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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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Articles 733 Documents

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PENENTUAN VALUE AT RISK SAHAM KIMIA FARMA PUSAT MELALUI PENDEKATAN DISTRIBUSI PARETO TERAMPAT Dede Zumrohtuliyosi; Abdul Hoyyi; Agus Rusgiyono
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Each investment object being traded in the stock market will get return that it has risk potential. Return and risk has mutual correlation that equilibrium. If the risk is high, then it obtains high return and vice versa. Risk management is the desain and implementation procedure for controlling risk. Value at Risk (VaR) is instrument to analyze risk management. Financial time series data for return data is assumed that it has heavy tail distribution and heteroscedasticity case (volatility clustering). Time series model that used to modelling this condition are Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregresive Conditional Heteroscedasticity (GARCH), while Value at Risk calculation is used Generalized Pareto Distribution (GPD) approach. This research uses return data from stock closing prices of Kimia Farma Pusat period October 2009 until September 2014. The best ARCH-GARCH model is ARIMA(0,1,1) GARCH(1,2) model because the parameters are significant and it has the smallest AIC value. Risk calculation that is gotten with GPD approach if invest in Kimia Farma Pusat with interval confidence 95% is 13.6928% rupiah from current asset. Keywords: Stock, Risk, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Generalized Pareto Distribution (GPD), Value at Risk (VaR)

PERBANDINGAN MODEL ARCH/GARCH MODEL ARIMA DAN MODEL FUNGSI TRANSFER (Studi Kasus Indeks Harga Saham Gabngan dan Harga Minyak Mentah Dunia Tahun 2013 sampai 2015) Fakhriyana, Deby; Hoyyi, Abdul; Widiharih, Tatik
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesian Composite Index is a value that used to measure the combined performance of shares listed in stock market. Price of crude oil is one of the factors that affect Indonesian Composite Index. If the prices of crude oil is increasing, it will be responsed by Indonesian goverment directly with also increasing the fuel prices, that will have an impact on Indonesian Composite Index. ARIMA and transfer function are methods of modeling time series data and it have assumption that the residual models have to be homogen. To overcome violations of those assumption, this study continue to modelling ARCH/GARCH with ARIMA and transfer function approach. The data used in this study are daily of Indonesian Composite Index and West Texas Intermediate (WTI) crude oil prices data from 2013 to 2015. This study gained two models, the first is ARIMA (1,1,[3]) which variance model of ARCH(1), it’s AIC value is equal to 7707,4287. The second is transfer fuction model (1,0,0) which noise model ARMA(0,[1,3) as well as variance model ARCH(1), it’s AIC value equal to 7689,18984. The best model is the one that has smallest AIC value. From this study can be concluded that the best of ARCH/GARCH model is ARCH/GARCH model with transfer function approach. Keywords : Indonesian Composite Index, crude oil prices, ARIMA, transfer function, ARCH/GARCH

PENGONTROLAN KUALITAS PRODUK MENGGUNAKAN METODE DIAGRAM KONTROL MULTIVARIAT np (Mnp) DALAM USAHA PENINGKATAN KUALITAS (Studi Kasus di PT Coca-Cola Amatil Indonesia (CCAI) Semarang) Nonik Brilliana Primastuti; Sudarno Sudarno; Suparti Suparti
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The industrial revolution was mark the beginning of the rise of industrial in the world. Moreover, in this globalization era, a lot of industry popping up especially those industries in Indonesia with many of those industries would emerge also thight competition. Each company must be trying to superior to that of its products so that each company will always improve the quality of their products in various ways so that the product can deportment in the market. One way of improving the quality of by doing quality control on each of its products. There are many method of conducting control quality. One method used is multivariate np chart. Multivariate np chart usually used for nonconforming units. Based on the results of this research, it is found that the production process in phase I namely from January to February in a state of controlled so that the parameters in the production process phase I can be used in the production process phase II, while to the process of the production phase II there are several observations that are out-of-control so that the production phase II in a state of uncontrolled.

ANALISIS DATA RUNTUN WAKTU DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) Saputra, Arsyil Hendra; Tarno, Tarno; Warsito, Budi
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One popular method of time series analysis is ARIMA. The ARIMA method requires some assumptions; residual of model must be white noise, normal distribution and constant variance. The ARIMA model tends to be better for time series data which is linear. Whereas for the nonlinear time series data have been widely studied by nonlinear methods, one of that is Adaptive Neuro Fuzzy Inference System or ANFIS. The ANFIS method is a method that combines techniques Neural Network and Fuzzy Logic. In this thesis discussed the ANFIS method specifically for the analysis of time series data that have characteristics such as stationary, stationary with outlier, non stationary and non stationary with outlier, and the data of Indonesian palm oil prices is used as a case study. The ANFIS results which were obtained are compared with the results of ARIMA method by the value of RMSE. Based on the analysis and discussion, it is obtained that the results of ANFIS method are better than the results of ARIMA method.

SISTEM ANTRIAN PADA PELAYANAN CUSTOMER SERVICE PT. BANK X Melati Puspa Nur Fadlilah; Sugito Sugito; Rita Rahmawati
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Customer Service is one form of service facility at PT. Bank X which is directly related with the public as customers. It contains kind of transactions that often caused a queue. To increase public interest in the activities of banking transactions, the facility provider tries to gives satisfaction to the customers who come, so they do not have to wait too long but without make disadvantages to the existing service system. Queueing analysis have been done in order to determine how the service system of Customer Service. Based on the analysis of research data on June, 27th 2016 to July, 1st 2016, a queueing model on Customer Service PT. Bank X is (Poisson/Weibull/3):(FCFS/∞/∞) with the customer arrival rate does not exceed the service rate. In that queueing model, the number of arrivals is Poisson distribution, service time is Weibull distribution and there are three service counters. Queueing discipline that applied is customers will be served were the first comes to the bank, with the system capacity and the calling population of customers is infinite. To provide information as a reference or consideration to the PT. Bank X, then a simulation with the software called Arena has been done to determine the performance of the service system with the addition or subtraction of the number of Customer Service.Keywords: Service, Customer, Bank, Customer Service, Queueing Model, Simulation, Arena.

APLIKASI METODE GOLDEN SECTION UNTUK OPTIMASI PARAMETER PADA METODE EXPONENTIAL SMOOTHING Mahkya, Dani Al; Yasin, Hasbi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting is predicting the activities values that have been previously known. One of the methods that can be used to predict is Exponential Smoothing. In this study, exponential smoothing method used is Single Exponential Smoothing (SES), Holt Double Exponential Smoothing (DES) and Triple Exponential Smoothing Holt-Winter (TES) Additive and Multiplicative models. Data used is value of Central Java Export from the period January 2006 until December 2013. There is some weighting parameters were evaluated in this method in order to produce a minimum error. Trial error method is used to obtain the weighting parameters. For SES method parameters evaluated were the parameters α, in DES method there are α and γ. And TES method there are α, γ and β. The value that will be minimize is Persentage Mean Absolute Error (MAPE). This study used the Golden Section method to find the parameter values that minimize the weighting function of MAPE. And built a Graphical User Interface (GUI) MATLAB in order to facilitate the analysis process. The Golden Section analysis found the best model is the TES Holt Winters Additive because it has a minimum value of MAPE. With Use the TES Holt Winters Additive will continue to predict the value of exports of Central Java 12 periods ahead with weighting parameters that minimize MAPE. Keywords : Exponential Smoothing, Graphical User Interface (GUI), Export, Golden Section, Predict

PERBANDINGAN ANALISIS KLASIFIKASI ANTARA DECISION TREE DAN SUPPORT VECTOR MACHINE MULTICLASS UNTUK PENENTUAN JURUSAN PADA SISWA SMA Rizky Ade Putranto; Triastuti Wuryandari; Sudarno Sudarno
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Data mining is a process that employs one or more of Machine Learning techniques to analyze and extract knowledge automatically. Analysis of data mining is to determine the classification of a new data record into one of several categories that have been defined previously, also known as Supervised Learning. Classification Decision Tree is one of the well-known technique in data mining and is one of the popular methods in the decision making process of a case in which the method is obtained entropy criteria, information gain and gain ratio. Classification Support Vector Machine Multiclass (SVMM) is known as the most advanced machine learning techniques to handle multi-class case where the output of the data set has more than two classes or categories. This final project aims to compare the level of accuracy and error rate of Decision Tree classification and prediction majors SVMM for high school students at SMAN 1 Jepara. The total accuracy of 88,57% and 11,43% error rate for the classification decision tree and the total accuracy of 87,14% and the error rate for the classification SVMM 12,86%. Keywords : Data Mining, Machine Learning, Supervised Learning, Decision Tree, Support Vector Machine Multiclass

PEMODELAN MARKOV SWITCHING AUTOREGRESSIVE Ariyani, Fiqria Devi; Warsito, Budi; Yasin, Hasbi
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Transition from depreciation to appreciation of exchange rate is one of regime switching that ignored by classic time series model, such as ARIMA, ARCH, or GARCH. Therefore, economic variables are modeled by Markov Switching Autoregressive (MSAR) which consider the regime switching. MLE is not applicable to parameters estimation because regime is an unobservable variable. So that filtering and smoothing process are applied to see the regime probabilities of observation. Using this model, transition probabilities and duration of the regime can be informed. In this case conducted exchange rate of Rupiah to US Dollar modeling with MSAR. The best model is MS(2)-AR(1) with transition probabilities from depreciation to appreciation is 0,052494 and appreciation to depreciation is 0,746716. Duration of the depreciation state is 19,04986 days and appreciation state is 1,339198 days.

PERAMALAN JUMLAH KECELAKAAN DI KOTA SEMARANG TAHUN 2017 MENGGUNAKAN METODE RUNTUN WAKTU (Studi Kasus : Data Jumlah Kecelakaan Lalu Lintas di Kota Semarang Periode Januari 2012 – Desember 2016) Iantazar Rezqitullah Maharsi; Moch. Abdul Mukid; Yuciana Wilandari
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Accident data from Satlantas Polrestabes Semarang City is known that in 2016 there is an increase in the number of traffic accidents in the Semarang city. In the future the impact of accidents is predicted to be bigger so it is necessary to forecasting. Forecasting is one of the most important elements in decision making, because effective or not a decision generally depends on several factors that can not be seen at the time the decision was taken. In this time study the possible time series model is ARMA (2,2), ARMA (2,1), ARMA (1,2), ARMA (1,1), AR (2), AR (1), MA (2), MA (1). However, after testing, the model used is ARMA (1,1). This model is used because it meets all the assumption requirements that are parameter significant , residual indepedent test, residual normality test and the smallest Mean Square Error value. According to data forecasting results showed the highest number of crashes existed in January of 97 accidents and the lowest in December amounted to 93 accidents, So that the necessary to action from the relevant agencies to cope with the increasing number of traffic accidents in the city of Semarang. Keywords : Time Series Method, ARMA (1,1), Traffic Accident.

PEMODELAN PERSENTASE PENDUDUK MISKIN DI KABUPATEN DAN KOTA DI JAWA TENGAH DENGAN PENDEKATAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION Hakim, Arief Rachman; Yasin, Hasbi; Suparti, Suparti
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical analysis that models the relationship between the response variable and the predictor variable. Geographically Weighted Regression (GWR) is the development of linear regression with the added factor of the geographical location where the response variable is taken, so that the resulting parameters will be local. Mixed Geographically Weighted Regression (MGWR) has a basic concept that is a combination of a linear regression model and GWR, by modeling variables that are local and which are global variables. Methods for estimating the model parameters MGWR no different from the GWR using Weighted Least Square (WLS). Selection of the optimum bandwidth using the Cross Validation (CV). Application models MGWR the percentage of poor people in the district and town in Central Java showed MGWR models that different significantly from the global regression model. As well as models generated for each area will be different from each other. Based on the Akaike Information Criterion (AIC) between the global regression model, the GWR and MGWR models, it is known that MGWR models with Gaussian kernel weighting function is the best model is used to analyze the percentage of poor in the counties and cities in Central Java because it has the smallest AIC value.Keywords: Akaike Information Criterion, Cross Validation, Kernel Gaussian function, Mixed Geographically Weighted Regression, Weighted Least Square.

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