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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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PENGUKURAN PROBABILITAS KEBANGKRUTAN OBLIGASI KORPORASI DENGAN SUKU BUNGA VASICEK MODEL MERTON (Studi Kasus Obligasi PT Bank Lampung, Tbk) Kumo Ratih; Di Asih I Maruddani; Abdul Hoyyi
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bond is one of financial instrument that have lower investment risk than stock. One of investment risk is credit risk. Its refers to the risk due to unexpected changes in the credit quality of a counterparty or issuer on maturity date. There are two ways in the modelling of credit risk, structural model and reduced models. The structural model introduced by Black-Scholes (1973) and Merton (1974). On the Merton model assume that default occurs when the firm can not pay the coupon or face value at the maturity date. The interest rate on this model asssumed following Vasicek rate. An empirical study using corporate bond of PT Bank Lampung, Tbk with 300 billion face value. Value of Probability of Default 0,0000007910811% provethat PT Bank Lampung still can full their obigation at November 2012.

ANALISIS CREDIT SCORING MENGGUNAKAN METODE BAGGING K-NEAREST NEIGHBOR Fatimah, Fatimah; Mukid, Moch. Abdul; Rusgiyono, Agus
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

According to Melayu (2004) credit is all types of loans that have to be paid along with the interest by the borrower according to the agreed agreement. To keep the quality of loans and avoid financial failure of banks due to large credit risks, we need a method to identified any potentially customer’s with bad credit status, one of the methods is Credit Scoring. One of Statistical method that can predict the classification for Credit Scoring called Bagging k-Nearest Neighbor. This Method uses k-object nearest neighbor between data testing to B-bootstrap of the training dataset. This classification will use six independence variables to predict the class, these are Age, Work Year, Net Earning, Other Loan, Nominal Account and Debt Ratio. The result determine k =1 as the optimal k-value and show that Bagging k-Nearest Neighbor’s accuracy rate is 66,67%. Key word : Credit scoring, Classification, Bagging k-Nearest Neighbor

PEMODELAN PROPORSI PENDUDUK MISKIN KABUPATEN DAN KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION Khusnul Yeni Widiyanti; Hasbi Yasin; Sugito Sugito
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 aims to quantify the effect of predictor variables on the response variable. Geographically Weighted Regression (GWR) is a local form of regression and a statistical method used to analyze spatial data. Geographically and Temporally Weighted Regression (GTWR) is the development of GWR models to handle data that is not stationary both in terms of spatial and temporal simultaneously. In obtaining estimates of parameters of the GTWR model can be used Weighted Least Square method (WLS). Selection of the optimum bandwidth used method of Cross Validation (CV). Conformance testing global regression and GTWR models approximated by the distribution of F, whereas the partial testing of the model parameters using the t distribution. Application GTWR models at the level of poverty in Central Java province in 2008 to 2012 showed GTWR models differ significantly from the global regression model. Based on R2 and Mean Squared Error (MSE) value between the global regression model and GTWR models, it is known that the GTWR model with exponential weighting kernel function is the best model is used to analyze proportion of poor people in Central Java province in 2008 to 2012 because it has a value of R2 larger and MSE is the smallest. Keywords: Bandwidth, Cross Validation, Exponential Kernel Functions, Geographically and Temporally Weighted Regression, Weighted Least Square, R2, Mean Squared Error.

KOMPUTASI GUI UNTUK INFERENSI VEKTOR MEAN DAN INFERENSI MATRIKS KOVARIANSI DENGAN MENGGUNAKAN SOFTWARE R Subakti, Yudha; Mukid, Moch. Abdul; Yasin, Hasbi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multivariate statistics is a branch of statistical science that discuss the analysis for multivariable case. Some analysis in multivariate statistics are discussing about inferences, there are inferences about mean vector and inferences of covariance matrices. Along with the development of technology, to support statistical analysis from both of inferences is requiring a statistical software, R is one of it with open source based. R is often used in statistical computing with command line interface (CLI) as the interface. In implementation, CLI requires the R user to remember names of used syntax and function. It makes less effective when the inferences have many related statistical analysis, so graphical user interface (GUI) needed to giving an easy way to accessing all of it. Testing for mean vectors of two populations will be performed using S. Rockiki’s data about measures of oxygen consumption for 25 males and 25 females. Results about assumptions showing both populations are normal multivariate distributed and have different covariannce matrix. The conclusion from the testing for mean vectors of two populations has performed is both populations have different mean vectors. There are packages are used on construction of GUI in R, including gdata, tcltk2, and devtools with additional software like Rtools and ActivePerl. The GUI has four main menus such as File, Analysis, Plot, and Help. Based on GUI usage, the GUI has been able to processing the chosen analysis and showing valid output.. Keywords: Multivariate Statistics, Inferences about mean vector, Inferences of covariancematrices, R, GUI.

ANALISIS INTERVENSI FUNGSI STEP (Studi Kasus Pada Jumlah Pengiriman Benda Pos Ke Semarang Pada Tahun 2006 – 2011) Amelia Crystine; Abdul Hoyyi; Diah Safitri
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Data time series yang dipengaruhi oleh beberapa kejadian yang disebut intervensi akan mengakibatkan perubahan pola data pada satu waktu t. Analisis intervensi terdiri dari dua fungsi yaitu fungsi step dan fungsi pulse.Time series data that are influenced by several events called the intervention will lead to changes in the pattern of data at a t time. Analysis of intervention consists of two functions, that is the step function and pulse function. Intervention of step function represents an intervention that have long-term effects, whereas pulse function represents an intervention that takes place at a particular time. Step function intervention model was created based on the delay time of the intervention (b), the length of the intervention effect (s), and the pattern of intervention effects that was occured after b + s period (r). Intervention modeling was done after ARIMA (Autoregressive Integrated Moving Average) model was acquired. ARIMA model was used to determine the b, s, and r order of intervention. In this study, the step function intervention analysis was used to assess the amount of postage on the period January 2006 to February 2011. Based on the analysis, the ARIMA model produced was ARIMA (0,1,1). Based on intervention response obtained residual value b = 4, s = 0, r = 2 is used to form a model of intervention using the least squares method.

PEMILIHAN INPUT MODEL REGRESSION ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (RANFIS) UNTUK KAJIAN DATA IHSG Sari, Sasmita Kartika; Tarno, Tarno; Safitri, Diah
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Jakarta Composite Index (JCI) is one of indexes issued by the Indonesia Stock Exchange (IDX) with its calculation component using all the registered emiten. Several factors affecting the JCI are Dow Jones Index, inflation, and USD/IDR exchange rate. The study used Regression Adaptive Neuro Fuzzy Inference System (RANFIS) to analyze the affect of predictor variables on the JCI. The role of regression in RANFIS is a preprocessing in the determination of input in ANFIS. The optimum ANFIS model in RANFIS is strongly influenced by three things, they are input determination, membership functions, and rule. The technique of defining rules followed the rule of genfis1 and genfis3. The model accuracy was measured using the smallest RMSE and MAPE. Based on the empirical studies which implemented Dow Jones Index, inflation, and USD/IDR exchange rate as the predictors and JCI as the response, it was obtained that optimum RANFIS model with gauss membership function, the number of cluster 2 with 2 rules generated by genfis3 produced RMSE in-sample 233.0 and out-sample 301.9, as well as MAPE in-sample 6.5% and out-sample 4.8%. While in regression analysis, it obtained RMSE in-sample 351.27 and out-sample 590.99, as well as MAPE in-sample 9.6% and out-sample 10.2% with violation of assumption. This shows that the result of RANFIS method is better than regression analysis. Keywords: JCI, regression analysis, neuro fuzzy, RANFIS, genfis

ANALISIS KORESPONDENSI UNTUK MENDAPATKAN PETA PERSEPSI DAN VARIABEL BAGI KEGIATAN USAHA (Studi Kasus Rumah Makan Spesial Sambal (SS) terhadap Pesaingnya) Susi Ekawati; Agus Rusgiyono; Triastuti Wuryandari
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Correspondence analysis is a technique for displaying the rows and columns of a data matrix primarily, a two-way contingency table as points in dual low-dimensional vector spaces. This technique is used to reduce the dimension of variables and describe the profile vector of rows and columns of the contingency table. This research aims to determine the position of the rivalry between the restaurants in Tembalang region based on consumer’s perceptions and to identify variables that distinguish it. The variables which used are including the price, taste, cleanliness, service, variety of food, and parking lots. Correspondence analysis is used to determine the variables that distinguish the 5th of the restaurant. The correspondence analysis produces a combined perceptual map with the satisfaction variables restaurant. From the analysis, it can be concluded that the perceptual map in the correspondence analysis shows the proximity between restaurant and satisfaction variables. Keywords : correspondence analysis, perceptual map, restaurant, satisfaction.

Pengendalian Kualitas Data Atribut Multivariat dengan Mahalanobis Distance dan T2 Hotelling (Studi Kasus PT Metec Semarang) Anggoro, Alfahari; Mustafid, Mustafid; Rahmawati, Rita
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Vending machine is a machine used to sell the product automatically without any operator. Data vending machine products are classifiable in the attribute data for the category of disabled and not disabled. To maintain consistency of product quality and in accordance with market needs, it is necessary to do quality control on the activity undertaken. In the production process, to monitor the quality of service can be used multivariate control charts. Diagram control is often used is the Mahalanobis Distance and T2 Hotelling. The study was conducted on the data of defects in the production of vending machines in September 2013 to April 2015. Results showed that in the control diagram Mahalanobis Distance acquired upper limit value is 15.615 the control diagram is known there are two observations that are outside the control limits. While the T2 Hotelling control chart obtained upper limit value is 36.12 and all observations are within control limits. The production process has been good vending machine, known from the process capability of Cp value of 1.1503.Keywords: Vending Machine, Mahalanobis Distance, T2 Hotelling

PENENTUAN TREN ARAH PERGERAKAN HARGA SAHAM DENGAN MENGGUNAKAN MOVING AVERAGE CONVERGENCE DIVERGENCE (Studi Kasus Harga Saham pada 6 Anggota LQ 45) Tri Murda Agus Raditya; Tarno Tarno; Triastuti Wuryandari
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of many examples of technical indicator that frequently used for stock price analysis is Moving Average Convergence Divergence (MACD). MACD generates two signal called goldencross and deathcross are used to find the reversal momentum of stock price trend movement. Goldencross as a oversold point marker serves to give a buying signal. While, deathcross as a overbought point marker serves to give a selling signal. Research on six stocks member of LQ45 (ANTM, BWPT, MNCN, TINS, BJBR, and LPKR) during the period January 1 until October 31, 2012 managed to prove the accuracy of the signal formed by MACD signal. By applying the MACD Indicator consistently, investors can get a percentage of profit above the actual inflation rate in 2012 by Indonesian Bank. On these results, the goldencross and deathcross signal give a good performance as tool of technical analysis for determining the trend of the direction of stock price movements

PENERAPAN METODE WEIGHTED PRODUCT (WP) DAN ELIMINATION ET CHOIX TRANDUISANT LA REALITÉ (ELECTRE) DENGAN PEMBOBOTAN ENTROPY MENGGUNAKAN GUI MATLAB (Studi Kasus: Pemilihan Hero Terkuat Arena of Valor) Sukanianto, Eko Adyan; Sugito, Sugito; Rahmawati, Rita
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Arena of Valor (AOV) is a mobile game published by Garena in Indonesia. There will be 5 players in each team, selecting a hero to play in the game. By selecting the strongest hero each role can help facilitate team to strategize the composition of heroes that will be used to achieve victory. Weighting each criteria and selecting the strongest hero also become a consideration to control the game to be stable and balanced by the developer. The alternatives are all hero from each role (Tank, Warrior, Assassin, Mage, Archer and Support), while the criterias are skill effect points, maximum HP (Health Points), physical attack, physical defense, movement speed and HP recovery every 5 seconds. In this study, the writer uses WP and ELECTRE methods to select the strongest hero with Entropy weighting method. This study produce a Matlab GUI that can be used to facilitate computational selection. The results show that the strongest hero in AOV are Grakk (Tank), Astrid (Warrior), Ormarr (Warrior), Murad (Warrior/Assassin), Lauriel (Mage/Assassin), The Joker (Archer) and Alice (Support). While the criteria with the highest weighting is the skill. Keywords: AOV, Garena Indonesia, WP, ELECTRE, Entropy, GUI Matlab

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