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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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KLASIFIKASI KINERJA PERUSAHAAN DI INDONESIA DENGAN MENGGUNAKAN METODE WEIGHTED K NEAREST NEIGHBOR (Studi Kasus: 436 Perusahaan Yang Terdaftar Di Bursa Efek Indonesia Tahun 2015) Cyntia Surya Utami; Moch. Abdul Mukid; Sugito Sugito
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

A company's performance can be seen from the analysis of the company's financial statements. Financial statement analysis is used to determine the development of the company's financial condition. In analyzing the financial statements required financial ratios depicting the weight of the company's performance. This thesis aims to classify the performance of the company into two classifications, namely the company healthy and unhealthy companies as well as determine the level of accuracy. This final project using financial ratio data 436 companies listed in the Indonesia Stock Exchange in 2015 which has been audited and is divided into two parts of 349 training data and 87 test data. The method used is the weighted k nearest neighbor with a dependent variable is the performance of the company and six independent variables are financial ratios WCTA, ROA, TATO, DAR, LDAR and ROI. The results of this thesis show that the method of calculation of weighted k k nearest neighbor optimal done by trial and error. Provided that the optimal k at k = 3 for kernel inversion, epanechnikov and triangles while for optimal kernel k gauss at k = 4. The accuracy of classification and classification performance of the company gave almost the same results by using kernel inversion, Gauss, epanechnikov and triangles. Keywords: financial ratios, weighted k nearest neighbor and kernel inversion, Gauss, epanechnikov and triangles.

MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) UNTUK KLASIFIKASI STATUS KERJA DI KABUPATEN DEMAK Kishartini, Kishatini; Safitri, Diah; Ispriyanti, Dwi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Unemployment is one of the issues relating to economic activities, public relations and also the problems of humanity. Unemployment also occur in Demak and factors suspected as the cause of unemployment in Demak: gender, area of residence, age, status in the household, marriage status and education. Demak BPS records the number of people looking for work (unemployed) as many as 226.228 people, or 29,55% of the working age population. MARS (Multivariate Adaptive Regression Splines) is one of the methods used for classification. MARS is used for high-dimensional data, which is data that has a number of predictor variables for 3 ≤ v ≤ 20 data used in this study is a secondary data from national labor force survey (SAKERNAS) in 2012. To get the best MARS models performed with by combining Maximum Base Function (BF), Minimal Observation (MO), and Maximum Interaction (MI) by trial and error. MARS model is used to classify employment status in Demak are MARS models (BF =24, MI=3, MO=1). Keywords: Unemployment, Classification, MARS

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT KEMISKINAN DENGAN METODE REGRESI PROBIT ORDINAL (Studi Kasus Kabupaten/ Kota di Jawa Tengah Tahun 2013) Alin Citra Suardi; Triastuti Wuryandari; Sugito Sugito
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v5i1.10908

According to BPS, Central Java is third in terms of the number of poor people in Indonesia. The overall number of poor people in Central Java in 2013 was 4.811.300 inhabitants. Factors that influence the level of poverty can be derived from the employment factor, economic factors, or educational factors. Based on these three factors independent variables were selected which supposed to influence the poverty level. There are inflation, City Minimum Wage by Regency/City, Gross Regional Domestic Product at constant market prices, Unemployment Rate, Mean Years of Schooling, and Illiteracy Rate. The poverty level is categorized into three categories. There are Prosperous, Medium and Poor. The independent variables were analyzed its effect on poverty levels that have been categorized by the Ordered Probit Regression method. The complete model of the ordered probit regression is tested the significance of the parameters by likelihood ratio test and Wald test. Based on ordered probit regression analysis, variables that affect the level of poverty in Central Java in 2013 was Inflation, City Minimum Wage by Regency/City, and Mean Years of Schooling (MYS).Keywords : Poverty Level, Central Java, Ordered Probit Regression.

PREDIKSI TINGGI PASANG AIR LAUT DI KOTA SEMARANG DENGAN MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) DAN DETEKSI OUTLIER Sa'adah, Alfi Faridatus; Ispriyanti, Dwi; Suparti, Suparti
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Semarang as the capital of the province of Central Java is a central transportation that has a high intensity and strategic activities. However, this area has a tidal disaster threat level is high enough. Tidal flood is a phenomenon where sea water entered the land area when the sea level has getting tides. In the future impact of tidal inundation in Semarang city is predicted to be greaterso that has needed the forecasting of high tide. The data pairs tend to experience seasonal monthly and contained outliers that may affect the suitability of the model so that Seasonal Autoregressive Integrated Moving Average (SARIMA) and outlier detection is used for forecasting method. For outlier detection, there are four types of outliers are additive outlier (AO), innovational outlier (IO), level shift (LS) and temporary change (TC). The study was conducted on the data of tide in Semarang period January 2004 - December 2012 based on the average high tide occurs when the maximum. The results of research showed that the model SARIMA with 7 outliers result predictions with high accuracy because it has a smaller AIC value is 649,1083 compared to the SARIMA models without outlier is 705,6404.

KLASIFIKASI DATA BERAT BAYI LAHIR MENGGUNAKAN PROBABILISTIC NEURAL NETWORK DAN REGRESI LOGISTIK (Studi Kasus di Rumah Sakit Islam Sultan Agung Semarang Tahun 2014) Erfan Sofha; Hasbi Yasin; Rita Rahmawati
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Birth Weight Infant (BWI) is the baby’s weight weighed in an hour after being born. Factors that may influence the BWI such as maternal age, length of gestation, body weight, height, blood pressure, hemoglobin and parity. One possibility of BWI is Low Birth Weight Infant (LBWI) (BWI < 2500 gram). LBWI is one of the causes of infant mortality. This study use the Probabilistic Neural Network (PNN) and Logistic Regression to classify the birth weight of infant in RSI Sultan Agung Semarang along the year of 2014. This study’s aims are to know the factors that affect the BWI by using logistic regression and finally finding the best method between PNN and logistic regression methods in classifying the BWI data. As a result, gestation, body weight and hemoglobin are the factors that affect the BWI in RSI Sultan Agung Semarang. The accuracy of PNN classification method on training data is 100%, which is better than the logistic regression method giving only about 88,2%, while the testing data has the same great accuracy at 86,67%. Keywords: BWI, LBWI, PNN, Logistic Regression, Classification

PEMODELAN KURS MATA UANG RUPIAH TERHADAP DOLLAR AMERIKA MENGGUNAKAN METODE GARCH ASIMETRIS Sulistyowati, Ulfah; Tarno, Tarno; Hoyyi, Abdul
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One factor causing to slowing economic growth in Indonesia is the currency exchange rate. In Indonesia,the exchange rate of the rupiah against the dollar is always become an attention of society. To monitor the movement needed a mathematical model that can be used to forecast the rupiah exchange rate to the dollar. Data rupiah exchange rate against the dollar is a financial time series data has a non-constant volatility. One model that is often used for the prediction of these data is ARIMA-GARCH. In this study discussed about modeling the data rate of the rupiah against the dollar using asymmetric GARCH, such as exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Autoregressive Power ARCH (APARCH). Modeling the exchange rate against the dollar using all three types of the Asymmetric GARCH models produce the best models, the ARIMA ([4.5], 1, [4,5]) - APARCH (2,1). With the results obtained using the model for volatility forecasting that volatility decreased from the previous forecast but still be at its high volatility.Keywords : Exchange rate, ARIMA, GARCH, Asymmetric GARCH, volatilty

ANALISIS BIPLOT ROW METRIC PRESERVING UNTUK MENGETAHUI KARAKTERISTIK PROVIDER TELEPON SELULER PADA MAHASISWA S1 FSM UNIVERSITAS DIPONEGORO Artha Ida Sri Anggriyani; Diah Safitri; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Communication is the basis of human interaction. One of the progression in telecommunications is telecommunication tools, e.g. a mobile phone. Usually on every communication tools such as mobile phones are equipped with a provider. The methods used to analyze mobile phone provider is the biplot analysis. Biplot analysis is an analysis which gives a demonstration of the matrix data graphically X into a plot with vector in row matrix X as describing an object, with a vector in column matrix X describing variables. If α = 1 then it is called analysis biplot Row Metric Preserving (RMP). The predictor variable used in this final project is the product, price, promotion and distribution. After analysis biplot, can be known that a two-dimensional graph biplot was able to explain 97,7% of actual data. The nearest competitor for Indosat provider is XL Axiata provider. Indosat provider winning in terms of promotions and distribution, Telkomsel provider winning in terms of products and Hutchison provider winning in terms of price. Keywords: telephone provider, Row Metric Preserving biplot, marketing mix

ANALISIS ANTRIAN PASIEN RAWAT INAP BERDASARKAN SPESIALISASI PENYAKIT DI RSUP Dr KARIADI SEMARANG Rahayu, Anisa Alfiani; Sugito, Sugito; Sudarno, Sudarno
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The arrival rate of inpatients at the Dr Kariadi Hospital very much in every day, either derived from poly outpatient and the ER (emergency room). With limited bed capacity, the hospital often refer patients to the hospital inpatient others who still have bed capacity. But many patients who do not want to refer to others hospitals and they will waiting for a inpatient ward. Therefore, it is necessary to determine the queuing system model according to the conditions and characteristics of the queue service facilities in Dr Kariadi hospital based specialization disease patients. Based on the analysis of data obtained for each specialization disease models queuing system that occurs in hospital based specialties Dr Kariadi hospital disease is (M / M / c): (GD / ∞ / ∞) and the model of the queue at the payment system is (M / M / 4): (GD / ∞ / ∞). Number of inpatient services by specialist have been effective because of the amount of each disease have many specialists. As for the payment / checkout number of officers who perform duties detailed breakdown of costs need to be added so that patients who come do not wait too long to get service.

ANALISIS DAMPAK SHOCK VOLUME PERDAGANGAN SAHAM PADA INDEKS HARGA SAHAM CONSUMER GOODS DENGAN STRUCTURAL VECTOR AUTOREGRESSIVE (SVAR) Infan Nur Kharismawan; Rukun Santoso; Budi Warsito
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The stock trading in the capital market will result daily volume of trading stock that impact on stock price. One of the indicators that describes the stock price movement is stock index. There are many types of stock index, one of them is consumer goods stock index. Stock index is a sensitive economic variable affected by shock and need a restriction to form its economic model. Based on that, Structural Vector Autoregressive (SVAR) is used to describe its economic model. SVAR is formed by a stable VAR, fulfilled white noise, k-variate normal distribution. The purpose of this study are to forecast data on each variables and analyze the impact of the shock through the descriptions of variance decomposition. VAR used as the basis for SVAR is VAR(8) whose the forming variable stationary at the first different degree. Performances of forecasting SVAR using MAPE (Mean Absolute Percentage Error) for in sample data are 13.87434% (volume of trading stock) and 0.87045% (consumer goods stock index) and for out sample data are 14.22964% (volume of trading stock) and 1.76054% (consumer goods stock index). Response of consumer goods stock index to the impact of the volume of trading stock shock shown by proportion of variance decomposition tends to increase, while the shock by itself has decreased until reach its equilibrium point. Keywords:cosumer goods stock index, SVAR, variance decomposition, volume of trading stock

PEMBENTUKAN KURVA IMBAL HASIL (YIELD) DENGAN MODEL NELSON SIEGEL-SVENSSON (NSS) (Studi Kasus Data Obligasi Pemerintah Periode 27 Oktober 2014 Sampai 31 Oktober 2014) Hutahayan, Eugenia Septri; Widiharih, Tatik; Wilandari, Yuciana
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Medium-term debt to long-term contains a promise from the issuer to pay interest in return for a certain period and repayment of the principal debt at a specified time to the purchaser bonds are called Bonds. A method to determine the relationship between the yield (yield) were obtained with the time to maturity for a particular type of bond at a given time is described by the yield curve (yield curve). One method to describe the yield curve is the Nelson Siegel Svensson. Observed data from the Bursa Efek Indonesia (BEI) that the data of Surat Utang Negara (SUN) with code FR (Fixed Rate). In this case the entire SUN FR with a yield is not empty in the period October 27, 2014 to October 31, 2014. Construction of the yield curve on October 27, 2014, October 28, 2014 and October 30, 2014 to form the normal curve (Positive Yield Curve) while the date October 29, 2014 and October 31, 2014 to form the combined curve between the normal curve (Positive Yield Curve) and negative curves (Inverted Yield Curve).Keywords : bond, the yield curve, Government Securities, Nelson Siegel Svensson.

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