAN PELAYANAN LOKET TIKET STASIUN TAWANG SEMARANG Aurum Anisa Salsabela; Sugito Sugito; Budi Warsito
Jurnal Gaussian Vol 10, No 2 (2021): 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.v10i2.29410

Abstract

Jamming is one of the serious problem in Indonesia caused by the increase of vehicle. The government has made solution for this situation for example was public transportation. Train is one of the suitable public transportation because of the ticket price was cheap. Tawang Railway Stasion Semarang was the biggest railway station in Semarang. In the specific day such long holiday or celebrating day, many people have chosen train to bring them. This make a queuing situation on the counter of station. Queue theory models provide the random of arrival and service time. The Bayesian theory suits to handle the problem of queuing that has been working for several times. Based on the analysis of the queue models for customer service, self-print tickets, cancellation and ordering are (G/G/c):(GD/∞/∞) from the posterior distribution with combination from prior distribution and likelihood sample. The combination of prior distribution and likelihood sample used in this research is Poisson distribution for all ticket counter except the arrival for cancellation counter which Normal distribution. The likelihood sample used Poissonn distribution for all ticket counter, except for self-print tickets which Diskrit Uniform Distribution.  Queue models can be used to count the size of the system performance. Based on the calculations and analysis, it can be concluded that the queueing system to the customer service, self-print tickets, cancellation and ordering have been good because its steady state and busy probability is higher than jobless probability. Keywords: Tawang Railway Station, Queue, Bayesian, size of the system performance
ANALISIS ANTREAN DAN KINERJA SISTEM PELAYANAN GARDU TOL OTOMATIS GERBANG TOL MUKTIHARJO (Studi Kasus: Gardu Tol Otomatis Gerbang Tol Muktiharjo) Erna Fransisca Angela Sihotang; Sugito Sugito; Moch. Abdul Mukid
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (538.082 KB) | DOI: 10.14710/j.gauss.v8i1.26625

Abstract

Queue process is a process related to the arrival of customers in a service facility, waiting in line queue if it cannot be served, get service and finally leaves the facility after being served. Research on the queue process can be seen directly through the queue system at the automatic toll booth Muktiharjo. Queue models and their distribution were obtained using the Sigma Magic program. The model of the vehicle queue system at the Muktiharjo Automatic Toll Gate is (GAMM/ GAMM/ 4): (GD/ ∞/ ∞). Based on the values of the queue system performance measures obtained through the MATLAB GUI program as a whole it can be concluded that the queue of vehicles at the Muktiharjo Automatic Toll Gate has a condition where the average number of vehicles estimated in the system every 15 minutes is 25,5646 vehicles. The average number of vehicles in the queue system every 15 minutes is 24,5639 vehicles. The waiting time in the system is estimated to be around 7,99332 seconds. The estimated waiting time in line is around 7,68042 seconds. The queue system has a busy opportunity of 63.2849% and the remaining 36.7106% is a chance the queue system is not busy. The simulation of the vehicle queue system at the Automatic Toll Gate of Muktiharjo Toll Gate by using ARENA is optimal with the number of service points as many as 4 automatic toll booths. Keywords: Automatic Toll Booth, Queue, Gamma Distribution, Performance Size, Queue Simulation
PENERAPAN GRAFIK KENDALI JUMLAH KUMULATIF UNTUK MENDETEKSI PERGERAKAN KURS MATA UANG (Studi Kasus: Kurs Jual dan Kurs Beli Dollar Amerika) Silvia Julietty Sinaga; Mustafid Mustafid; Sugito Sugito
Jurnal Gaussian Vol 6, No 4 (2017): 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.v6i4.30382

Abstract

The Average Control Chart (  can be used to see if there has been an average change in a process. But this graph has a weakness that is not sensitive to small average shifts. The Cumulative Sum Control Chart (CUSUM) is considered to be more effective in detecting small average process shifts, because it combines information taken from multiple samples by describing the cumulative number of sample deviations from the target value. Both of these graphs are used to detect currency exchange rate shifts with the conclusion that the exchange rate of US Dollar (USD) to Rupiah (IDR) are out of control. The Average Run Length (ARL) value of the CUSUM Chart tends to be smaller than ARL of the  chart. The ARL of CUSUM Control Chart for the selling rate and buying rate is 14,4269 and 19,3798. The ARL of  chart with the 3 sigma limit is 370,37. CUSUM control chart also gives the result that the average of selling rate has increased from 13,022 to 13,200 and the average of buying rate has decreased from 13,022 to 12,6027. This means that the Dollar selling price in the bank will increase/expensive while the Dollar purchase price will decrease/cheaper. Keywords: Exchange Rate, Average Control Chart, Cumulative Sum Control Chart (CUSUM), Average Run Length (ARL), US Dollar (USD), Rupiah (IDR)
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT KEMISKINAN DENGAN METODE REGRESI PROBIT ORDINAL (Studi Kasus Kabupaten/ Kota di Jawa Tengah Tahun 2013) Alin Citra Suardi; Triastuti Wuryandari; Sugito Sugito
Jurnal Gaussian Vol 5, No 1 (2016): 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.v5i1.10908

Abstract

According to BPS, Central Java is third in terms of the number of poor people in Indonesia. The overall number of poor people in Central Java in 2013 was 4.811.300 inhabitants. Factors that influence the level of poverty can be derived from the employment factor, economic factors, or educational factors. Based on these three factors independent variables were selected which supposed to influence the poverty level. There are inflation, City Minimum Wage by Regency/City, Gross Regional Domestic Product at constant market prices, Unemployment Rate, Mean Years of Schooling, and Illiteracy Rate. The poverty level is categorized into three categories. There are Prosperous, Medium and Poor. The independent variables were analyzed its effect on poverty levels that have been categorized by the Ordered Probit Regression method. The complete model of the ordered probit regression is tested the significance of the parameters by likelihood ratio test and Wald test. Based on ordered probit regression analysis, variables that affect the level of poverty in Central Java in 2013 was Inflation, City Minimum Wage by Regency/City, and Mean Years of Schooling (MYS).Keywords : Poverty Level, Central Java, Ordered Probit Regression.
PERAMALAN MENGGUNAKAN MODEL FEED FORWARD NEURAL NETWORK DENGAN ALGORITMA ADAPTIVE SIMULATED ANNEALING (Studi kasus: Harga minyak mentah dunia yang dipublikasikan oleh OPEC) Affan Hanafaie; Sugito Sugito; Sudarno Sudarno
Jurnal Gaussian Vol 7, No 4 (2018): 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.v7i4.28865

Abstract

Today, crude oil trading industry is still an important industry in the world because it still has high fuel oil consumption. The crude oil prices tend to fluctuate causing the prediction of crude oil in the coming periods to be a challenge. Forecasting the price of crude oil can be done by various methods, one of them is ARIMA Box-Jenkins model with OLS method to estimate the parameter, but this method has several assumptions that must be met. As time goes by, many methods that discovered, one of them is artificial neural network which can combined with various parameter optimization methods such as Adaptive Simulated Annealing algorithm. Adaptive Simulated Annealing algorithm is an optimization method that inspired by the process of crystallization, the advantages of this algorithm has a running time faster than similar algorithms. The combination of artificial neural networks and Adaptive Simulated Annealing algorithms can be used to model the historical data without requiring assumptions in the analysis. Based on the analysis on this research, the best model is obtained FFNN 2-5-1 with MAPE value of 1.0042%. Keywords: neural network, Adaptive Simulated Annealing, crude oil.
ANALISIS METODE BAYESIAN MENGGUNAKAN NON-INFORMATIF PRIOR UNIFORM DISKRIT PADA SISTEM ANTREAN PELAYANAN GERBANG TOL MUKTIHARJO Dini Febriani; Sugito Sugito; Alan Prahutama
Jurnal Gaussian Vol 10, No 3 (2021): 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.v10i3.32783

Abstract

The growth rate of the traffic that is high resulting in congestion on the road network system. One of the government's efforts in addressing the issue with the build highways to reduce congestion, especially in large cities. One of the queuing phenomena that often occurs in the city of Semarang is the queue at the Toll Gate Muktiharjo, that the queue of vehicles coming to make toll payment. This study aims to determine how the service system at the Toll Gate Muktiharjo. This can be known by getting a queue system model and a measure of system performance from the distribution of arrival and service. The distribution of arrival and service are determined by finding the posterior distribution using the Bayesian method. The bayesian method combine the likelihood function of the sample and the prior distribution. The likelihood function is a negative binomial. The prior distribution used a uniform discrete. Based on the calculations and analysis, it can be concluded that the queueing system model at the Toll Gate Muktiharjo is a (Beta/Beta/5):(GD/∞/∞). The queue simulation obtained that the service system Toll Gate Muktiharjo is optimal based on the size of the system performance because busy probability is higher than jobless probability.  
PENENTUAN FAKTOR-FAKTOR YANG MEMPENGARUHI INTENSITAS CURAH HUJAN DENGAN ANALISIS DISKRIMINAN GANDA DAN REGRESI LOGISTIK MULTINOMIAL (Studi Kasus: Data Curah Hujan Kota Semarang dari Stasiun Meteorologi Maritim Tanjung Emas Periode Oktober 2018 – Maret 2019) Shella Faiz Rohmana; Agus Rusgiyono; Sugito Sugito
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (609.707 KB) | DOI: 10.14710/j.gauss.v8i3.26684

Abstract

Meteorologist develop rainfall forecasting methods to obtain better and more accurate rainfall information. One of them is the research of grid data and the method of grouping rainfall. According to BMKG, rainfall is classified into light, medium, and heavy rain. This study aims to determine the factors that influencing rainfall grouping using multiple discriminant analysis with a stepwise selection method. This study uses the daily climate data of Semarang City for period of October 2018 to March 2019. Based on its partial F value, the wind speed variable is eliminated so the significant variable on rainfall grouping are air temperature, air humidity, and wind direction. This analysis produces discriminant scores obtained from linear combinations between discriminant weights and observation values of significant independent variable. The classification procedure is based on the discriminant score each observations compared to cutting score resulted in classification accuracy of 62.89%. Multinomial logistic regression analysis is used to determine the effect of independent variables on rainfall intensity using the odds ratio. This analysis produces an estimate of the conditional probability of each group using significant independent variables are air temperature, air humidity, wind speed, and wind direction. The classification procedure is based on the largest conditional probability value between rainfall groups resulted in classification accuracy of 69.80%. Keywords: multiple discriminant analysis, multinomial logistic regresion, classification accuracy, rainfall