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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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PEMODELAN JARINGAN SYARAF TIRUAN DENGAN ALGORITMA ONE STEP SECANT BACKPROPAGATION DALAM RETURN KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT Najwa, Maulida; Warsito, Budi; Ispriyanti, Dwi
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Exchange rate is the currency value of a country that is expressed by the value of another country's currency. Changes in exchange rates indicate risks or uncertainties that would return obtained by investors. With the predicted value of return, investors can make informed decisions when to sell or buy foreign currency to gain an advantage. Forecasting of return values can be using artificial neural network with backpropagation. In backpropagation procedure, data is divided into two pairs, namely training data for training process and testing data for testing process. In the training process, the network is trained to minimize the MSE. One of optimization method that can minimize the MSE is one step secant backpropagation. In this research, the data used is the return of the exchange rate of rupiah against US dollar in the period of January 1st, 2015 until December 31st, 2015. The results were obtained architecture best model neural network that was built from 8 neurons in the hidden layer, 1 unit of input layer with input xt-1 and 1 unit of output layer. The activation function used in the hidden layer and output layer are bipolar sigmoid and linear, respectively. The architecture chosen based on the smallest MSE of testing data is 0.0014. After obtaining the best model, data is foreseen in the period of November 2016 produce MAPE=153.23%.Keyword : Artificial Neural Network, Backpropagation, One Step Secant, Time Series, Exchange Rate.

IDENTIFIKASI LAMA STUDI BERDASARKAN KARAKTERISTIK MAHASISWA MENGGUNAKAN ALGORITMA C4.5 (Studi Kasus Lulusan Fakultas Sains dan Matematika Universitas Diponegoro Tahun 2013/2014) Bramaditya Swarasmaradhana; Moch. Abdul Mukid; Agus Rusgiyono
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Based on academics regulation No. 209/PER/UN7/2012, the study period of students in Diponegoro University has been scheduled for 4 years. In this study the graduation status of students that graduate under or equal to 4 years categorized as graduate on time, meanwhile students that graduate over 4 years categorized as graduate out of time. Hence, it is important to understand the profile of students who graduate on time and out of time based on gender, majors, GPA, organizational experience, part time experience, scholarship, students origin and pathways scholar. The purpose of this study is to identify those students profiles using Algorithm C4.5. Algorithm C4.5 contructs a decision tree that able to handle missing values, able to handle continues attribute and able to simplify the trees by pruning. The accuration of the Algorithm C4.5 is 84.475% and the number of the nodes are 20 nodes where 13 nodes are leaf nodes. The students profile that identified graduate on time are students of Physics who had received scholarship and a woman; students of Chemistry with GPA > 3.06; students of Statistics with GPA > 3.43 from SNMPTN also PSSB and students of Mathematics with GPA > 2.96. Keywords: Study Period, Algorithm C4.5, Decision Tree.

PERBANDINGAN METODE KLASIFIKASI REGRESI LOGISTIK BINER DAN RADIAL BASIS FUNCTION NETWORK PADA BERAT BAYI LAHIR RENDAH (Studi Kasus: Puskesmas Pamenang Kota Jambi) Samosir, Riama Oktaviani; Wilandari, Yuciana; Yasin, Hasbi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Low Birth Weight (LBW) is one of the main causes of infant mortality. LBW must be identified and predicted before the baby birth by observing historical data of expectant. This research aims to analyze the classification of status newborn in order to reduce the risk of LBW. The statistical method used are the Binary Logistic Regression and Radial Basis Function Network. The data used in this final project is birth weight at Pamenang Jambi City health center in 2014. In this research, the data are divided into training data and testing data. Training data will be used to generate the model and pattern formation, while testing the data is used to measure how the accuracy of the representative model or pattern formed in classifying data through confusion tables. The results of analysis showed that the Binary Logistic Regression method gives 81,7% of classification accuracy for training data and 77,4% of classification accuracy for testing data, while Radial Basis Function Network method gives 92,96% of classification accuracy for training data and 80,64% of classification accuracy for testing data. Radial Basis Function Network method has better classification accuracy than the Binary Logistic Regression method. Keywords: Low Birth Weight (LBW), Binary Logistic Regression, Radial Basis Function Network, Classification, Confusion

ANALISIS CONJOINT PAIRWISE-COMPARISON UNTUK MENGETAHUI TINGKAT KEPENTINGAN ATRIBUT JASA BIRO PERJALANAN WISATA (Studi Kasus Beberapa SMA Negeri di Kabupaten Klaten) Galih Maraseta W H Prasaja; Yuciana Wilandari; Sudarno Sudarno
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Competition in the business world travel agency today's increasingly stringent. Travel agency is a business that organizes tourist activities and other services related to the operation of the tour both domestically and abroad in a foreign country. To help business people of the travel agency in knowing and understanding consumer preferences on a combination of attributes of a travel agency conjoint analysis can be used. In this study conjoint analysis is used by using presentation method of pairwise-comparison. There are four attributes used in this analysis, they are bus facilities, agency facilities, hotel, and dining facilities. From the results of the analysis that obtained by the respondents, the most important attribute in selecting a travel agency is the dining attribute with a relative importance value of 38,02%. The next most important attribute according to the respondents is the attribute of the bus facility with a relative importance value of 28,46%, attributes agency facilities with a relative importance value of 19,58%, attributes the hotel facilities with a relative importance value of 13,94%. The combination of desired respondents in choosing or use the services of a travel agency is a travel agency with wifi bus facilities, hotel facilities with the large bed, an agency facility of video documentation and a buffet meal.

PEMILIHAN MEREK LIPSTIK TERFAVORIT DENGAN MADM BERBASIS GUI MATLAB Finisa, Husnul; Widiharih, Tatik; Mukid, Moch. Abdul
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Lipstick is a cosmetic usually worn by women to improve appearance with apply to the lips. The interest on lipstick among student at indonesia based on the various brands lipstick of national and international land of selling in indonesia. Based on this condition , it takes a method that can evaluate most favorite brand lipstick according to college student . The method applied to choose most favorite brand lipstick are Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both this method can do the decision to establish an alternative best of a number of alternatives based on a number of certain criteria in overcoming Multi Attribute Decision Making (MADM), The concept of SAW is looking for a sum of the weighted performance rating for each alternative in all criteria. While TOPSIS using the principle that alternative chosen should have the shortest distance of a solution ideal positive and farthest of a solution ideal negative. There are 10 alternative brand lipstick and 10 criteria, the criterias are the price, color, form, packaging, resilience, pigmentation, texture, scent, the availability of code expired lipstick. The result of the research indicated that to the SAW method most favorite brand lipstick is of NYX and to the TOPSIS method most favorite brand lipstick is Wardah. The research also produce an application programming GUI Matlab that can help users in process data uses the method saw and topsis for an election most favorite brand lipstick.Keywords : GUI, Lipstick, MADM, SAW, TOPSIS

PEMODELAN STATUS KESEJAHTERAAN DAERAH KABUPATEN ATAU KOTA DI JAWA TENGAH MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION SEMIPARAMETRIC Firda Shintia Dewi; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Welfare in society is one of the most important aspects in ensuring the realization of the social where people have a good level of welfare. Benchmarks achieved prosperity is the fulfillment of basic needs of society as feasible. Statistical methods have been developed for the analysis of spatial data by taking into account factors that Geographically Weighted Logistic Regression Semiparametric (GWLRS). GWLRS is a local form of the logistic regression where there are parameters that are influenced by the location (Geographically varying coefficient) and the parameters that are not influenced by the location (fixed coefficient). Selection of the optimum bandwidth using Cross Validation (CV). Model GWLRS Welfare Status district or city in Central Java showed that GWLRS models differ significantly from the logistic regression model. And models generated for each area will be different from each other. To get the best models, the number of models to be evaluated. One method for selecting the best model is the value of the Akaike Information Criterion (AIC). Based on AIC obtained the best model is the model GWLRS because it has the smallest AIC value of 46.11213 with a classification accuracy of 77.14%. Keywords: Welfare, Geographically Weighted Logistic Regression Semiparametric, Cross Validation, Akaike Information Criterion

ANALISIS ANTRIAN PASIEN INSTALASI RAWAT JALAN POLIKLINIK LANTAI 1 DAN 2 RSUD CENGKARENG, JAKARTA Nadeak, Sanitoria; Sugito, Sugito; Suparti, Suparti
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The queue process associates with the arrival of the costumers of a service facility, waiting in a queue line when all waiters are busy, and finally left the facility after being served. Queuing phenomena can be found in public service facilities, such as in District General Hospital (RSUD) Cengkareng. The length of the registration procedure, consultation services for physicians, and waiting time for the pharmacy services, can influence the satisfaction of the patients of Outpatient Installation of RSUD Cengkareng. Therefore, it is necessary to have an appropriate queue model to get an effective service, balanced and efficient, that can reduce the long queues and waiting time. From the analysis, the queue model for the registration of the Workers Social Security Agency (BPJS) patient is (M /M/6):(GD/∞/∞) with the number of server is 6 counters and for the non BPJS patients is (M/M/2):(GD/∞/∞) with the number of server is 2 counters. The queue model for the psychiatrist clinic and anesthetic is (M/M/1):(GD/∞/∞) with the number of server is 1 counter. The queue model for the other Polyclinic is (M/M/c):(GD/∞/∞) with the number of server depends on the clinic itself.Keywords: Queue, Outpatient Installation, District General Hospital (RSUD) Cengkareng

PENGUKURAN RISIKO KREDIT OBLIGASI KORPORASI DENGAN CREDIT VALUE AT RISK (CVAR) DAN OPTIMALISASI PORTOFOLIO MENGGUNAKAN METODE MEAN VARIANCE EFFICIENT PORTFOLIO (MVEP) Agus Somantri; Di Asih I Maruddani; Abdul Hoyyi
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Getting benefits of many kinds of coupon is not the only advantage of bond investment, but also it gives potential risks such as credit risk. Credit risk originates from the fact that counterparties may be unable to fulfill their contractual obligations. Credit Value at Risk (CVaR) is introduced as a method to calculate bond credit risk if default occurs. CVaR is defined as the most significant credit loss which occurs unexpectedly at the selected level of confidence, measured as the deviation of Expected Credit Loss (ECL). To construct optimal bond portfolio requires Mean variance Efficient Portfolio (MVEP) method. MVEP is defined as the portfolio with minimum variance among all possible portfolios that can be formed. This study case has been constructed through two bonds, bond VI of Jabar Banten Bank (BJB) year 2009 serial B and bond of BTPN Bank I year 2009 serial B. Based on the R programming output, the obtained results for bonds with a rating idAA BJB, has a positive CVaR value of Rp 22.728.338,00. While bonds with a rating idAA BTPN and portfolio for both bonds, each of which has a negative CVaR value amounted Rp 28.759.098,00 and Rp 32.187.425,00. CVaR is positive (+) expressed as the loss addition of ECL while is negative () expressed as a decrease in loss of ECL. For optimal bond portfolio, gained weight for each bond is equal to 16,85202% for BJB and 83,14798% for BTPN bonds.

MODEL KOMBINASI ARIMA DALAM PERAMALAN HARGA MINYAK MENTAH DUNIA Setiyowati, Eka; Rusgiyono, Agus; Tarno, Tarno
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Oil is the most important commodity in everyday life, because oil is one of the main sources of energy that is needed for other people. Changes in crude oil prices greatly affect the economic conditions of a country. Therefore, the aim of this study is develop an appropriate model for forecasting crude oil price based on the ARIMA and its ensembles. In this study, ensemble method uses some ARIMA models to create ensemble members which are then combined with averaging and stacking techniques. The data used are the price of world crude oil period 2003-2017. The results showed that ARIMA (1,1,0) model produces the smallest RMSE values for forecasting the next thirty six months. Keywords: Ensemble, ARIMA, Averaging, Stacking, Crude Oil Price

VALUASI COMPOUND OPTION PUT ON CALL TIPE EROPA PADA DATA SAHAM FACEBOOK Muhammad Sunu Widianugraha; Di Asih I Maruddani; Diah Safitri
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Option is a contract that gives the right to individuals to buy (call options) or sell (put options) the underlying asset by a certain price for a certain date. One type of options that are traded is compound options. Compound option model is introduced by Robert Geske in 1979. Compound option is option on option. Compound option put on a call is put option where the underlying asset are call option. An empirical study using compound option put on a call stocks of Facebook. It has strike price compound option US$ 77.5 and strike price call option US$ 80, with the first exercise date on September 26, 2014 and the second exercise date on October 31, 2014. The theoritical price of compound option put on call on stocks of Facebook is US$ 75.65048. Keywords: Compound option, put on a call, option stocks of Facebook, Black-Scholes model, theoritical price.

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Jawa tengah

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