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All Journal Statistika Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Sains dan Teknologi JURNAL INSTEK (Informatika Sains dan Teknologi) Prosiding Seminar Nasional Unimus

Budi Warsito

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Author-ID : 2977118

Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Nursing Public Health Other

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PEMODELAN KASUS KEMISKINAN DI JAWA TENGAH MENGGUNAKAN REGRESI NONPARAMETRIK METODE B-SPLINE Anisa Septi Rahmawati; Dwi Ispriyanti; Budi Warsito
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Poverty is one of the diseases in the economy, so it must be cured or at least reduced. According to BPS (2016), poor people are people who have an average expenditure per capita per month below the poverty line. The poverty line in Central Java in 2016 amounted to Rp 317 348, - per capita per month. In 2016, the average level of poverty in the Java Island, Central Java province placed as the second highest after DIY. Many factors are thought to affect the level of poverty. In this study, the predictor variables used are the rate of economic growth (X1), unemployment rate (X2), and education level above high school to (X3). This study aims to obtain a model of the relationship between the factors that affect poverty on the percentage of poor and calculate the predictions. The method used is B-spline nonparametric regression. Nonparametric approach are used if the function of previous data is unknown. The best B-spline model depends on the determination of the optimal knots point having a minimum Generalized Cross Validation (GCV). In this study, the best B-spline model obtained when the order of X1is 2, the order of X2 is 2, and the order of X3 is 2. The knots obtained in X1 at the point 4,51273, X2 at the point 3,60626, and X3 at point 11,4129 and 16,2481 with GCV value of 9,79353. Keywords: Poverty, Nonparametric Regression, B-Spline, Generalized Cross Validation

PEMODELAN TINGKAT INFLASI INDONESIA MENGGUNAKAN MARKOV SWITCHING AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY Omy Wahyudi; Budi Warsito; Alan Prahutama
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The financial sector often under conditions of fluctuating due to changes in monetary policy, the political instability even just a rumor. The linear model cannot capture changes in these conditions, so the model used is Markov Switching Autoregressive Conditional Heteroskedasticity (SWARCH). This model produces value of transition probability and the duration of each state. Filtering and smoothing process performed to determine probability of the observation data in each state. Modeling about the inflation data in Indonesia was done. The model used is SWARCH (2.1) with 240 data. The probability of inflation rate switch from non crisis state to crisis state is 0.016621, while the probability of inflation rate switch from crisis state to non crisis state is 0.195719. Expectation value of the length time in non crisis state is 60.16 days and the crisis state is 5.11 days.Keywords : filtering, smoothing, transition probability, SWARCH

PEMODELAN MARKOV SWITCHING VECTOR AUTOREGRESSIVE (MSVAR) Hayuk Permatasari; Budi Warsito; Sugito Sugito
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Economic and financial variables are variables that are fluctuated because of regime switching as a result of political and economical conditions. Linear modeling can not capture the regime switching, so it is better to use Markov Switching Vector Autoregressive Models (MSVAR). MSVAR is a combination of vector autoregressive models and hidden markov models. Daily return of Rupiah buying rate against the USD and Euro are economic variables that are fluctuated and they can explain economic condition of a country. The best model of five order iteration is MS (2) - VAR (4) with the smallest AIC value, that is -1460.48. Maximum Likelihood Estimation is a method to get parameters estimation. With 73 data, the return rates has transition probability 0.08 from crisis to normal state, while the transisition probablity of the opposite condition is 0.6. Expected value being at normal state is 13.10 days and being at crisis state is 1,68 days.

PEMODELAN MARKOV SWITCHING DENGAN TIME-VARYING TRANSITION PROBABILITY Anggita Puri Savitri; Budi Warsito; Rita Rahmawati
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Exchange rate or currency is an economic variable which reflects country’s state of economy. It fluctuates over time because of its ability to switch the condition or regime caused by economic and political factors. The changes in the exchange rate are depreciation and appreciation. Therefore, it could be modeled using Markov Switching with Time-Varying Transition Probability which observe the conditional changes and use information variable. From this model, time-varying transition probability and expected durations are obtained; both are very useful to explain economic growth better and more detailed. This research modeled ln return value of Indonesian Rupiah to U.S Dollars and using ln return value of Indonesian Rupiah to Euro as information variable. The best model is MS(2) – AR(1). Overall, the mean of transition probability from appreciation to depreciation is 0,025242 and the transition probability from depreciation to appreciation is 0,666369. Expected duration of appreciation is 39,61623 days meanwhile the expected duration of depreciation is 39,18689 days. Keywords : regime switching, Markov switching, time-varying, transition probability, expected duration

PENERAPAN TEORI ANTRIAN PADA PELAYANAN TELLER BANK X KANTOR CABANG PEMBANTU PURI SENTRA NIAGA Nia Puspita Sari; Sugito Sugito; Budi Warsito
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bank X Puri Sentra Niaga branch office is one of bank that can not be separated from the queue issue. The customers want a fast and easy service. The length of queueing and the long waiting times may cause customers cancel the transaction and choose another bank. Therefore, it is necessary to define a suitable queueing model of teller service. Bank X Puri Sentra Niaga branch office have two types of teller service namely Antrian 1 and Antrian 2. Queueing model for Antrian 1 is (M/G/1):(GD//). The model describe that the customers arrival distribution is Poisson, the customer service distribution is General, the number of server is 1, the service disipline is FIFO (first in first out), the customers capacity and the resource of customers are infinite. Queueing model for Antrian 2 is (M/M/2):(GD//). The model describe that customers arival distribution and service distribution are Poisson, the number of server is 2, the service disipline is FIFO (first in first out), the customers capacity and the resource of customers are infinite. Software Arena is applied in simulation to compare the measures of performance if the number of teller added.Keywords: Queue, Queueing System Model, Bank, Teller.

PERBANDINGAN METODE K–MEANS DAN SELF ORGANIZING MAP (STUDI KASUS: PENGELOMPOKAN KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA 2015) Rachmah Dewi Kusumah; Budi Warsito; Moch. Abdul Mukid
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Cluster analysis is a process of separating the objects into groups, so that the objects that belong to the same group are similar to each other and different from the other objects in another group. In this study used two method to classify data of district / city in Central Java based on indicators of Human Development Index (HDI) 2015 are K-Means and Self Organizing Map (SOM) with the number of groups as much as two to seven. Furthermore, the results of both methods were compared using the Davies-Bouldin Index (DBI) values to determine which method is better. Based on the research that has been conducted found that the K-Means (K=4) method works better than SOM (K=2) to classify district / city in Central Java based on indicators of Human Development Index (HDI) as evidenced by the value of the Davies-Bouldin Index (DBI) on K-Means (K=4) of 0.786 is smaller than the value at SOM (K=2) Davies-Bouldin Index (DBI) which is equal to 0.893. Keywords: clustering, HDI, K-Means, SOM, DBI

PEMODELAN NEURO-GARCH PADA RETURN NILAI TUKAR RUPIAH TERHADAP DOLLAR AMERIKA Umi Sulistyorini Adi; Budi Warsito; Suparti Suparti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Exchange rate can be defined as the value of a currency against other currencies. Exchange rates always fluctuate all the time. Very high fluctuations and unconstant becoming problem in forecasting where the data changed extremely. Most of economic data have heteroskedasticity characteristic analyzed using (Generalized Autoregressive Conditional Heteroskedasticity) GARCH models. Another model that commonly used as an alternative is Artificial Neural Network (ANN). However, both models have weaknesses. ARIMA models are linear, but the residual probably still contains non-linear relationship, while the ANN model used to non-linear relationship there is difficulty in determining the input. In this research combination of the two models is Neuro-GARCH model, with GARCH model used as input of ANN model. The purpose of this study was determined the best variance model Neuro-GARCH of return exchange rates rupiah against US dollar. The data used is daily return value of the rupiah (IDR) against the US dollar (USD) from August 27th, 2012 to March 31st, 2016. In this research, the mean model obtained is MA (1) and varian model is GARCH (1,1). The best model is Neuro-GARCH (2-10-1) which MSE smaller than the GARCH (1,1). Keywords: exchange rate, return, GARCH, Neuro-GARCH.

ANALISIS INTERVENSI DAN DETEKSI OUTLIER PADA DATA WISATAWAN DOMESTIK (Studi Kasus di Daerah Istimewa Yogyakarta) Lenny Budiarti; Tarno Tarno; Budi Warsito
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The tourist data is very interesting to be studied because the Indonesian tourism sector is an activator of the national economic which is potential to push higher economic growth in the future. Therefore, the forecast about tourist data is very needed for tourism business. The tourist data tend to fluctuate caused by many factors that affect the number of tourists extremely in an area, such as disasters, government regulation, social stability, violence and terrorism. That the extreme data can be assessed using intervention analysis and outlier detection. Intervention model is a time series model that can be used to forecast data consist of intervention of internal and external factors. In the intervention model, there are two kinds of intervention function, i.e., step and pulse functions. Step function is a form of intervention occurred in period of time while the pulse function is a form of intervention occurred only in a certain time. For the outlier detection, there are four types, such as additive outlier (AO), innovational outlier (IO), level shift (LS) and temporary change (TC). As an empirical studies was conducted by the domestic tourists data in Yogyakarta from January 2006 until December 2010 who staying on five-star hotels and motel throughout Yogyakarta. Based on the result of this research, known that the intervention occurred on January 2010 using the pulse function with MSE value 1172. Meanwhile based on the outliers detection, known any five outliers but only four outliers that significant included to the intervention model with MSE value 523,7167. So, the intervention model and outlier detection are chosen as a the best model based on the smallest MSE criterion. Keywords: Domestic tourists, intervention model, pulse function, outlier detection

PELATIHAN FEED FORWARD NEURAL NETWORK MENGGUNAKAN ALGORITMA GENETIKA DENGAN METODE SELEKSI TURNAMEN UNTUK DATA TIME SERIES David Yuliandar; Budi Warsito; Hasbi Yasin
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

ABSTRAK Pemodelan time series seringkali dikaitkan dengan proses peramalan suatu nilai karakteristik tertentu pada periode mendatang. Salah satu metode peramalan yang berkembang saat ini adalah menggunakan artificial neural network atau yang lebih dikenal dengan neural network.Penggunaan neural network dalam peramalan time series dapat menjadi solusi yang baik, namun yang menjadi masalah adalah arsitektur jaringan dan pemilihan metode pelatihan yang tepat. Salah satu pilihan yang mungkin adalah menggunakan algoritma genetika. Algoritma genetika adalah suatu algoritma pencarian stokastik berdasarkan cara kerja melalui mekanisme seleksi alam dan genetik yang bertujuan untuk mendapatkan solusi dari suatu masalah. Algoritma ini dapat digunakan sebagai metode pembelajaran dalam melatih model feed forward neural network. Penerapan algoritma genetika dan neural network untuk peramalan time series bertujuan untuk mendapatkan bobot-bobot yang optimum dengan meminimumkan error. Dari hasil pelatihan dan pengujian pada data kurs Dolar Australia terhadap Rupiah didapatkan nilai RMSE sebesar 117.3599 dan 82.4917. Model ini baik untuk digunakan karena memberikan hasil prediksi yang cukup akurat yang ditunjukkan oleh kedekatan target dengan output.

METODE PERAMALAN DENGAN MENGGUNAKAN MODEL VOLATILITAS ASYMMETRIC POWER ARCH (APARCH) Cindy Wahyu Elvitra; Budi Warsito; Abdul Hoyyi
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Exchange rate can be defined as a ratio the value of currency. The exchange rate shows a currency price, if it exchanged with another currency. Exchange rates of a currency fluctuate all the time. Rise and fall exchange rates of a currency in the money market shows the magnitude of volatility occurred in a country currency to other's. To estimate the volatility behavior of the data gave rise to volatility clustering or heteroscedasticity problems, can’t be modeled using ARMA model and asymmetric effects that can‘t be modeled by ARCH or GARCH, can be modeled by Asymmetric Power ARCH (APARCH). In determining the estimated parameter values of APARCH model, used the maximum likelihood method, followed by using the iteration method is Berndt, Hall, Hall and Hausman (BHHH). The APARCH model used to the data return of exchange rate against dollar is APARCH(2,1) or in the form as follows : = 0,00000268 + 0,830902 + 0,130516 + 0,074784 + 0,151157

Co-Authors Abdul Hoyyi Alan Prahutama Anggita Puri Savitri Anisa Septi Rahmawati Annisa Pratiwi Arief Rachman Hakim Aris Sugiharto Cindy Wahyu Elvitra David Yuliandar Di Asih I Maruddani Diah Safitri DONNY IRAWAN MUSTABA Dwi Ispriyanti Endang Purbowati Fajar Hari Prasetyo Hasbi Yasin Hayuk Permatasari Juwanda, Farikhin Lenny Budiarti Moch Abdul Mukid Moch. Abdul Mukid MUHAMMAD ARIFIN Nia Puspita Sari Omy Wahyudi Rachmah Dewi Kusumah Retza Bahtiar Anugrah Rita Rahmawati Sri Sumiyati Sudarno Sudarno Sugito - Sugito Sugito Suparti Suparti Tarno Tarno Umi Sulistyorini Adi Yunita Pipiet Sugandhi

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