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Jurnal Gaussian

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Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

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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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Articles 733 Documents

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ANALISIS REGRESI LINIER PIECEWISE DUA SEGMEN Syilfi Syilfi; Dwi Ispriyanti; Diah Safitri
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical method that is widely used in research. In general, the regression analysis is the study of the relationship of one or more independent variables with the dependent variable. In analyze the functional relationship between X as the independent variables and Y as the dependent variable, there may be a linear relationship is different for each interval X. If the regression of X on Y has a linear relationship on the certain of the interval of X, but also has a distinct linear relationship at another interval of X, so the use of piecewise linear regression is appropriate in this case. Piecewise linear regression is a method in regression analysis that divided the independent variable into several segments based on a particular value called the X-knots, and in each segment of the data contained linear regression model. X-knot is a value on the independent variable, where X is the current value of the X-knots, it will form a linear regression equation of the line that is different than the current value of X is under X-knots. Piecewise linear regression can be applied in many fields, one of them in the waters of the analysis regarding the influence of river discharge on the basis of the number of transport sediman. By comparison MSE simple linear regression and multiple linear piecewise two segments, the result that the two segments piecewise linear regression is a model that describes the influence of river discharge on the basis of the number of bedload transport

PEMODELAN HARGA SAHAM DENGAN GEOMETRIC BROWNIAN MOTION DAN VALUE AT RISK PT CIPUTRA DEVELOPMENT Tbk Trimono Trimono; Di Asih I Maruddani; Dwi Ispriyanti
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Financial sector investment is an activity that attracts a lot of public interest. One of them is investing funds in purchasing company’s shares. Profit received from stock investment activity can be seen from the value of stock returns. While, if the previous stock returns Normal distribution, the future stock price can be predicted by Geometric Brownian Motion Method. Based on the stock price prediction, can also be measured an estimated value of the investment risk. The result of data processing shows that the stock price prediction of PT. Ciputra Development Tbk period December 1, 2016 untuk January 31, 2017, has very good accuracy, based on the value of MAPE 1.98191%. Further, Value at Risk Method of Monte Carlo Simulation with α = 5% significance level was used to measure the share investment risk of PT.Ciputra Development Tbk. Thus, this method is only useful if it can be used to predict accurately. Therefore, backtesting is needed. Based on the processing obtained data, backtesting generates the value of violation ratio at 0, it means that at significance level α = 5%, Value at Risk Method of Monte Carlo Simulation can be used at all levels of probability violation.. Keywords : Geometric Brownian Motion, Risk, Value at Risk, Backtesting

ANALISIS SISTEM ANTRIAN PELAYANAN NASABAH BANK X KANTOR WILAYAH SEMARANG Prizka Rismawati Arum; Sugito Sugito; Yuciana Wilandari
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Waiting is very boring for many people because it will only waste a lot of their time. This situation is common happen in a queue, for example customers who will conduct the transaction in the bank. Bank X Semarang Regional Office is the largest branch of Bank X is in Semarang is also not free from this problem. Therefore, the queuing model search is very important in order to improve the quality of service to customers / clients. Based on the analysis of data in the Customer Service and Teller obtained the appropriate queuing models which, for Customer Service and Public Teller queuing model is (M / M / 6): (GD / ∞ / ∞) queuing model for the Teller Express is (M / M / 2): (GD / ∞ / ∞) and for Special Teller model of the queue is (M / G / 1): (GD / ∞ / ∞). Based on the calculations and analyzes that have been done, it can be concluded that the customer service system to the Customer Service and teller at Bank X Semarang Regional Office has been good. Keywords: Queue, Queuing System Model, Bank, Customer Service, Teller.

PERAMALAN DINAMIS PRODUKSI PADI DI JAWA TENGAH MENGGUNAKAN METODE KOYCK DAN ALMON Firdha Rahmatika Pratami; Sudarno Sudarno; Dwi Ispriyanti
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Paddy is one of the staple crops that have strategic value and has a great influence in economic, environmental, social and political. Almost of Indonesia's population consumes rice every day. Because of that, need models to determine or predict the amount of paddy production in Central Java for the future. Because the data used is the historical data, there will be a regression analysis that takes into account the time. If the regression model include not only the value of the independent variable X at this time, but also the value of the past (lagged), this model called a distributed-lag model. The methods used in determining the equation of distributed-lag are Koyck and Almon method. Koyck method used to determine the estimated dynamic model of distributed-lag time difference (lag) is unknown. Almon method used to determine the estimated dynamic model of distributed-lag time difference (lag) is known. Selection of the best model is using Mean Absolut Percentage Error criteria. According the result of the analysis, using Almon model has better result than Koyck Model.Keyword: Paddy, Distributed-lag model, Koyck, Almon

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

PEMODELAN REGRESI ROBUST S-ESTIMATOR UNTUK PENANGANAN PENCILAN MENGGUNAKAN GUI MATLAB (Studi Kasus : Faktor-Faktor yang Mempengaruhi Produksi Ikan Tangkap di Jawa Tengah) Dhea Kurnia Mubyarjati; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multiple Linear Regression can be solved by using the Ordinary Least Squares (OLS). Some classic assumptions must be fulfilled namely normality, homoskedasticity, non-multicollinearity, and non-autocorrelation. However, violations of assumptions can occur due to outliers so the estimator obtained is biased and inefficient. In statistics, robust regression is one of method can be used to deal with outliers. Robust regression has several estimators, one of them is Scale estimator (S-estimator) used in this research. Case for this reasearch is fish production per district / city in Central Java in 2015-2016 which is influenced by the number of fishermen, number of vessels, number of trips, number of fishing units, and number of households / fishing companies. Approximate estimation with the Ordinary Least Squares occur in violation of the assumptions of normality, autocorrelation and homoskedasticity this occurs because there are outliers. Based on the t- test at 5% significance level can be concluded that several predictor variables there are the number of fishermen, the number of ships, the number of trips and the number of fishing units have a significant effect on the variables of fish production. The influence value of predictor variables to fish production is 88,006% and MSE value is 7109,519. GUI Matlab is program for robust regression for S-estimator to make it easier for users to do calculations. Keywords: Ordinary Least Squares (OLS), Outliers, Robust Regression, Fish Production, GUI Matlab.

ALGORITMA ITERATIVE DICHOTOMISER 3 (ID3) UNTUK MENGIDENTIFIKASI DATA REKAM MEDIS (Studi Kasus Penyakit Diabetes Mellitus Di Balai Kesehatan Kementerian Perindustrian, Jakarta) Avia Enggar Tyasti; Dwi Ispriyanti; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Iterative Dichotomiser 3 (ID3) Algorithm is a basic decision tree learning algorithm. These algorithms perform a thorough search (greedy) in all possible decision tree. ID3 algorithm can be implemented using a recursive function, (function that calls itself). One of the problems that can be solved using the ID3 algorithm is a classification of diabetic patients. Diabetic is a disease because of the body is not able to control the amount of sugar or glucose in the bloodstream. Classification using ID3 in the case of diabetics produce trees with many vertices to 32 knot where 21 of them is a leaf node and attribute two-hour postprandial glucose fasting elected as the root node in the decision-making tree. Based on the classification performance measurements show that the classification accuracy or measurement accuracy reaches 89,75%. While the measurement accuracy of the classification algorithm ID3 using test samples totaling 84 samples showed an accuracy of 72,619%. Keywords: ID3 Algortihm, Decision Tree, DiabetesALGORITMA ITERATIVE DICHOTOMISER 3 (ID3) UNTUK MENGIDENTIFIKASI DATA REKAM MEDIS(Studi Kasus Penyakit Diabetes Mellitus Di Balai Kesehatan Kementerian Perindustrian, Jakarta)

PROBABILISTIC NEURAL NETWORK BERBASIS GUI MATLAB UNTUK KLASIFIKASI DATA REKAM MEDIS (Studi Kasus Penyakit Diabetes Melitus di Balai Kesehatan Kementerian Peridustrian Jakarta) Johan Adi Wicaksana; Hasbi Yasin; Sudarno Sudarno
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Neural Network (NN) system is an information-processing that has characteristics similar to the neural network in living beings. A model of Neural Network is used for classification is Probabilistic Neural Network (PNN). PNN structured by four layers, the input layer, layer pattern, the summation layer and output layer. One of classification problems that can be solved by PNN is a classification of Diabetes Mellitus’s status. Diabetes mellitus is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin produced. To facilitate the classification of diabetes mellitus, it is used a software-based Graphical User Interface (GUI) of Matlab to build a software of PNN. GUI that is formed can do PNN classification and predict the status of one’s Diabetes Mellitus. PNN structure that is formed resulting the highest accuracy 0.9143548 on the training process and 0.919512 on the testing process obtained by the percentage of training data than testing data by 90%:10% with holdout accuracy evaluation method, and a smoothing value of 1. This classification resulting 23 patients were classified as negative diabetes and 18 patients were classified as positive diabetes.Keywords: Neural Network, Probabilistics Neural Network, diabetes mellitus, GUI, holdout, smoothing parameter.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI GIZI BURUK BALITA DI JAWA TENGAH DENGAN METODE SPATIAL DURBIN MODEL Ikha Rizky Ramadani; Rita Rahmawati; Abdul Hoyyi
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Severe malnutrition is a state of nutritional deficiencies at a severe level, where the nutritional status is far below the standard. Anyone can suffer from severe malnutrition, especially infants and children who are in the growth period. Central Java Province is one of many provinces in Indonesia where the cases of severe malnourished children under five years are high enough. It is noted that Central Java Province is one of 10 provinces in Indonesia with the highest rate of severe malnutrition cases for 6 years (2005-2010). Using data from year 2011, the result of the Moran’s I test states that there are spatial dependencies on severe malnutrition’s rate of children under five years and some of its influential factors on Central Java Province. Therefore, Spatial Durbin Model (SDM) method is used in this experiment. Variables which significantly affect severe malnutrition on Central Java Province through SDM method are : the numbers of infants with low birth weight ( ), the numbers of houses with good health status ( ), and the numbers of households with access to source of clean water ( ). SDM model obtains value of as much as 70.3% with AIC and MSE respectively 476.32 and 35280.11, results better than Ordinary Least Square (OLS) which produce as much as 41.5% with AIC 490.52 and MSE 60653.693

PERAMALAN EKSPOR NONMIGAS DENGAN VARIASI KALENDER ISLAM MENGGUNAKAN X-13-ARIMA-SEATS (Studi Kasus: Ekspor Nonmigas Periode Januari 2013 sampai Desember 2017) Eka Lestari; Tatik Widiharih; Rita Rahmawati
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Non-oil and gas exports are one of the largest foreign exchange earners for Indonesia. Non-oil and gas exports always experience a decline in the month of Eid Al-Fitr due to delays in the delivery of export goods because the loading and unloading of goods at the port is reduced during Eid Al-Fitr. The shift of the Eid Al-Fitr month on the data will form a pattern or season with an unequal period called the moving holiday effect. The time series forecasting method that usually used the ARIMA method. Because the ARIMA method only suitable for time series data with the same seasonal period and can’t handle the moving holiday effect, the X-13-ARIMA-SEATS method used two steps. First, regARIMA modeling is a linear regression between time series data and the weight of Eid Al-Fitr and the residuals follow the ARIMA process. The weighting is based on three conditions, namely pre_holiday, post_holiday, and multiple. Second, X-12-ARIMA decomposition method for seasonal adjustments that produces trend-cycle components, seasonal, and irregular. Based on the analysis carried out on the monthly non-oil and gas export data for the period January 2013 to December 2017, the X-13-ARIMA-SEATS (1,1,0) model was obtained in the post_holiday condition as the best model. The forecasting results in 2018 show the largest decline in non-oil and gas exports in June 2018 which coincided with the Eid Al-Fitr holiday. MAPE value of 10.90% is obtained which shows that the forecasting ability is good.Keywords: time series, non-oil and gas, X-13-ARIMA-SEATS, moving holiday

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