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All Journal Statistika Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Prosiding Seminar Nasional Unimus

Budi Warsito

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Author-ID : 2977118

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

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PERAMALAN PENUMPANG PELAYARAN DALAM NEGERI DI PELABUHAN TANJUNG PRIOK DENGAN METODE ARIMA BOX-JENKINS DAN METODE VARIASI KALENDER ARIMAX Annisa Pratiwi; Diah Safitri; Budi Warsito
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 1 (2018): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Sea transportation is an inseparable and indispensable part of society in everyday lifefor Indonesian people especially during special moment such as Eid al-Fitr. This can be shown by the increasing of the number of sea transport passengers duringEid al-Fitr every year. The month shift during Eid al-Fitr shows the effect of calendar variation.The calendar variation method is a method that combines the dummy regression model with the ARIMA model. The purpose of this research is to obtain the best model by using time series analysis approach on ARIMA Box-Jenkins method and calendar variationARIMAX method to predict the number of domestic sea passenger at Tanjung Priok Port for 12 periods in the future. Based on the analysison the data of the number of domestic sea passenger at the Port of Tanjung Priok, it is concluded that the method of calendar variationARIMAX as the best method with ARIMA model (0, 0, [3]), V2,t,S1,t, S2,t, S3,t, S4,t, S5,t, S6,t, S7,t, S8,t, S9,t, S10,t, S11,t,t, V1,tt, V2,tt, S7,tt,S8,tt, S9,ttbecause it has the smallest MAPE value that is 14.3782% which indicates that the result of forecasting is good. Keywords : Sea Passengers, ARIMA Box-Jenkins, Calendar Variation, ARIMAX

MODEL STOKHASTIK ANTRIAN NON POISSON PADA PELAYANAN PERBANKAN Sugito -; Alan Prahutama; Budi Warsito; Moch Abdul Mukid; Nia Puspita Sari
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 5, No 1 (2017): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Suatu proses secara umum terpisahkan menjadi dua jenis yaitu proses deterministik dan proses stokhastik. Pada proses stokastik bisa digolongkan menjadi 4 macam yaitu proses stokhastik dengan waktu diskrit dan ruang state diskrit, proses stokastik dengan waktu diskrit dan ruang state kontinu, proses stokastik dengan waktu kontinu dan ruang state kontinu serta proses stokastik dengan waktu kontinu dan ruang state diskrit. Proses stokhastik dengan ruang state diskrit dan waktu kontinu merupakan model matematik yang banyak dijumpai dalam kehidupan sehari-hari. Secara matematik bentuk proses stokhastik yang seperti ini di antaranya adalah proses poisson. Dalam tulisan ini akan dibahas proses poisson antrian yaitu secara spesifik proses stokhastik antrian non poisson. Proses stokhastik antrian non poisson adalah merupakan model antrian (a,b,c)/(d,e,f) dimana notasi a dan b nya tidak berdistribusi poisson ataupun tidak berdistribusi eksponensial. Secara spesifik pada tulisan ini model antrian non poissonnya adalah model antrian (M/G/c) : (GD/ dan Model antrian (G/G/c) : (GD/∞/∞). Untuk aplikasi model antrian non poisson ini diterapkan pada antrian teller di suatu bank di jawa barat. Sehingga diperoleh dua model antrian non poisson yaitu model antrian (M/G/3) : (GD/ dan model (G/G/c) : (GD/ .Kata Kunci : Stokhastik, Antrian, Non Poisson, Teller

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

Co-Authors Abdul Hoyyi Alan Prahutama Anggita Puri Savitri Anisa Septi Rahmawati Annisa Pratiwi Arief Rachman Hakim Cindy Wahyu Elvitra David Yuliandar Di Asih I Maruddani Diah Safitri Dwi Ispriyanti Endang Purbowati Hasbi Yasin Hayuk Permatasari 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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