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Jurnal Gaussian

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Published by Universitas Diponegoro

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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.

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Search results for , issue "Vol 9, No 2 (2020): Jurnal Gaussian" : 10 Documents clear

PENERAPAN ARTIFICIAL NEURAL NETWORK DENGAN OPTIMASI MODIFIED ARTIFICIAL BEE COLONY UNTUK MERAMALKAN HARGA BITCOIN TERHADAP RUPIAH Di Mokhammad Hakim Ilmawan; Budi Warsito; Sugito Sugito
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bitcoin is one of digital assets that can be used to make a profit. One of the ways to use Bitcoin profitly is to trade Bitcoin. At trade activities, decisions making whether to buy or not are very crucial. If we can predict the price of Bitcoin in the future period, we can make a decisions whether to buy Bitcoin or not. Artificial Neural Network can be used to predict Bitcoin price data which is time series data. There are many learning algorithm in Artificial Neural Network, Modified Artificial Bee Colony is one of optimization algorithm that used to solve the optimal weight of Artificial Neural Network. In this study, the Bitcoin exchage rate against Rupiah starting September 1, 2017 to January 4, 2019 are used. Based on the training results obtained that MAPE value is 3,12% and the testing results obtained that MAPE value is 2,02%. This represent that the prediction results from Artificial Neural Network optimized by Modified Artificial Bee Colony algorithm are quite accurate because of small MAPE value.

PENYUSUNAN DAN PENERAPAN METODE REGRESSION ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (RANFIS) UNTUK ANALISIS DATA KURS IDR/USD Lamik Nabil Mu'affa; Tarno Tarno; Suparti Suparti
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The exchange rate of rupiah is one of the important prices in an open economy because the exchange rate can be used as a tool to measure the economic condition of a country. The movement of the rupiah exchange rate affected the Indonesian economy, maintaining the stability of the rupiah exchange rate became an important thing to do. In an effort to maintain the stability of the rupiah exchange rate, the factors that influence it must first be identified. Several factors affect the IDR / USD exchange rate, namely the large trade price index, foreign exchange reserves, money supply and interest rates. In this study, the Regression Adaptive Neuro Fuzzy Inference System (RANFIS) method was used to analyze the effect of predictor variables on IDR / USD exchange rates. The optimal RANFIS model is strongly influenced by three things, namely the determination of input predictor variable, membership functions, and number of clusters. Determination of the optimal RANFIS model is measured based on the smallest MAPE in-sample. Based on empirical studies applied to predictor variables on IDR / USD exchange rates, it was found that the RANFIS model was optimal, namely with 3 predictor variable inputs consisting of large trade price index variables, money supply and interest rates; with the gauss membership function; 2 clusters and rules produce an MAPE in-sample of 1.93% and an MAPE out-sample of 2.68%, so the performance of the RANFIS model has a very good level of accuracy.

ANALISIS MULTIRESOLUSI WAVELET DENGAN TRANSFORMASI WAVELET DISKRIT BERBASIS GUI R (STUDI KASUS: INFLASI DI INDONESIA PADA PERIODE OKTOBER 2007-MEI 2018) Sania Anisa Farah; Suparti Suparti; Dwi Ispriyanti
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Lately, the wavelet applications are widely used in statistics, one of them is discrete wavelet transform (DWT) which is a non-parametric method for signal analysis, data compression, and time series analysis. As technology becomes more advanced, a software is necessary to support the statistical analysis by such method, one of them being the open source based R. It is often used in statistical computing with command line interface (CLI) which requires the R user to remember the names of syntaxes and functions. It becomes less effective when there are many related statistical analysis involved, so graphical user interface (GUI) is needed to access all of them easily. The testing of multiresolution analysis by DWT for Haar, Daublets, and Coiflets filters with levels 1-6 had been performed by using the inflation data in Indonesia during October 2007-May 2018 taken from Bank Indonesia website. The result shows that the sixth level of DWT gives the best estimation for each filters, and Daublets 20 is the best filter for overall estimation with MSE, MAPE, and MASE values are 0.05755, 3.40678, and 0.35343 respectively. The packages for GUI construction in R are wavelets and shiny. Based on its usage, the GUI is capable of processing the chosen analysis and showing the valid output.

VECTOR AUTOREGRESSIVE STABILITY CONDITION CHECK UNTUK PEMODELAN DAN PREDIKSI SUMBER PENERIMAAN PABEAN BELAWAN Mia Anastasia Sinulingga; Di Asih I Maruddani; Abdul Hoyyi
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Customs Intermediate are an institution that is responsible for regulating the flow of export and import trade activities in the Customs Area with the revenue coming from import duties and export duties. The time series data from the customs acceptance component import dan export which have a relationship between variables. Vector Autoregressive is a statistical method used in predicting and evaluating interrelationships between variables. The purpose of this study is to obtain a model for predicting import and export by using the VAR model and detecting the stability of the model. Model requirements are said to be stable if all modulus values from roots characteristic of coefficient matrices ≤ 1 that the predicted results can be verified. The data is divided into in sample data starting from January 2010 to June 2018 and out sample data starts from July 2018 until December 2018. The results of data analysis in this study, the model obtained for prediction is the VAR model (4) and there is a direct relationship between both variables. The VAR (4) residual model fulfills the assumption of white noise, while the assumption of multivariate normality is not fulfilled. Based on out sample the value of MAPE for import variables 18.42%, export 12.94% shows the VAR model (4) has good predictive capabilities that can be used for predicting future periods. Predicted results on import show fluctuations during the period of January to December 2019 while in the export shows increase during the period of January to December 2019.

PERAMALAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA DI KEPULAUAN RIAU DENGAN MENGGUNAKAN MODEL FUNGSI TRANSFER Tamura Rolasnirohatta Siahaan; Rukun Santoso; Alan Prahutama
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Transfer function models is a data analysis model that combines time series and causal approach, in another words, transfer function models is a method that ilustrates that the predicted value in teh future is affected by the past value time series and based on one or more related time series. In this research, an analysis of the number of tourist arrival and rainfall in several regions in Kepulauan Riau from January 2013 until December 2017 was aimed at obtaining a transfer function model and forecasting the number of tourist arrival in several regions of the Kepulauan Riau for next periods. Based on the result of the analysis, rainfall in Tanjung Pinang does not affect the visit of tourist with the values of MAPE is 13,63494%. Rainfall in Batam also does not affect the visit of tourist with the values of MAPE is 7,977151%. While in Tanjung Balai Karimun, tourist arrivals was affected by rainfall with the values of MAPE is 10,32777%.

PEMODELAN JUB DAN BI RATE TERHADAP INFLASI DAN KURS RUPIAH MENGGUNAKAN REGRESI SEMIPARAMETRIK BIRESPON BERDASARKAN ESTIMATOR PENALIZED SPLINE Siti Fadhilla Femadiyanti; Suparti Suparti; Budi Warsito
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Some indicators of the Indonesian economy are inflation and the exchange rate of rupiah against US dollar. Inflation and the rupiah exchange rate are thought to be influenced by the money supply (JUB) and the BI Rate. The money supply has a nonparametric relationship pattern to inflation and the rupiah exchange rate, while the BI Rate has a parametric relationship pattern to inflation and the rupiah exchange rate. The right method for detecting the relationship between inflation and the exchange rate with JUB and BI Rate is birespon semiparametric regression with a splined penalized estimator. The semiparametric regression coefficient of birespon spline penalized is estimated using the Weighted Least square (WLS) method which is determined based on the degree of polynomials, the number and location of the optimal knot points, and the optimal lambda determined based on the minimum of Generalized Cross Validation (GCV). This research uses the R Program. Based on the results of the analysis, the best spline penalized birespon semiparametric regression model is located in the number of knots is 5 at the knot points of 5257,783; 6649,469; 8976,871; 11099,19 and 13535,51 found in the first degree of response is 1 and the second degree of response is 2 with an optimal lambda of 99,99. The results of the performance evaluation of the model produce value of is 99,9007%, meaning that the model's performance is very good for out samples of the data and the MAPE value of 2.89169% is less than 10% which means the model's performance is very good.

SEGMENTASI PELANGGAN E-MONEY DENGAN MENGGUNAKAN ALGORITMA DBSCAN (DENSITY BASED SPATIAL CLUSTERING APPLICATIONS WITH NOISE) DI PROVINSI DKI JAKARTA Windy Rohalidyawati; Rita Rahmawati; Mustafid Mustafid
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Customer segmentation is one effective way of marketing to determine the most potential target market. Increasing of E-money usage in DKI Jakarta and more banks are providing E-money products. One way to be able to compete in the global market, banks can segment customers. Determining potential customers of E-money users in DKI Jakarta can form segments by applying the DBSCAN (Density Based Spatial Clustering Application with Noise) algorithm. The quality of segments was measured by using the Silhouette Coefficient. In this study, E-money customers were grouped by reason of using the bank used, transaction activities, number of transactions, nominal balance, and frequency of top-up. The results of this study were using the density radius of 2 and minimum 3 objects that enter the density radius forming 2 segments and 17 noises. The segment quality value of 0.26. The most potential segment was the segment that has an average greater than the average of all data.

PEMODELAN WAVELET NEURAL NETWORK UNTUK PREDIKSI NILAI TUKAR RUPIAH TERHADAP DOLAR AS Tri Yani Elisabeth Nababan; Budi Warsito; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Each country has its own currency that is used as a tool of exchange rate valid in the transaction process. In the process of transaction between countries often experience problems in terms of payment because of the difference in the value of money prevailing in each country. The price movement of the exchange rate or the value of foreign currencies that fluctuate from time to time it encouraged predictions of the value of the rupiah exchange rate against the U.S. dollar. Wavelet Neural Network (WNN) is a combination of methods between wavelet transforms and Neural networks. WNN modeling begins with wavelet decomposition resulting in wavelet coefficients and scale coefficients. Selection of inputs is based on PACF plots and divides into training data and testing data. To determine the final output by calculating the value of MAPE in data testing. The best architecture on WNN model for prediction of the value of the rupiah exchange rate against the U.S. dollar is a model with sigmoid logistic activation function, 2 neurons in the input layer, 10 neurons in the hidden layer, and 1 neuron in the output layer. The MAPE value is obtained at 0.2221%.

PENERAPAN STRUCTURAL EQUATION MODELLING (SEM) UNTUK MENGANALISIS FAKTOR – FAKTOR YANG MEMPENGARUHI KINERJA BISNIS (STUDI KASUS KAFE DI KECAMATAN TEMBALANG DAN KECAMATAN BANYUMANIK PADA JANUARI 2019) Ade Irma Pramudita; Tatik Widiharih; Rukun Santoso
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This research is done to examine the effect of quality of service and product attractiveness toward business strategies based on service in order to improving business performance. The sample of this study were Cafe owners in Tembalang Subdistrict and Banyumanik Subdistrict, total are 116 respondents. In this Final Project, the processing of Structural Equation Modeling (SEM) is AMOS software. The results of the analysis show that service quality has a positive effect on business strategies based on service to improving business performance. The most significant factor that affecting business performance is quality of service. Quality of service is important in the performance of a café business. Cafe owners must always pay attention to the quality of café service to customers, because the quality of service is the main consideration for customers to visit cafes.

PERAMALAN HARGA CABAI MERAH MENGGUNAKAN MODEL VARIASI KALENDER REGARIMA DENGAN MOVING HOLIDAY EFFECT (STUDI KASUS: HARGA CABAI MERAH PERIODE JANUARI 2012 SAMPAI DENGAN DESEMBER 2019 DI PROVINSI JAWA BARAT) Aulia Rahmatun Nisa; Tarno Tarno; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Chili is one of the vegetable commodities that has high economic value, because of it’s role is large enough to supply domestic needs as an export commodity in the food industry. The price of red chilliesalways increase in the month of Eid al-Fitr. This is due to the large number of people who use Red Chili as food they consume. Shifting the moon during the Eid al-Fitr will form a seasonal system with different periods, which became known as the Moving Holiday Effect. One of the calendar variation models used to eliminate the Moving Holiday Effect and has a simple processing flow is the RegARIMA model. The RegARIMA model is a combination of linear regression with ARIMA. In the regression model the weighting matrix is used as an independent variable and the price of red chili as the dependent variable. The weight value is obtained based on the number of days that affect Eid, which is 14 days. Based on the analysis the red chili price data in West Java Province with the period of January 2012 to December 2018, the RegARIMA model (1.0,0)(0,1,1) 12 is the best model because it has the smallest AIC. Forecasting results in 2020 showed an increase in the price of red chili in West Java occurred in May to coincide with the Eid al-Fitr holiday which fell on May 24, 2020, the sMAPE value obtained by 24.96%. It means, the forecast still in the level of reasonableness.

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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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Jawa tengah

INDONESIA