Article Per Year (5 Year)
p-Index From 2021 - 2026
2.644
P-Index
This Author published in this journals
All Journal Media Statistika SAINS DAN MATEMATIKA Jurnal Gaussian E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal Surya Masyarakat Jurnal Surya Masyarakat
Sugito Sugito
Departemen Statistika, FSM, Universitas Diponegoro, Jl. Prof Soedharto SH Tembalang, Semarang
Religion Humanities Decision Sciences, Operations Research & Management Engineering Environmental Science Mathematics Public Health Social Sciences Other

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

Found 37 Documents
Search

 1   2   3   4 
MODEL FEED FORWARD NEURAL NETWORK (FFNN) DENGAN ALGORITMA PARTICLE SWARM SEBAGAI OPTIMASI BOBOT (Studi Kasus : Harga Daging Sapi dari Bank Dunia Periode Januari 2007 – Desember 2018) Faisal Fikri Utama; Budi Warsito; Sugito Sugito
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 (443.97 KB) | DOI: 10.14710/j.gauss.v8i1.26626

Abstract

Beef is one of the important food commodities to fulfill the nutritional adequacy of humans. The World Bank notes the beef prices that are exported worldwide every month. For this reason, those data becomes a predictable series for the next period. Feed Forward Neural Network is a non-parametric method that can be used to make predictions from time series data without having to be bound by classical assumptions. The initiated weight will be evaluated by an algorithm that can minimize errors. Particle Swarm Optimization (PSO) is an optimization algorithm based on particle speed to reach the destination. The FFNN model will be combined with PSO to get predictive results that are close to the target. The best architecture on FFNN is obtained with 2 units of input, 1 unit of bias, 3 hidden units, and 1 unit of output by producing MAPE training 11.7735% and MAPE testing 8.14%. According to Lewis (1982) in Moreno et. al (2013), the MAPE value below 10% is highly accurate forecasting. Keywords: Feed Forward Neural Network (FFNN), Particle Swarm Optimization (PSO), neurons, weights, predictions.
ANALISIS ANTREAN BUS NONPATAS JALUR TIMUR TERMINAL TIRTONADI KOTA SURAKARTA MENGGUNAKAN METODE BAYESIAN Rizka Nur Faizah; Sugito Sugito; Sudarno Sudarno
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.v11i1.33993

Abstract

The queuing system relates to customers and service facilities. Queuing theory designs service facilities to address service requests. Queues occur if the service capacity is not sufficient to provide services to many customers. The queuing phenomenon occurs on non-patas buses on the eastern route of Tirtonadi Terminal, Surakarta with Surabaya, Karanganyar, Wonogiri, Purwodadi and Pedesaan buses. The Bayesian method combines information from current research and previous studies with similar cases, and produces a posterior distribution to form a queuing system model and measure of service system performance. The bus queuing system model for Surabaya, Karanganyar, Wonogiri and Purwodadi has a Gamma-distributed arrival and service pattern. Pedesaan buses has an arrival pattern with a Gamma distribution and a service pattern with an Inverse Gamma distribution. Each line has 1 bus line as a service system, FIFO queue discipline, the number of customer capacity and call sources is not limited. The Surabaya buses has the highest probability of 93.49% that the line is idle and the Pedesaan buses  has the highest probability that the line will be busy serving at 89.50%. The queuing system are considered good because the five lines of service facilities are able to meet customer needs. Keywords: Tirtonadi Terminal, Bayesian, Posterior Distribution, Queue Models, System Performance Measures
PEMODELAN REGRESI RIDGE ROBUST S,M, MM-ESTIMATOR DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi Kemiskinan di Jawa Tengah Tahun 2020) Anggun Perdana Aji Pangesti; Sugito Sugito; Hasbi Yasin
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.v10i3.32799

Abstract

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linier regression parameters. If there is a violation of assumptions such as multicolliniearity especially coupled with the outliers, then the regression with OLS is no longer used. One method can be used to solved the multicollinearity and outliers problem is Ridge Robust Regression.  Ridge Robust Regression is a modification of ridge regression method used to solve the multicolliniearity and using some estimators of robust regression used to solve the outlier, the estimator including : Maximum likelihood estimator (M-estimator), Scale estimator (S-estimator), and Method of moment estimator (MM-estimator). The case study can be used with this method is data with multicollinearity and outlier, the case study in this research is poverty in Central Java 2020 influenced by life expentancy, unemployment number, GRDP rate, dependency ratio, human development index, the precentage of population over 15 years of age with the highest education in primary school, mean years school. The result of estimation using OLS show that there is a multicollinearity and presence an outliers. Applied the ridge robust regression to case study prove that ridge robust regression can improve parameter estimation. The best ridge robust regression model is Ridge Robust Regression S-Estimator. The influence value of predictor variabels to poverty is 73,08% and the MSE value is 0,00791. 
PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH Riski Arum Pitaloka; Sugito Sugito; Rita Rahmawati
Jurnal Gaussian Vol 8, No 2 (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 (574.446 KB) | DOI: 10.14710/j.gauss.v8i2.26648

Abstract

Import is activities to enter goods into the territory of a country, both commercial and non-commercial include goods that will be processed domestically. Import is an important requirement for industry in Central Java. The increase in high import values can cause deficit in the trade balance. Appropriate information about the projected amount of imports is needed so that the government can anticipate a high increase in imports through several policies that can be done. The forecasting method that can be used is ARIMA Box-Jenkins. The development of modeling in the field of time series forecasting shows that forecasting accuracy increases if it results from the merging of several models called ensemble ARIMA. The ensemble method used is averaging and stacking. The data used are monthly import value data in Central Java from January 2010 to December 2018. Modeling time series with Box-Jenkins ARIMA produces two significant models, namely ARIMA (2,1,0) and ARIMA (0,1,1). Both models are combined using the ARIMA ensemble averaging and stacking method. The best model chosen from the ARIMA method and ensemble ARIMA based on the least RMSE value is the ARIMA model (2,1,0) with RMSE value of 185,8892 Keywords: Import, ARIMA, ARIMA Ensemble, Stacking, Averaging
PEMODELAN SISTEM ANTREAN PELAYANAN BUS JALUR BARAT TERMINAL TIRTONADI KOTA SURAKARTA DENGAN METODE BAYESIAN Nurul Khasanah; Sugito Sugito; Yuciana Wilandari
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.v10i3.32807

Abstract

Tirtonadi is the largest bus station in Surakarta City. The departure line is devided into two lines, namely west line and east line. The west line serves buses to the west of Surakarta City. The number of buses that enter and leave the station every day causes bus queues. Modeling the queue system and analyzing the system performance measure aims to determine wether the bus service system is good or not. The queue system model is obtained by finding the distribution of arrival patterns and service patterns using the Bayesian method. This method is used because it combines the information from the current research and the prior information from the previous research. The queueing condition of the five lanes in the west line meets steady state conditions because the utility value is less than 1. The queue displant is First Come First Service (FCFS) with unlimited customers and unlimited calling sources. Based on the posterior distribution, the queue system of service bus is (GAMM/IG/1):(GD/∞/∞) for Solo-Jakarta-Bandung lane and Pedesaan lane, while for Solo-Purwokerto-Cilacap, Solo-Yogyakarta, and Solo-Semarang has the queue system (GAMM/GAMM/1):(GD/∞/∞). The queue system of service bus for each lane has good services based on the value of system performance measure. 
ANALISIS METODE BAYESIAN PADA SISTEM ANTREAN RAWAT JALAN DI RSUP Dr. KARIADI DENGAN DISTRIBUSI SAMPEL POISSON DAN GEOMETRIK Nur Azizah; Sugito Sugito; Hasbi Yasin
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.v10i3.32801

Abstract

Hospital service facilities cannot be separated from queuing events. Queues are an unavoidable part of life, but they can be minimized with a good system. The purpose of this study was to find out how the queuing system at Dr. Kariadi. Bayesian method is used to combine previous research and this research in order to obtain new information. The sample distribution and prior distribution obtained from previous studies are combined with the sample likelihood function to obtain a posterior distribution. After calculating the posterior distribution, it was found that the queuing model in the outpatient installation at Dr. Kariadi Semarang is (G/G/c): (GD/∞/∞) where each polyclinic has met steady state conditions and the level of busyness is greater than the unemployment rate so that the queuing system at Dr. Kariadi is categorized as good, except in internal medicine poly. 
PENERAPAN SIX SIGMA DALAM RANCANGAN PERCOBAAN FAKTORIAL UNTUK MENENTUKAN SETTING MESIN PRODUKSI AIR MINERAL Muhammad Nugroho Karim Amrulllah; Mustafid Mustafid; 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 | Full PDF (697.572 KB) | DOI: 10.14710/j.gauss.v5i1.11037

Abstract

Machine setting is one of the factors which affects the high defects of mineral water cup. The determination of the optimal machine setting is needed to reduce the defects that occur. Six Sigma DMAIC (define, measure, analyze, improve, control) method in the factorial experimental design can be used for determining the optimal machine setting. This research, which is conducted in PT Sekar Sari, found the most optimal combination of pressure and temperature setting of the machine, so that the defects generated are decreasing after optimal condition treatment. Sigma level increased by 0.89 sigma, from 2,47 sigma to 3,36 sigma and COPQ (cost of poor quality) percentage decreased by 3.64%.Keywords: Six Sigma, DMAIC, Factorial Design of Experiment, COPQ.
METODE BAYESIAN PADA SISTEM ANTREAN PELAYANAN MENGGUNAKAN GUI R (Studi Kasus: Antrean Pelayanan di Kantor Dinas Kependudukan dan Pencatatan Sipil Kota Semarang) Atikah Mufidah; Sugito Sugito; Di Asih I Maruddani
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.v11i1.34002

Abstract

The increase population of Semarang City has given many kinds of problem from births, deaths, marriages and other important events. The change of population identity data causes the number of visitors to the Semarang City Dispendukcapil to increase so that the service system becomes busy. The study aims to determine whether the service system in the Dispendukcapil is good or not. This can be known by determining the distribution of arrival patterns and service patterns to obtain a queuing system model and system performance measures. In this study, the distribution of arrival patterns and service patterns is determined by finding the posterior distribution using the Bayesian method. The Bayesian method was chosen because it is able to combine the distribution of the sample in the current study with previous information for the same case. Posterior distribution can be obtained if it has elements, namely prior distribution and likelihood function. The distribution of arrival patterns and service patterns obtained from prior information, follows the Discrete Uniform and Log-Normal distribution. Based on the calculation and analysis of the posterior distribution, the service system model of the Dispendukcapil Semarang City is obtained, namely for the Customer Service counter, and  for the legalization counter and the population document service counter with a good service system.Keywords:Population, Dispendukcapil Semarang City, queue, Bayesian, prior distribution, posterior distribution, queuing system model, Beta, Gamma, Inverse Gamma.
PENENTUAN MODEL ANTREAN NON-POISSON DAN PENGUKURAN KINERJA PELAYANAN BUS RAPID TRANSIT TRANS SEMARANG (STUDI KASUS: SHELTER PEMBERANGKATAN BRT KORIDOR V) Purwati Ayuningtyas; Sugito Sugito; Di Asih I Maruddani
Jurnal Gaussian Vol 10, No 1 (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.v10i1.30932

Abstract

One of the queue systems that is often found  in daily life is the transportation service system, for example a queue system at the shelters departure of corridor V Bus Rapid Transit (BRT) Trans Semarang. Corridor V has three departure shelters, they are Shelter Victoria Residence, Shelter Marina, and Shelter Bandara Ahmad Yani. Corridor V was choosen, because of its high load factor on January to June 2019. Based on the observation, the service time at the departure shelter is usually longer than the normal shelter. This causes the rise of queue at the departure shelters. The queue at the departure shelters can hamper the arrival of BRT at the other shelters, so the application of the queue theory is needed to find out the extent of operational effectiveness at the departure shelters. The resulting queue model is the Non-Poisson queue model, the queue model for Victoria Residence Shelter: (DAGUM/GEV/1):(GD/∞/∞), Marina Shelter: (DAGUM/G/1):(GD/∞/∞), and Bandara Ahmad Yani Shelter: (GEV/GEV/1):(GD/∞/∞). Based on the value from measurement of the queue system performance, it can be conclude that the three departure shelters of corridor V BRT Trans Semarang have some optimal condition. Keywords: Shelter Departure of Corridor V, Non-Poisson Queueing Model, Dagum, Generalized Extreme Value, System Perfomance Measure  
ANALISIS MODEL ANTREAN NON-POISSON DAN UKURAN KINERJA SISTEM BERBASIS GUI WEB INTERAKTIF MENGGUNAKAN R-SHINY (Studi Kasus: Bus di Terminal Penggaron Kota Semarang) Devi Wijayanti; Sugito Sugito; Hasbi Yasin
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.v9i4.29010

Abstract

Since September 1, 2018, The Semarang City Government has diverted intercity bus stop within the province from Terboyo Terminal to Penggaron Terminal, resulting in an imbalance of movement and capacity of the Penggaron Terminal which causes queue of bus. Non-Poisson queue is a queue model in which the arrival and service distribution do not have a Poisson distribution or do not have an Exponential distribution. The study was conducted on buses entering the Penggaron Bus Station with the destination of Jepara, Kedungjati, Juwangi, Yogyakarta, Kudus/Pati/Lasem, Pekalongan/Tegal, and Purwokerto/Purworejo. The main goal of this project is to identify the queue model of Non-Poisson and calculate the measure of system performance using the GUI R. One of the programs in R that can create an interactive web-based GUI (Graphical User Interface) is R-Shiny. R-Shiny is a toolkit of R programs that can be used to create online programs. The distribution test obtained using the EasyFit program. The bus queue model of Jepara is (DAGUM/GEV/4):(GD/∞/∞), the queue model of Kedungjati is (GPD/ DAGUM/1):(GD/∞/∞), the queue model of Juwangi is (GEV/ GEV/1):(GD/∞/∞), the queue model of Yogyakarta is (DAGUM/ DAGUM/1) : (GD/∞/∞), the queue model of Kudus/Pati/Lasem is (DAGUM/GEV/1):(GD/∞/∞), the queue model of Pekalongan/Tegal is (GEV/DAGUM/1):(GD/∞/∞), and the queue model of Purwokerto/Purworejo is (GPD/DAGUM/1) : (GD/∞/∞). 
Co-Authors A. Haris A. Rusgiyono Abdul Hoyyi Affan Hanafaie Agus Rusgiyono Ajeng Dwi Rizkia Alan Prahutama Alin Citra Suardi Anggun Perdana Aji Pangesti Arief Rachman Hakim Arief Rachman Hakim Arnelli Arnelli Atikah Mufidah Aurum Anisa Salsabela B. Raharjo Budi Warsito D. Ispriyanti Dedi Baleo Pasaribu Devi Wijayanti Di Asih I Maruddani Di Mokhammad Hakim Ilmawan Didik Setiyo Widodo Dini Febriani Diyah Rahayu Ningsih Dwi Ispriyanti E. Setiawati Eny Sulistyowati Erna Fransisca Angela Sihotang F. Ariyanto Faisal Fikri Utama Gandhes Linggar Winanti H. Sutanto Hasbi Yasin I. Marhaendrajaya J. E. Suseno Juwanda, Farikhin Lifana Nugraeni M. Izzati M. Nur Moch. Abdul Mukid Muhammad Nugroho Karim Amrulllah Mustafid Mustafid Nur Azizah Nurina Salma Alfiyyah Nurul Khasanah Priyono Priyono Purwati Ayuningtyas R. Hariyati R. Hastuti Radian Lukman Riski Arum Pitaloka Rita Rahmawati Rizka Nur Faizah Rizki Faizatun Nisa Ryan Anugrah S. Tana Shella Faiz Rohmana Silvia Julietty Sinaga Sri Sumiyati Sudarno Sudarno Suhartono Suhartono Suparti Suparti T. Windarti Tarno Tarno Tatik Widiharih Triastuti Wuryandari Trisnawati Gusnawita Berutu W. H. Rahmanto Yuciana Wilandari Z. Muhlisin