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Jurnal Gaussian

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Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

Core Subject : Education,

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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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PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang) Helmi Panjaitan; Alan Prahutama; Sudarno Sudarno
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to T=102) so the intervention model that was suspected was a step function. The best model uses the Intervention step function is ARIMA ([13]; 1; 1) (b=0; s=18; r=0) with MSE of 1124. Autoregressive Fractionally Integrated Moving Average (ARFIMA) method is a development of the ARIMA method. The advantage of the ARFIMA method is the non-integer differentiation value so that it can overcome long memory effect that can not be solve with the ARIMA method. ARFIMA model is capable of modeling high changes in the long term (long term persistence) and explain long-term and short-term correlation structures at the same time. The number of local economy class train passengers in DAOP IV Semarang contains long memory effects, so the ARFIMA method is used to obtain the best model. The best model obtained is the ARMA(0; [1,13]) model with the differential value is 0,367546, then the model can be written into ARFIMA (0; d; [1,13]) with an MSE value of 0,00964. Based on the analysis of the three methods, the best method of analyzing the number of local economy class train passengers in DAOP IV Semarang is the ARFIMA method with the model is ARFIMA (0; 0,367546; [1,13]). Keywords: Train Passengers, ARIMA, Intervention, ARFIMA, Forecasting

PENGELO MPOKAN KUALITAS UDARA AMBIEN MENURUT KABUPATEN/KOTA DI JAWA TENGAH MENGGUNAKAN ANALISIS KLASTER Rizki Taher Dwi Kurniawati; Rita Rahmawati; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Ambient air is free of air inhaled daily by living creatures. Ambient air quality can be said to be decreased which is known from the results of measuring the quality of ambient air. The measurements carried out on residential areas, industrial areas, and traffic congested area and to SO2, NO2, CO, and HC. To help find solutions used cluster analysis of air pollution. Cluster analysis classifying objects based on object similarity. Similarities object seen by the small size of the Euclidean distance. The process of clustering with average linkage method performed on the data type of the region and type of pollutants. Clustering process produces two clusters for different kinds of land and 2 clusters for these types of pollutants. From the analysis on the type of region, cluster 1 is composed of 33 districts/cities with the results of measuring between 507 to 6760 can be said to have a good air quality conditions and in cluster 2 consists of two districts/cities with the results of measuring 11856.6 and 10594.8 is said to have poor air quality conditions. On the type of pollutant, Cluster 1 consists of 34 districts/cities with the measuring between 30 to 10810 which is said to have good air condition and the second cluster consists of one district/cities that have poor air conditions with a value of 20095 HC pollutants Keywords: ambient air, euclidean, average linkage, cluster analysis.

ANALISIS DISKRIMINAN FISHER POPULASI GANDA UNTUK KLASIFIKASI NASABAH KREDIT Ungu Siwi Maharunti; Moch. Abdul Mukid; Agus Rusgiyono
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is the biggest asset carried out by a bank and become the most dominant contributor to the bank income. However, the activity to distribute the credit takes a risk which can influence health and continuance of bank business. The credit risk which potentially occurs can be measured and controlled by analyzing directly whichever the credit client categorized to. The credit risk categorized to current credit, in specific concern credit, less current credit, doubtful credit and bad credit based on Bank Indonesia Regulation No.: 7/2/PBI/2005. The independent variables used in this research are nominal credit, principal balance, in time being bank client, time period, and bank interest. Fisher multiple discriminant analysis is a method whose assumption equality of covariance matrices. The result from using the Fisher multiple discriminant analysis in data of credit client from bank “X” in Pati shows that variable principal balance, in time being bank client, time period, and bank interest significant to measure credit risk. The classification using the Fisher multiple discriminant analysis in data of credit client from bank “X” in Pati gives the accurate 64,33%. Keywords: credit, classification, fisher multiple discriminant analysis

Pemodelan Regresi 2-Level Dengan Metode Iterative Generalized Least Square (IGLS) (Studi Kasus: Tingkat Pendidikan Anak di Kabupaten Semarang) Dyan Anggun Krismala; Dwi Ispriyanti; Moch. Abdul Mukid
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In a research, data was used often hierarchical structure. Hierarchical data is data obtained through multistage sampling from a population with independent variables can be defined within each level and dependent variable can be defined at the lowest level. One analysis that can be used for data with a hierarchical structure is a multilevel regression analysis. Multilevel regression analysis is the most simple regression analysis 2-levels. 2-level regression analysis will be used to construct a regression model the education level of children in Semarang where children (level-1) nested on the distrits (level-2) with the factors that influence. Estimation of parameter in 2-level regression model can use some methods, one of them is Iterative Generalized Least Square (IGLS). From the results of the discussion indicates that the factors which affect the level of education of children in Semarang is the mother’s education, father’ education, and percentage of farm families. The diversity level of the education of children in Semarang caused more variation among children than the variation between districts.

IMPLEMENTASI ALGORITMA MODIFIED GUSTAFSON-KESSEL UNTUK CLUSTERING TWEETS PADA AKUN TWITTER LAZADA INDONESIA Ratna Kencana Putri; Budi Warsito; Mustafid Mustafid
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Online social media is a new kind of media which is steadily growing and has become publicly popular. Due to its ability to spread informations rapidly and its easiness to access for internet users, social media provides new alternative to conduct advertising and product segmentation. Twitter is one of the most favored social media with 19.5 million users in Indonesia to the date. In this research, the application of text mining to cluster tweets from the @LazadaID Twitter account is done using the Modified Gustafson-Kessel clustering algorithm. The clustering process is executed five times with the number of cluster starts from two to six cluster. The results of this research indicate that the optimum number of clusters formed based on the Partition Coefficient and Classification Entropy validation index are three clusters. Those three clusters are tweets containing electronic stuff offers, discounts, and prize quizes. Tweets with the most retweets and likes are prize quiz tweets. PT Lazada Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @LazadaID Twitter account followers.Keywords: Twitter, advertising, Lazada Indonesia, Gustafson-Kessel Clustering algorithm, validation index

PEMODELAN KASUS KEMISKINAN DI JAWA TENGAH MENGGUNAKAN REGRESI NONPARAMETRIK METODE B-SPLINE Anisa Septi Rahmawati; Dwi Ispriyanti; Budi Warsito
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Poverty is one of the diseases in the economy, so it must be cured or at least reduced. According to BPS (2016), poor people are people who have an average expenditure per capita per month below the poverty line. The poverty line in Central Java in 2016 amounted to Rp 317 348, - per capita per month. In 2016, the average level of poverty in the Java Island, Central Java province placed as the second highest after DIY. Many factors are thought to affect the level of poverty. In this study, the predictor variables used are the rate of economic growth (X1), unemployment rate (X2), and education level above high school to (X3). This study aims to obtain a model of the relationship between the factors that affect poverty on the percentage of poor and calculate the predictions. The method used is B-spline nonparametric regression. Nonparametric approach are used if the function of previous data is unknown. The best B-spline model depends on the determination of the optimal knots point having a minimum Generalized Cross Validation (GCV). In this study, the best B-spline model obtained when the order of X1is 2, the order of X2 is 2, and the order of X3 is 2. The knots obtained in X1 at the point 4,51273, X2 at the point 3,60626, and X3 at point 11,4129 and 16,2481 with GCV value of 9,79353. Keywords: Poverty, Nonparametric Regression, B-Spline, Generalized Cross Validation

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUASAN MAHASISWA DALAM PEMILIHAN JURUSAN MENGGUNAKAN STRUCTURAL EQUATION MODELING (SEM) (Studi Kasus di Jurusan Statistika Universitas Diponegoro Semarang) Allima Stefiana Insani; Abdul Hoyyi; Rita Rahmawati
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

University is an institution that provide educational service which has a wide variety of majors. Image of the university would affect the interest of new students in decision making process, as this will affect student satisfaction through the course. Many factors influence students decision in determining their aim majors, such as service quality, curriculum, environment and academic ability. These factors are latent variables then Structural Equation Modeling (SEM) used to determine factors effect that affect student satisfaction in selection of majors. The research conducted at Diponegoro University in Statistics Department. Overall model fit test obtain Goodness Of Fit on model with the value of GFI = 0,875 and RMSEA = 0,084 are indicative of a good fit. In concluding the analysis, the factors that affect student satisfaction in decision to choose Statistics Department can be measured by academic ability, curriculum, and service quality. Students decision in choosing Statistics Department can be explained by the academic ability of students, the curriculum which is owned by Statistics Department and quality of service that is owned by the department of statistics at 96,9%. Statistics students satisfaction can be explained by academic ability of students and student decision after choosing Statistics Department of 68,8%. Key words: Decision in choosing major, students satisfaction, Structural Equation Modeling

METODE LENTH PADA RANCANGAN FAKTORIAL FRAKSIONAL 3^(k-p) DENGAN ESTIMASI EFEK ALGORITMA YATES Mutiara Ardin Rifkiani; Rita Rahmawati; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Factorial design often is used in experiments on various fields to identify the influence of main factors and interaction factors to respons were observed. A design which has k factors with three levels for each factor called factorial design. For a large number of factors, fractional factorial design is an effective alternative because it has less combination of treatment than factorial design, but it still has important needed information. In experiments conducted without repetition, determining factors that influence towards response is difficult to be analyzed if using analysis of variance. It was due to the the average of squared error absence, where error variance estimation is based on the variability of the data obtained from repeated observations. To overcome this, we use Lenth Method to identify the factors that affect the response. Lenth method uses the value of the statistic margin of error (ME) test for the main factor, and simultaneous margin of error (SME) for the interaction factor. The calculation of the statistic test ME and SME values are based on the estimated effects of each treatment. Yates algorithm is used to calculate the effect’s estimation for each treatment. To clarify the discussion about this matery is given an example of fractional factorial design application with 27 experiments on combustion boiler. The results indicate that treatment factors are influenced towards the response are , , , dan . Keywords: three-level fractional factorial, factorial without replication, Lenth Methods, Yates Algorithm

PREDIKSI DATA HARGA SAHAM HARIAN MENGGUNAKAN FEED FORWARD NEURAL NETWORKS (FFNN) DENGAN PELATIHAN ALGORITMA GENETIKA (Studi Kasus pada Harga Saham Harian PT. XL Axiata Tbk) Ira Puspita Sari; Triastuti Wuryandari; Hasbi Yasin
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Artificial neural network (ANN) or Neural Network (NN) is an information processing system that has characteristics similar to biological neural networks. One of the ANN models have network is quite simple and can be applied to time series data prediction is Feed Forward Neural Networks (FFNN). In general, FFNN trained using Backpropagation algorithm to obtain weights, but performance will decrease and trapped in a local minimum when applied to data that have great complexity like financial data. The solution to this problem is to train FFNN using Genetic Algorithm (GA). GA is a search algorithm that is based on the mechanism of natural selection and genetics to determine the global optimum. Training FFNN using GA is a good solution but the problem is how to understand the workings of FFNN training using the GA, the determination of the combination of the probability of crossover (), number of populations, number of generations, and the size of the tournament (k) on the AG to produce predictive value approaching actual value. One possible option is to use the technique of trial-end-error by experimenting for some combination of these four parameters. Of the 64 times the application of the AG test results to train FFNN models on daily stock price data PT. XL Axiata Tbk obtained results are sufficiently accurate predictions indicated by the proximity of the target to the output of the crossover probability () 0.8, a population of 50, the number of generations 20000 and tournament size of 4 produces the testing RMSE 107.4769.

STRUCTURAL VECTOR AUTOREGRESSIVE UNTUK ANALISIS DAMPAK SHOCK NILAI TUKAR RUPIAH TERHADAP DOLAR AMERIKA SERIKAT PADA INDEKS HARGA SAHAM GABUNGAN Annisa Rahmawati; Di Asih I Maruddani; Abdul Hoyyi
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Instability and depreciation of the rupiah be a motivating factor for investors to pull out a portfolio in Indonesia. The weakening of rupiah led to a decline in investor demand for stocks. Measurement of stock price fluctuations or portfolio using the Composite Stock Price Index (CSPI). The exchange rate and CSPI is a sensitive macroeconomic variables affected by shock and it takes restriction of macroeconomic structural model. Based on this, Structural Vector Autoregressive (SVAR) model is used. The purpose of this thesis is to analyze the impact of the exchange rate shock on CSPI through the description of Structural Impulse Response Function and Structural Variance Decomposition modeling based on a restriction on SVAR. SVAR also called the theoretical VAR used to respond to criticism on the VAR model where necessary the introduction of restrictions on economic models. By using daily data exchange rate of the rupiah against the US dollar and CSPI from January 2013 to December 2016 acquired the VAR model is stable and meets the white noise assumption as the basis for modeling residual SVAR and has a short-term restriction. The response of CSPI from the impact of the shock rupiah exchange rate is likely to experience an increase, while the response to the shock CSPI itself is fluctuating but tends to decrease. Patterns proportion shock effect on the exchange rate is increasingly rising stock index in the period of time, whereas the effect of the shock CSPI itself getting down on each period of time. Keywords : exchange rate, CSPI, SVAR, Structural Impulse Response Function, Structural Variance Decomposition

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